<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>6805</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>9</volume>
    <articleNumber>e202500349</articleNumber>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Weinheim</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-04-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comprehensive Analysis of Float Current Behavior and Calendar Aging Mechanisms in Lithium‐Ion Batteries</title>
    <abstract language="eng">Aiming to quantify degradation currents from solid electrolyte interphase formation (ISEIgrowth) and gain of active lithium due to cathode lithiation (ICL), resulting from electrolyte decomposition, the float current behavior of lithium-ion batteries is investigated with different cathode materials. The float current, IFloat , represents the recharge current required to maintain the cell at a fixed potential during calendar aging. This current arises as lithium is irreversibly consumed at the anode or inserted into the cathode, shifting the electrode potentials. To account for the asymmetric response of the electrodes, a voltage-dependent scaling factor, SF, is introduced, derived from the slopes of the electrode-specific voltage curves. Using this factor in combination with measured float currents and capacity loss rates from check-up tests, ISEIgrowth and ICL is quantified at 30 °C across various float voltages. Although the SF and capacity data are limited to 30 °C, the model is extended to a range of 5–50 °C using only float current measurements. The results show that using capacity loss rates alone underestimate ISEIgrowth and that ICL, contributes significantly to the observed float current at elevated voltages, indicating that cathode lithiation plays an increasingly important role in high-voltage calendar aging.</abstract>
    <parentTitle language="eng">Batteries &amp; Supercaps</parentTitle>
    <identifier type="issn">2566-6223</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-68054</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">http://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PublisherName,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,ArticleNumber,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1002/batt.202500349</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mohamed</first_name>
      <last_name>Azzam</last_name>
    </author>
    <author>
      <first_name>Dirk Uwe</first_name>
      <last_name>Sauer</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6805/Azzam_Comprehensive_Analysis.pdf</file>
  </doc>
  <doc>
    <id>6797</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2047</pageFirst>
    <pageLast>2054</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-03-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Benchmark Dataset for Bus Travel and Dwell Time Prediction</title>
    <parentTitle language="eng">2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-2418-0</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)</enrichment>
    <enrichment key="conference_place">Gold Coast, Australia</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC60802.2025.11423733</enrichment>
    <enrichment key="THI_conferenceName">IEEE 28th International Conference on Intelligent Transportation Systems (ITSC 2025), Gold Coast (Australia), 18.-21.11.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Horn</last_name>
    </author>
    <author>
      <first_name>Philip-Roman</first_name>
      <last_name>Adam</last_name>
    </author>
    <author>
      <first_name>Stefanie</first_name>
      <last_name>Schmidtner</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43141">Schmidtner, Stefanie</collection>
  </doc>
  <doc>
    <id>6792</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-03-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet Autoencoder</title>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2105.01924</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1269</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Wurst</last_name>
    </author>
    <author>
      <first_name>Lakshman</first_name>
      <last_name>Balasubramanian</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
  </doc>
  <doc>
    <id>6787</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-03-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influences of Vehicle Communication on Human Driving Reactions: A Simulator Study on Reaction Times and Behavior for Forensic Accident Analysis</title>
    <parentTitle language="deu">SSRN</parentTitle>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">posted-content/preprint</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PublisherName,TitleMain_1,CompletedYear</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://dx.doi.org/10.2139/ssrn.4588275</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4898</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Paula</last_name>
    </author>
    <author>
      <first_name>Claus</first_name>
      <last_name>Pfeilschifter</last_name>
    </author>
    <author>
      <first_name>Franziska</first_name>
      <last_name>Petermeier</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26627">Riener, Andreas</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
  </doc>
  <doc>
    <id>6784</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>25</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-03-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Vehicle-in-the-Loop Approach for Front Camera Verification Using Adaptive High Beam</title>
    <abstract language="eng">As automated driving functions based on environmental sensors become increasingly deployed, ensuring reliable performance over the vehicle lifetime is essential. Currently, verification is carried out through internal self-diagnostics, which do not always operate correctly, and periodic technical inspection, which assesses only the test criteria installation and condition. Test criteria for function and efficiency of environmental sensors are neither standardized nor routinely assessed, creating the need for new testing approaches. Previous low-cost research approaches defined a method and conducted experiments to verify a vehicle’s front camera by displaying visual stimuli and evaluating the high beam assist response. Whereas the camera’s function could be verified through a basic qualitative check, the approach did not enable a quantitative evaluation of its performance. The aim of this work was therefore to advance this approach and investigate the added value of a Vehicle-in-the-Loop test bench for front camera verification. Three tests were conducted. A supporting method was introduced to reproducibly detect and define the position of the headlight cutoff line, enabling consistent evaluation of the vehicle’s reaction. With static camera stimuli (Test I), the function of the front camera could be verified, and the influence of the vehicle geometry on the reaction was assessed. Dynamic stimuli (Test II) additionally enabled an efficiency evaluation, allowing quantitative comparison between vehicles. However, transferring the stimuli into a reproducible virtual simulation (Test III) remained challenging, as the vehicles under test did not respond consistently. Further research is required to refine and simplify the method toward a standardized periodic technical inspection procedure.</abstract>
    <parentTitle language="ces">IEEE Open Journal of Intelligent Transportation Systems</parentTitle>
    <identifier type="issn">2687-7813</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/OJITS.2026.3672438</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Robin</first_name>
      <last_name>Langer</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Tentrup</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
  </doc>
  <doc>
    <id>6777</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>162</issue>
    <volume>2026</volume>
    <articleNumber>104717</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-03-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Toxic gas emission in electric vehicles: What a battery fire means for occupant safety</title>
    <abstract language="eng">This work investigates the ability of occupants to escape from a battery electric vehicle during a thermal runaway of the traction battery initiated by nail penetration. Such events generate intense fires and large amounts of toxic gases, rapidly reducing the time available for safe evacuation. In controlled full-scale outdoor experiments on two identical mid-range battery electric vehicles, the smoke gas composition inside the cabin is examined. Using FTIR spectrometers and an oxygen analyzer, temporal and spatial concentrations of organic carbonates, hydrocarbons, hydrogen fluoride, hydrogen cyanide, acetylene, and oxygen are measured. Sampling locations include the driver's breathing zone, the right rear passenger's breathing zone, and the exterior right rear wheel housing. The study further evaluates occupant's escape capability using the fractional effective dose (FED) model in accordance with ISO 13571. Results show that toxic gases can reach harmful concentrations within minutes after smoke enters the cabin, though smoke entry times vary widely with fire progression. Differences in fire development and smoke dispersion strongly affect FED values and thus the time available for self-rescue. Overall, the findings provide an important basis for assessing occupant safety during battery electric vehicle fires and highlight the need for improved mitigation strategies.</abstract>
    <parentTitle language="eng">Fire Safety Journal</parentTitle>
    <identifier type="issn">1873-7226</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-67774</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PublisherName,TitleMain_1,Language,TitleParent_1,ArticleNumber,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.firesaf.2026.104717</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Tim</first_name>
      <last_name>Rappsilber</last_name>
    </author>
    <author>
      <first_name>Simone</first_name>
      <last_name>Krüger</last_name>
    </author>
    <author>
      <first_name>Tina</first_name>
      <last_name>Raspe</last_name>
    </author>
    <author>
      <first_name>Rudolf</first_name>
      <last_name>Reclo</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6777/1-s2.0-S0379711226000858-main.pdf</file>
  </doc>
  <doc>
    <id>6776</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-03-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">ROSA: Roundabout Optimized Speed Advisory with Multi-Agent Trajectory Prediction in&#13;
Multimodal Traffic</title>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2602.14780</enrichment>
    <enrichment key="THI_DownloadUrl">https://github.com/urbanAIthi/ROSA</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Anna-Lena</first_name>
      <last_name>Schlamp</last_name>
    </author>
    <author>
      <first_name>Jeremias</first_name>
      <last_name>Gerner</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Bogenberger</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Stefanie</first_name>
      <last_name>Schmidtner</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <collection role="persons" number="43141">Schmidtner, Stefanie</collection>
  </doc>
  <doc>
    <id>6716</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-02-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An Entropy Based Outlier Score and its Application to Novelty Detection for Road Infrastructure Images</title>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2005.13288</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1406</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Wurst</last_name>
    </author>
    <author>
      <first_name>Alberto</first_name>
      <last_name>Flores Fernandez</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
  </doc>
  <doc>
    <id>6696</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-02-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Risk Estimation for Automated Driving</title>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2601.15018</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Leon</first_name>
      <last_name>Tolksdorf</last_name>
    </author>
    <author>
      <first_name>Arturo</first_name>
      <last_name>Tejada</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Bauernfeind</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <author>
      <first_name>Nathan</first_name>
      <last_name>van de Wouw</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
  </doc>
  <doc>
    <id>6691</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>16</volume>
    <articleNumber>7387</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Quantitative Kernel estimation from traffic signs using slanted edge spatial frequency response as a sharpness metric</title>
    <abstract language="eng">Sharpness is a critical optical property of automotive cameras, measured by the spatial frequency response (SFR) within the end-of-line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of an automotive camera, which could be the first step toward state monitoring of automotive cameras. To achieve this, Principal Component Analysis (PCA) was performed, using synthetic kernels generated by Zemax. The PCA model was built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images were created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data were utilized for algorithm development, and later on, validation was performed on real-life data. The algorithm extracts two pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel was used to blur the reference ROIs, and the resulting SFR was compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel was evaluated against the true kernel for accuracy. The structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.99, and mean squared error (MSE) from to . Validation on real-life camera images showed that the SSIM between the estimated and blurred ROI was &gt;0.82, showing promising accuracy in kernel estimation, which could be used towards in-field monitoring of camera sharpness degradation.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-66915</identifier>
    <enrichment key="opus.import.date">2026-02-21T02:23:29+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">e79892ce6cd3b4d97ff204793629b970</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6241</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-026-40556-w</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_DownloadUrl">https://static-content.springer.com/esm/art%3A10.1038%2Fs41598-026-40556-w/MediaObjects/41598_2026_40556_MOESM1_ESM.pdf</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
    </author>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Nandana Kappuva</first_name>
      <last_name>Veettil</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6691/41598_2026_Article_40556.pdf</file>
  </doc>
  <doc>
    <id>6685</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>445</pageFirst>
    <pageLast>452</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Approach for Passive Safety Assessment of Rearward-Sitting Occupants</title>
    <parentTitle language="eng">2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</parentTitle>
    <identifier type="isbn">978-1-6654-7778-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</enrichment>
    <enrichment key="conference_place">Coventry, United Kingdom</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_conferenceName">19th IEEE International Conference on Vehicular Electronics and Safety (ICVES 2025), Coventry (UK), 27.-28.10.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICVES65691.2025.11376179</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6625</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Ömer</first_name>
      <last_name>Dönmez</last_name>
    </author>
    <author>
      <first_name>Ricardo</first_name>
      <last_name>Tejero de la Piedra</last_name>
    </author>
    <author>
      <first_name>Simona</first_name>
      <last_name>Klose</last_name>
    </author>
    <author>
      <first_name>Matthieu</first_name>
      <last_name>Riolet</last_name>
    </author>
    <author>
      <first_name>Lukas</first_name>
      <last_name>Rozek</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hach</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
  </doc>
  <doc>
    <id>6679</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>183</pageFirst>
    <pageLast>188</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Accelerating the Approval of Automated Driving Vehicles through standardized XiL test environments</title>
    <parentTitle language="eng">2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</parentTitle>
    <identifier type="isbn">978-1-6654-7778-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</enrichment>
    <enrichment key="conference_place">Coventry, United Kingdom</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICVES65691.2025.11376566</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">19th IEEE International Conference on Vehicular Electronics and Safety (ICVES 2025), Coventry (UK), 27.-28.10.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Christoph Dominic</first_name>
      <last_name>Sell</last_name>
    </author>
    <author>
      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>6678</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>57</pageFirst>
    <pageLast>64</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving</title>
    <parentTitle language="eng">2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</parentTitle>
    <identifier type="isbn">978-1-6654-7778-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</enrichment>
    <enrichment key="conference_place">Coventry, United Kingdom</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICVES65691.2025.11376043</enrichment>
    <enrichment key="THI_conferenceName">19th IEEE International Conference on Vehicular Electronics and Safety (ICVES 2025), Coventry (UK), 27.-28.10.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Fabian</first_name>
      <last_name>Ulreich</last_name>
    </author>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
    </author>
    <author>
      <first_name>Pak Hung</first_name>
      <last_name>Chan</last_name>
    </author>
    <author>
      <first_name>Tuomas</first_name>
      <last_name>Herraren</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Ebert</last_name>
    </author>
    <author>
      <first_name>André</first_name>
      <last_name>Kaup</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="27210">Ebert, Martin</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>6683</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>21353</pageFirst>
    <pageLast>21383</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fault Tolerant Control With Reinforcement Learning for Evasive Maneuvers Using a Scaled Vehicle</title>
    <abstract language="eng">Autonomous vehicle controllers are responsible for handling the vehicle at all times in any situation, including emergency conditions. An emergency might arise from, e.g., adverse weather conditions, short-time detection, and system faults. In this paper, we develop a fault-tolerant controller to handle actuator faults for an over-actuated autonomous vehicle based on reinforcement learning. A worst-case scenario is selected for the controller development, involving short-time detection of the preceding objects, high velocity, and dry to wet road conditions. The design of the vehicle controller is performed in three steps. First, a robust controller based on sliding mode control with lateral and longitudinal coupled strategy was built to ensure stability in emergency scenarios. Secondly, a strategy was proposed to identify the most critical vehicle faults that might lead to a crash. Building on these foundations, this study extends the vehicle controller to handle vehicle faults with a reinforcement learning strategy, enabling adaptive and robust fault handling in complex fault scenarios. The vehicle controller is designed and optimized in IPG-Carmaker®, and proof of concept is carried out in a scaled 1:3.33 test vehicle. The results demonstrate the robustness of the proposed controller in an emergency single-lane change with a velocity of up to 130 km/h. Tests in the scaled vehicle demonstrate the vehicle controller’s accuracy against simulation, with the application of reinforcement learning strategy in real-case scenarios.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-66839</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorIdentifierOrcid_5,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ACCESS.2026.3661179</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Amauri</first_name>
      <last_name>Da Silva Junior</last_name>
    </author>
    <author>
      <first_name>Steffen</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <author>
      <first_name>Reza</first_name>
      <last_name>Jazar</last_name>
    </author>
    <author>
      <first_name>Hormoz</first_name>
      <last_name>Marzbani</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6683/Fault_Tolerant_Control.pdf</file>
  </doc>
  <doc>
    <id>6680</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue>2</issue>
    <volume>12</volume>
    <articleNumber>025005</articleNumber>
    <type>article</type>
    <publisherName>IOP Publishing</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Dry electrode impedance: a new approach towards improved characterization</title>
    <abstract language="eng">Electrode-skin impedance plays a crucial role in electrophysiological signal acquisition, influencing signal quality and measurement reliability. We designed a reproducibility measurement setup, using a membrane with a saline solution and a three-electrode Electrochemical Impedance Spectroscopy measurement setup (range 1 Hz–20 kHz), to mimic the electrode-skin impedance. The system allowed controlled application of pressure to the working electrode (WE) and measurement of all setup parameters. With this setup, reproducible results were achieved, with a standard deviation of 5.5% of the mean impedance across three builds. Potentiostatic and impedance analyzer measurements with six types of dry electrodes produced comparable results, with an average error of 10%. The six dry electrode types exhibited impedance variations of up to a factor of 10,000 at low frequencies, depending on material and geometry. Ag/AgCl-coated electrodes exhibited an impedance reduction by a factor of 100 at 1 Hz compared to their uncoated counterparts. The proposed setup provides a standardized and reproducible approach for characterizing electrode impedance across different materials, coatings, and geometries.</abstract>
    <parentTitle language="eng">Biomedical Physics &amp; Engineering Express</parentTitle>
    <identifier type="issn">2057-1976</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-66800</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PublisherName,TitleMain_1,TitleAbstract_1,TitleParent_1,ArticleNumber,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1088/2057-1976/ae3e9c</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Laurens</first_name>
      <last_name>Kreilinger</last_name>
    </author>
    <author>
      <first_name>Stefan</first_name>
      <last_name>Zott</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Hemmert</last_name>
    </author>
    <author>
      <first_name>Sonja</first_name>
      <last_name>Karg</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="43326">Karg, Sonja</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6680/Kreilinger_2026_dry_electrode.pdf</file>
  </doc>
  <doc>
    <id>6671</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>401</pageFirst>
    <pageLast>406</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Author-Oriented Semantic Plagiarism Detection Using Transformer Architectures</title>
    <parentTitle language="eng">2025 IEEE International Conference on Advanced Computing Technologies (ICACT)</parentTitle>
    <identifier type="isbn">979-8-3315-9002-4</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE International Conference on Advanced Computing Technologies (ICACT)</enrichment>
    <enrichment key="conference_place">Tirupati, India</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICACT67549.2025.11351398</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">IEEE International Conference on Advanced Computing Technologies (ICACT 2025), Tirupati (India), 01.-02.09.2025</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Atharva Mangeshkumar</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Arvind Kumar</first_name>
      <last_name>Chaudhary</last_name>
    </author>
    <author>
      <first_name>Viswa</first_name>
      <last_name>Gandamalla</last_name>
    </author>
    <author>
      <first_name>Aravind Reddy</first_name>
      <last_name>Sheru</last_name>
    </author>
    <author>
      <first_name>Ashmita</first_name>
      <last_name>Chakraborty</last_name>
    </author>
    <author>
      <first_name>Bhavesh Arjan</first_name>
      <last_name>Dhirwani</last_name>
    </author>
    <author>
      <first_name>Priyank</first_name>
      <last_name>Agarwal</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
  </doc>
  <doc>
    <id>6670</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>407</pageFirst>
    <pageLast>411</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sarcasm-Aware Neural Translation for Informal Social Media Texts in Low-Resource Languages</title>
    <parentTitle language="eng">2025 IEEE International Conference on Advanced Computing Technologies (ICACT)</parentTitle>
    <identifier type="isbn">979-8-3315-9002-4</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE International Conference on Advanced Computing Technologies (ICACT)</enrichment>
    <enrichment key="conference_place">Tirupati, India</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICACT67549.2025.11351510</enrichment>
    <enrichment key="THI_conferenceName">IEEE International Conference on Advanced Computing Technologies (ICACT 2025), Tirupati (India), 01.-02.09.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <author>
      <first_name>Sai Bhuvana</first_name>
      <last_name>Kurada</last_name>
    </author>
    <author>
      <first_name>Atharva Mangeshkumar</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Viswa</first_name>
      <last_name>Gandamalla</last_name>
    </author>
    <author>
      <first_name>Krishna</first_name>
      <last_name>Goel</last_name>
    </author>
    <author>
      <first_name>Bhavesh Arjan</first_name>
      <last_name>Dhirwani</last_name>
    </author>
    <author>
      <first_name>Prakhyati</first_name>
      <last_name>Bansal</last_name>
    </author>
    <author>
      <first_name>Gopi Chandu</first_name>
      <last_name>Injarapu</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
  </doc>
  <doc>
    <id>6663</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue>3</issue>
    <volume>19</volume>
    <articleNumber>825</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-02-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analysis of the Measurement Uncertainties in the Characterization Tests of Lithium-Ion Cells</title>
    <abstract language="eng">The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research.</abstract>
    <parentTitle language="eng">Energies</parentTitle>
    <identifier type="issn">1996-1073</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-66630</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/en19030825</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Hußenether</last_name>
    </author>
    <author>
      <first_name>Carlos Antônio</first_name>
      <last_name>Rufino Júnior</last_name>
    </author>
    <author>
      <first_name>Tomás</first_name>
      <last_name>Selaibe Pires</last_name>
    </author>
    <author>
      <first_name>Tarani</first_name>
      <last_name>Mishra</last_name>
    </author>
    <author>
      <first_name>Jinesh</first_name>
      <last_name>Nahar</last_name>
    </author>
    <author>
      <first_name>Akash</first_name>
      <last_name>Vaghani</last_name>
    </author>
    <author>
      <first_name>Richard</first_name>
      <last_name>Polzer</last_name>
    </author>
    <author>
      <first_name>Sergej</first_name>
      <last_name>Diel</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19310">Fakultät Wirtschaftsingenieurwesen</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="persons" number="26446">Diel, Sergej</collection>
    <collection role="persons" number="59916">Polzer, Richard</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6663/energies-19-00825.pdf</file>
  </doc>
  <doc>
    <id>6654</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-02-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles</title>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2004.08206</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1405</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Friedrich</first_name>
      <last_name>Kruber</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
    </author>
    <author>
      <first_name>Samarjit</first_name>
      <last_name>Chakraborty</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
  </doc>
  <doc>
    <id>6625</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>Zenodo</publisherName>
    <publisherPlace>Genf</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2026-02-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Approach for Passive Safety Assessment of Rearward-Sitting Occupants</title>
    <abstract language="eng">The introduction of highly automated vehicles (HAVs) will allow vehicle occupants to take advantage of new seating configurations, such as sitting rearward in the first row. One critical aspect of assessing occupant safety during high-speed impacts is the lack of a dedicated safety framework for rearward-facing passengers in the first row. This paper introduces a method to develop new assessment criteria for these novel seat configurations. Thus, this research presents some preliminary results of rearward-facing occupant injury biomechanics analyses carried out employing a variety of anthropomorphic test devices (ATDs) and the VIVA+ 50M human body model (HBM), restrained with different belt configurations and considering different seat typologies. It reviews the suitability of 50th percentile male ATDs to capture a biofidelic engagement with the seat structure and belt system and evaluates the reaction loads on the occupant, along with the energy management resulting from seat back rotational stiffness and energy-absorbing foams layered behind the seat cushion. Based on the results, the THOR-AV-50M is a suitable candidate for further biofidelity analysis. Torso occupant loads can be effectively reduced utilizing seat back rotation but pelvis load management requires further studies.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-66254</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.5281/zenodo.18338626</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6685</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Ömer</first_name>
      <last_name>Dönmez</last_name>
    </author>
    <author>
      <first_name>Ricardo</first_name>
      <last_name>Tejero de la Piedra</last_name>
    </author>
    <author>
      <first_name>Simona</first_name>
      <last_name>Klose</last_name>
    </author>
    <author>
      <first_name>Matthieu</first_name>
      <last_name>Riolet</last_name>
    </author>
    <author>
      <first_name>Lukas</first_name>
      <last_name>Rozek</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hach</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6625/Doenmez_et_al.pdf</file>
  </doc>
  <doc>
    <id>6611</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-01-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Active Disturbance Rejection Control for a Self-Driving Racecar</title>
    <parentTitle language="eng">Proceedings of the 2025 IEEE 7th Colombian Conference on Automatic Control (CCAC)</parentTitle>
    <identifier type="isbn">979-8-3315-9955-3</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE 7th Colombian Conference on Automatic Control (CCAC)</enrichment>
    <enrichment key="conference_place">Pereira, Colombia</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/CCAC64704.2025.11259182</enrichment>
    <enrichment key="THI_conferenceName">7th IEEE Colombian Conference on Automatic Control (CCAC 2025), Pereira (Colombia), 14.-17.10.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Andres</first_name>
      <last_name>Hoyos</last_name>
    </author>
    <editor>
      <first_name>Diana Marcela</first_name>
      <last_name>Ovalle Martinez</last_name>
    </editor>
    <author>
      <first_name>Ximena</first_name>
      <last_name>Quevedo</last_name>
    </author>
    <editor>
      <first_name>German Dario</first_name>
      <last_name>Obando Bravo</last_name>
    </editor>
    <author>
      <first_name>Camilo</first_name>
      <last_name>Cortes</last_name>
    </author>
    <author>
      <first_name>Santiago</first_name>
      <last_name>Triana</last_name>
    </author>
    <author>
      <first_name>Sohel</first_name>
      <last_name>Anwar</last_name>
    </author>
    <author>
      <first_name>Andres</first_name>
      <last_name>Tovar</last_name>
    </author>
    <author>
      <first_name>Alvaro</first_name>
      <last_name>Gutiérrez</last_name>
    </author>
    <author>
      <first_name>Jaime</first_name>
      <last_name>Arcos-Legarda</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
  </doc>
  <doc>
    <id>6605</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>6417</pageFirst>
    <pageLast>6437</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>14</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-01-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Message Generation Rules for V2X Maneuver Coordination Service</title>
    <abstract language="eng">Maneuver coordination enables connected and automated vehicles (CAVs) to collaboratively plan, negotiate, and execute driving maneuvers, aiming to enhance safety, traffic flow, and energy efficiency. As part of the Vehicle-to-Everything (V2X) communication system, it operates as a dedicated service that relies on detailed, bidirectional interactions between vehicles, unlike traditional broadcast-based V2X services. While maneuver coordination is the focus of ongoing research and standardization, the rules that govern when and how vehicles generate Maneuver Coordination Messages (MCMs) within the Maneuver Coordination Service (MCS) are still not fully defined. These rules are essential for ensuring timely and reliable coordination. This paper proposes three adaptive MCM generation strategies based on the operation mode of the MCS, maneuver priority, and current channel load. The first approach defines MCM rules for intent sharing, maneuver negotiation, and execution phases, factoring in maneuver importance. The second adjusts the frequency of intent MCMs when a negotiation message is received from another vehicle. The third adapts frequency based on real-time channel load during intent sharing. For comparison, two baseline strategies are also evaluated. The proposed rules are tested for highway merging and lane change use cases in traffic scenarios with increasing vehicle density and high channel loads. A comprehensive evaluation is conducted using metrics related to the network, message generation frequency, congestion control, and maneuver coordination. The results demonstrate significantly enhanced channel efficiency and communication reliability across all metrics. For example, under the final approach, maneuver negotiation time is reduced by a factor of five in congested V2X environments.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-66051</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ACCESS.2026.3652364</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Maksimovski</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26695">Facchi, Christian</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6605/Adaptive_Message_Generation.pdf</file>
  </doc>
  <doc>
    <id>6596</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-01-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Closed-Form Modeling of MOSFET Switching Losses Including Variable Gate-Drain Capacitance and Zero-Voltage Switching</title>
    <parentTitle language="eng">Proceedings of the 2025 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific)</parentTitle>
    <identifier type="isbn">979-8-3315-5984-7</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITECAsia-Pacific63742.2025.11345027</enrichment>
    <enrichment key="THI_conferenceName">2025 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), Singapore (Singapore), 25.-28.11.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hanzl</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>6579</id>
    <completedYear/>
    <publishedYear>2026</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>12</pageFirst>
    <pageLast>34</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>74</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>De Gruyter</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-01-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Fahrzeugsicherheit autonomer Fahrzeuge: Grenzen heutiger Systeme und potenzielle Lösungen</title>
    <parentTitle language="deu">at - Automatisierungstechnik</parentTitle>
    <identifier type="issn">2196-677X</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PublisherName,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,PageNumber,PageFirst,PageLast,Issue,Volume,CompletedYear,IdentifierIssn</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1515/auto-2025-0064</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Rainer</first_name>
      <last_name>Engert</last_name>
    </author>
    <author>
      <first_name>Patrick</first_name>
      <last_name>Zaumseil</last_name>
    </author>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Zdetski</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26848">Brandmeier, Thomas</collection>
  </doc>
  <doc>
    <id>6563</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>60</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>17</volume>
    <articleNumber>3</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2026-01-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Approaches for Lifetime Prediction of Vehicle Traction Battery Systems During a Technical Inspection: A Systematic Review</title>
    <abstract language="eng">Creating trust in society for new technologies, such as a new types of powertrains, and making them marketable requires transparent, neutral, and independent technical verification. This is crucial for the acceptance and success of electrified vehicles in the used car markets. A key component of electric vehicles is the traction battery, whose current and future condition, particularly regarding aging, determines its residual value and safe operation. This review aims to identify and evaluate methods for predicting the lifetime of onboard traction batteries, focusing on their applicability in technical inspections. A systematic literature and patent review was conducted using targeted keywords, yielding 22 patents and 633 publications. From these, 150 distinct lifetime prediction methods were extracted and categorized into a four-level mind map. These methods are summarized, cited, and structured in detailed tables. The relationships between approaches are explained to clarify the current research landscape. Long Short-Term Memory, Convolutional Neural Networks, and Particle Filters were identified as the most frequently used techniques. However, no methods were found suitable for predicting the lifetime of traction batteries during technical vehicle inspections, which operate under short test durations, limited data access, and diverse real-world operating conditions. Most studies focused on cell-level testing and did not address complete battery systems in operational vehicles. This gap highlights the need for applied research and the development of practical methods to support battery assessment in real-world conditions. Advancing this field is essential to foster confidence in battery systems and enable a sustainable transition to electromobility.</abstract>
    <parentTitle language="eng">World Electric Vehicle Journal</parentTitle>
    <identifier type="issn">2032-6653</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-65631</identifier>
    <enrichment key="opus.import.date">2026-01-08T15:25:30+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">12e4b97853b954f56b3f807aa9885cde</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/wevj17010003</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Markus</first_name>
      <last_name>Gregor</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Aline</first_name>
      <last_name>Kirsten Vidal de Oliveira</last_name>
    </author>
    <author>
      <first_name>Pascal</first_name>
      <last_name>Mast</last_name>
    </author>
    <author>
      <first_name>Ricardo</first_name>
      <last_name>Rüther</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6563/wevj-17-00003.pdf</file>
  </doc>
  <doc>
    <id>6558</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>4</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Technion</publisherName>
    <publisherPlace>Haifa</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Investigating Floating Car Observers for Traffic State Estimation through Co-Simulation</title>
    <parentTitle language="eng">MFTS: The 5th Symposium on Management of Future Motorway and Urban Traffic System 2024</parentTitle>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">5th Symposium on Management of Future Motorway and Urban Traffic System Crete (MFTS 2024), Heraklion (Greece), 04.-06.09.2024</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Jeremias</first_name>
      <last_name>Gerner</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Bogenberger</last_name>
    </author>
    <author>
      <first_name>Stefanie</first_name>
      <last_name>Schmidtner</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43141">Schmidtner, Stefanie</collection>
  </doc>
  <doc>
    <id>6530</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>61</pageFirst>
    <pageLast>73</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>7</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">YoFlow Method for Scenario Based Automatic Accident Detection</title>
    <abstract language="eng">Recent advances in sensor and computing technologies have enabled road side units (RSUs) to not only monitor traffic flow but also process data in real time to improve road safety. However, leveraging RSUs for proactive accident detection remains a challenging and underexplored task, partly due to the lack of diverse accident data. To address this, this study proposes two key contributions: (i) a scenario-based synthetic data generation framework, and (ii) YoFlow, a novel system for vehicle-tovehicle accident detection from a simulated RSU camera perspective. The proposed framework leverages the PEGASUS method for scenario generation strategy and BeamNG.tech for generating synthetic traffic videos. This approach led to the development of the SB-SIF dataset, which includes five representative intersection crash scenarios derived from German accident data. The SB-SIF dataset contains 914 crash videos, 123 near-miss events, and 924 normal traffic instances and is publicly available at: https://doi.org/10.5281/zenodo.15267252. The proposed YoFlow system identifies accidents by analyzing temporal variations in vehicle speed vectors, using YOLO for vehicle classification and CUDA-accelerated dense optical flow to capture abrupt motion changes. The extracted features are processed and classified using an XGBoost model, achieving 94% recall and 90% precision in accident detection.</abstract>
    <parentTitle language="eng">IEEE Open Journal of Intelligent Transportation Systems</parentTitle>
    <identifier type="issn">2687-7813</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/OJITS.2025.3639557</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6055</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_DownloadUrl">https://doi.org/10.5281/zenodo.15267252</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Aditya</first_name>
      <last_name>Haryanto</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>accident detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>traffic accident</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>surveillance camera</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>optical flow</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>traffic scenarios</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6530/YoFlow_Method.pdf</file>
  </doc>
  <doc>
    <id>6498</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>10</issue>
    <volume>16</volume>
    <articleNumber>1164</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sintering for High Power Optoelectronic Devices</title>
    <abstract language="eng">Residual-free eutectic Au80Sn20 soldering is still the dominant assembly technology for optoelectronic devices such as high-power lasers, LEDs, and photodiodes. Due to the high cost of gold, alternatives are desirable. This paper investigates the thermal performance of copper-based sintering for optoelectronic submodules on first and second level to obtain thermally efficient thin bondlines. Sintered interconnects obtained by a new particle-free copper ink, based on complexed copper salt, are compared with copper flake and silver nanoparticle sintered interconnects and benchmarked against AuSn solder interconnects. The copper ink is dispensed and predried at 130 °C to facilitate in situ generation of Cu nanoparticles by thermal decomposition of the metal salt before sintering. Submounts are then sintered at 275 °C for 15 min under nitrogen with 30 MPa pressure, forming uniform 2–5 µm copper layers achieving shear strengths above 31 MPa. Unpackaged LEDs are bonded on first level using the copper ink but applying only 10 MPa to avoid damaging the semiconductor dies. Thermal performance is evaluated via transient thermal analysis. Results show that copper ink interfaces approach the performance of thin AuSn joints and match silver interconnects at second level. However, at first level, AuSn and sintered interconnects of commercial silver and copper pastes remained superior due to the relative inhomogeneous thickness of the thin Cu copper layer after predrying, requiring higher bonding pressure to equalize surface inhomogeneities.</abstract>
    <parentTitle language="eng">Micromachines</parentTitle>
    <identifier type="issn">2072-666X</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64984</identifier>
    <enrichment key="opus.import.date">2025-12-10T11:46:59+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">a09285a3a2193403b49238ec92b7c2f5</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/mi16101164</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Hannes</first_name>
      <last_name>Schwan</last_name>
    </author>
    <author>
      <first_name>Nihesh</first_name>
      <last_name>Mohan</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Rocky Kumar</first_name>
      <last_name>Saha</last_name>
    </author>
    <author>
      <first_name>Holger</first_name>
      <last_name>Klassen</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <collection role="persons" number="26880">Schmid, Maximilian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6498/micromachines-16-01164.pdf</file>
  </doc>
  <doc>
    <id>6487</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison of different Input data for the prediction of LED solder joints using Artificial Neural Networks</title>
    <abstract language="eng">Scarcity of raw data is a major issue for applying data driven methods to reliability prediction, so making the best use of what is available is critical. This paper studies how different aspects of measurement data can be used best. Specifically, the reliability of the solder joint of LED packages is predicted based on Transient Thermal Analysis and Scanning Acoustic Microscopy data from a large measurement campaign. The impact of using full temporal information vs measurements at individual datapoints is investigated as well as the benefit of including the SAM data, and different ways of presenting the TTA information, either as a full curve or as expert-selected features. The impact of formatting categorical information of solder and LED package type as one-hot encoding or using embeddings is considered. Finally, the performance for Pass/Fail predictions of the best identified model with a model architecture developed on a similar dataset is compared. We identified the most relevant sources of information for predicting the behavior and the best format for the data, which helps guide the choice for future model architectures.</abstract>
    <parentTitle language="eng">IEEE Transactions on Device and Materials Reliability</parentTitle>
    <identifier type="issn">1558-2574</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/TDMR.2025.3633876</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Zippelius</last_name>
    </author>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="persons" number="26880">Schmid, Maximilian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
  </doc>
  <doc>
    <id>6483</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>29</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-12-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">UrbanIng-V2X: A Large-Scale Multi-Vehicle, Multi-Infrastructure Dataset Across Multiple&#13;
Intersections for Cooperative Perception</title>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2510.23478</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_DownloadUrl">https://github.com/thi-ad/UrbanIng-V2X</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Karthikeyan</first_name>
      <last_name>Chandra Sekaran</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Geisler</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Rößle</last_name>
    </author>
    <author>
      <first_name>Adithya</first_name>
      <last_name>Mohan</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Cremers</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
    </author>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <collection role="persons" number="41270">Schön, Torsten</collection>
  </doc>
  <doc>
    <id>6482</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue>196</issue>
    <volume>2026</volume>
    <articleNumber>109396</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluating optical performance degradation of automotive cameras under accelerated aging</title>
    <abstract language="eng">Automotive cameras are subject to environmental stress, which degrades performance by reducing image sharpness. To qualify for automotive use and to ensure that the cameras maintain sharpness according to the hard requirements of end-of-line testing, cameras have to undergo standardized accelerated aging tests. These tests are performed to demonstrate reliability and functional safety over lifetime. Few studies have been published that demonstrate how aging contributes to the degradation of optical performance. This study addresses this gap by combining accelerated thermal aging with sharpness tracking to investigate degradation over time. To quantify sharpness degradation, six series-production cameras were subjected to accelerated thermal aging between −40◦𝐶 and +85◦𝐶. Each camera underwent 2000 aging cycles, equivalent to 80% of their lifetime based on the Coffin-Manson model of the LV124 standard. Sharpness was measured by calculating the Spatial Frequency Response (SFR) from images captured of a double-cross reticle projected by a virtual object generator with three illumination wavelengths (625nm, 520nm, and 470nm). The change in sharpness was evaluated with SFR50 and SFR at 60 line pairs per millimeter (SFR@60). During the first 250 cycles, a wear-in effect was observed, where sharpness increased before leveling off, as seen previously. The results also indicated a slow decline in sharpness showing long-term stability. Analysis indicated that before aging, the best focal plane was located closer to the focal position of the red wavelength, which lies furthest from the objective. By the end of the aging process, the best focal plane had shifted toward the focal position of the blue wavelength, which is located closer to the objective. This suggests a forward movement of the image sensor due to aging. Even after 2000 cycles, all cameras maintained an SFR@60 above 0.5. A Random Forest regression model was trained to predict the age based on the SFR curves, achieving a mean absolute error of 126 cycles and a 𝑅2 score of 0.96.</abstract>
    <parentTitle language="eng">Optics and Lasers in Engineering</parentTitle>
    <identifier type="issn">1873-0302</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64821</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PublisherName,TitleMain_1,Language,TitleParent_1,ArticleNumber,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.optlaseng.2025.109396</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
    </author>
    <author>
      <first_name>Stephan</first_name>
      <last_name>Kühn</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Weis</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6482/1-s2.0-S0143816625005810-main.pdf</file>
  </doc>
  <doc>
    <id>6470</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>81</pageFirst>
    <pageLast>118</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>27</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-12-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Systematic Literature Review on Vehicular Collaborative Perception—A Computer Vision Perspective</title>
    <abstract language="eng">The effectiveness of autonomous vehicles relies on reliable perception capabilities. Despite significant advancements in artificial intelligence and sensor fusion technologies, current single-vehicle perception systems continue to encounter limitations, notably visual occlusions and limited long-range detection capabilities. Collaborative Perception (CP), enabled by Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, has emerged as a promising solution to mitigate these issues and enhance the reliability of autonomous systems. Beyond advancements in communication, the computer vision community is increasingly focusing on improving vehicular perception through collaborative approaches. However, a systematic literature review that thoroughly examines existing work and reduces subjective bias is still lacking. Such a systematic approach helps identify research gaps, recognize common trends across studies, and inform future research directions. In response, this study follows the PRISMA 2020 guidelines and includes 106 peer-reviewed articles. These publications are analyzed based on modalities, collaboration schemes, and key perception tasks. Through a comparative analysis, this review illustrates how different methods address practical issues such as pose errors, temporal latency, communication constraints, domain shifts, heterogeneity, and adversarial attacks. Furthermore, it critically examines evaluation methodologies, highlighting a misalignment between current metrics and CP’s fundamental objectives. By delving into all relevant topics in-depth, this review offers valuable insights into challenges, opportunities, and risks, serving as a reference for advancing research in vehicular collaborative perception.</abstract>
    <parentTitle language="eng">IEEE Transactions on Intelligent Transportation Systems</parentTitle>
    <identifier type="issn">1558-0016</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64708</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorIdentifierOrcid_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,PersonAuthorIdentifierOrcid_8,PersonAuthorFirstName_9,PersonAuthorLastName_9,PersonAuthorFirstName_10,PersonAuthorLastName_10,PersonAuthorIdentifierOrcid_10,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5963</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/TITS.2025.3631141</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Lei</first_name>
      <last_name>Wan</last_name>
    </author>
    <author>
      <first_name>Jianxin</first_name>
      <last_name>Zhao</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Wiedholz</last_name>
    </author>
    <author>
      <first_name>Manuel</first_name>
      <last_name>Bied</last_name>
    </author>
    <author>
      <first_name>Mateus</first_name>
      <last_name>Martinez de Lucena</last_name>
    </author>
    <author>
      <first_name>Abhishek Dinkar</first_name>
      <last_name>Jagtap</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Antônio</first_name>
      <last_name>Fröhlich</last_name>
    </author>
    <author>
      <first_name>Hannan Ejaz</first_name>
      <last_name>Keen</last_name>
    </author>
    <author>
      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6470/A_Systematic_Literature_Review.pdf</file>
  </doc>
  <doc>
    <id>6456</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Reliability of High-Power LEDs Under Varying Thermal Aging Conditions</title>
    <parentTitle language="eng">2025 31st International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)</parentTitle>
    <identifier type="isbn">979-8-3315-9486-2</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 31st International Workshop on Thermal Investigations of ICs and Systems (THERMINIC)</enrichment>
    <enrichment key="conference_place">Naples, Italy</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/THERMINIC65879.2025.11216869</enrichment>
    <enrichment key="THI_conferenceName">31st International Workshop on Thermal Investigations of ICs and Systems (THERMINIC 2025), Naples (Italy), 24.-26.09.2025</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Zippelius</last_name>
    </author>
    <author>
      <first_name>Elias</first_name>
      <last_name>Hufnagel</last_name>
    </author>
    <author>
      <first_name>Jainam</first_name>
      <last_name>Shah</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="persons" number="26880">Schmid, Maximilian</collection>
  </doc>
  <doc>
    <id>6453</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>599</pageFirst>
    <pageLast>604</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Improving B-Spline-Based 3D Extended Object Tracking Using Doppler Measurements</title>
    <parentTitle language="eng">Proceedings of the 2025 IEEE Radar Conference (RadarConf25), October 4–9, 2025,&#13;
Kraków, Poland</parentTitle>
    <identifier type="isbn">979-8-3315-4433-1</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE Radar Conference (RadarConf25)</enrichment>
    <enrichment key="conference_place">Krakow, Poland</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/RadarConf2559087.2025.11205008</enrichment>
    <enrichment key="THI_conferenceName">025 IEEE Radar Conference (RadarConf’25), Krakow (Poland), 04.-10.10.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
    </author>
    <editor>
      <first_name>Marek</first_name>
      <last_name>Rupniewski</last_name>
    </editor>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
    </author>
    <editor>
      <first_name>Shannon</first_name>
      <last_name>Blunt</last_name>
    </editor>
    <author>
      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
    </author>
    <editor>
      <first_name>Jacek</first_name>
      <last_name>Misiurewicz</last_name>
    </editor>
    <editor>
      <first_name>Maria Sabrina</first_name>
      <last_name>Greco</last_name>
    </editor>
    <editor>
      <first_name>Braham</first_name>
      <last_name>Himed</last_name>
    </editor>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
  </doc>
  <doc>
    <id>6451</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>000339</pageFirst>
    <pageLast>000344</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sensorless Detection of Unbalanced Loads in Embedded Motor Control via ML-Trained Pattern Matching on Speed Signals</title>
    <parentTitle language="eng">SISY 2025: IEEE 23rd International Symposium on Intelligent Systems and Informatics, September 25-27, 2025, Subotica, Serbia, Proceedings</parentTitle>
    <identifier type="isbn">979-8-3315-9657-6</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE 23rd Jubilee International Symposium on Intelligent Systems and Informatics (SISY)</enrichment>
    <enrichment key="conference_place">Subotica, Serbia</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/SISY67000.2025.11205383</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">23rd International Symposium on Intelligent Systems and Informatics (SISY 2025), Subotica (Serbia), 25.-27.09.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Franziska</first_name>
      <last_name>Kofler</last_name>
    </author>
    <author>
      <first_name>Mark</first_name>
      <last_name>Wendler</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Baumgartner</last_name>
    </author>
    <author>
      <first_name>József</first_name>
      <last_name>Kopják</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
  </doc>
  <doc>
    <id>6450</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1721</pageFirst>
    <pageLast>1732</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>58</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IIETA</publisherName>
    <publisherPlace>Edmonton</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Emission Testing of Flex Fuel Vehicles up to M100/E100: Upgradation of Existing Test Facility for Vehicles below 3.5-ton GVW for Testing Methanol or Ethanol Operated Vehicles</title>
    <abstract language="eng">This paper is focused on enhancing the existing dilute emission measurement system to accommodate the emission testing of vehicles operating with gasoline blends with methanol and ethanol, dedicated M100/E100 etc. India is moving towards achieving the E20 blend on the pan-country level by April 2025 with the initiations of NITI Aayog and exploring alternate fuels like 100% methanol and ethanol. Government of India is now focused on introducing the flex fuel vehicle (FFV) technology that supports the usage of higher ethanol blends. As per CMVR guidelines for M and N category vehicles with a gross vehicle weight less than 3,500 kg; a dilute emission measurement system is used to evaluate tailpipe pollutants and fuel economy. Most of the present emission systems are compatible with testing the vehicle till M15 and E20 blends. Beyond this percentage, usage of a test facility for M100, E100, and other flex-fuel vehicles, require certain modifications in existing test facility to maintain the precision and reliability of the results considering higher water content in exhaust and effect of condensation on emission measurement due to dilution. The modifications needed, implementation and their impact are experimentally briefed in the present paper.</abstract>
    <parentTitle language="eng">Journal Européen des Systèmes Automatisés​</parentTitle>
    <identifier type="issn">2116-7087</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64502</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">http://iieta.org/sites/default/files/TEXT%20AND%20DATA%20MINING%20SERVICE%20AGREEMENT.pdf</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.18280/jesa.580816</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mohammad I.</first_name>
      <last_name>Jamadar</last_name>
    </author>
    <author>
      <first_name>Ravi</first_name>
      <last_name>Sekhar</last_name>
    </author>
    <author>
      <first_name>Vijay Ramarao</first_name>
      <last_name>Yada</last_name>
    </author>
    <author>
      <first_name>Sandeep D.</first_name>
      <last_name>Rairikar</last_name>
    </author>
    <author>
      <first_name>Sukrut</first_name>
      <last_name>Thipse</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6450/jesa_58.08_16.pdf</file>
  </doc>
  <doc>
    <id>6408</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>43</pageNumber>
    <edition/>
    <issue>11</issue>
    <volume>11</volume>
    <articleNumber>11110424</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Design and Implementation of an Intelligent Reconfigurable High-Voltage Battery System for Next-Generation Electric Vehicles</title>
    <abstract language="eng">Battery system engineers face the challenge of balancing competing requirements regarding performance, maintainability, sustainability, safety, and cost—especially in the automotive industry. IBS potentially offer a solution with fewer trade-offs. They feature a battery management system with advanced sensing and data analysis capabilities that facilitate improved battery monitoring and operation. Reconfigurable energy storage units enable sophisticated operating strategies, including complete cell state control, full energy content utilization, and a measured response to faults. This article presents the design, development, and operation of a full-scale intelligent battery system prototype comprising 324 automotive lithium-ion cells with a nominal voltage of 400V. The system exhibits a modular single cell architecture and an advanced centralized battery management system. We detail the system architecture, hardware and software component design, and system integration. Initial tests demonstrate the battery’s operability, extended functionality, and enhanced safety. Our analysis shows that the additional losses introduced by reconfigurability are more than offset by the benefits of full energy utilization—even for new cells, with increasing advantage as aging progresses. The results underscore the potential of intelligent battery systems and motivate further research and development toward economic assessment and industrial adoption.</abstract>
    <parentTitle language="eng">Batteries</parentTitle>
    <identifier type="issn">2313-0105</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-64086</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/batteries11110424</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Stefan</first_name>
      <last_name>Schwertner</last_name>
    </author>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Buchberger</last_name>
    </author>
    <author>
      <first_name>Simon</first_name>
      <last_name>Diehl</last_name>
    </author>
    <author>
      <first_name>Rebekka</first_name>
      <last_name>Ferg</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Hanzl</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Hartmann</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Hölzle</last_name>
    </author>
    <author>
      <first_name>Jan</first_name>
      <last_name>Kleiner</last_name>
    </author>
    <author>
      <first_name>Lidiya</first_name>
      <last_name>Komsiyska</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Liebhart</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Scholz</last_name>
    </author>
    <author>
      <first_name>Sascha</first_name>
      <last_name>Speer</last_name>
    </author>
    <author>
      <first_name>Julia</first_name>
      <last_name>Stöttner</last_name>
    </author>
    <author>
      <first_name>Christoph</first_name>
      <last_name>Terbrack</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Hinterberger</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6408/batteries-11-00424-v3.pdf</file>
  </doc>
  <doc>
    <id>6404</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>38</pageFirst>
    <pageLast>44</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Hybrid Machine Learning Model with a Constrained Action Space for Trajectory Prediction</title>
    <parentTitle language="eng">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">979-8-3315-3803-3</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE Intelligent Vehicles Symposium (IV)</enrichment>
    <enrichment key="conference_place">Cluj-Napoca, Romania</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5778</enrichment>
    <enrichment key="THI_conferenceName">36th IEEE Intelligent Vehicles Symposium (IV 2025), Cluj-Napoca (Romania), 22.-25.06.2025</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV64158.2025.11097759</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Fertig</last_name>
    </author>
    <author>
      <first_name>Lakshman</first_name>
      <last_name>Balasubramanian</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
  </doc>
  <doc>
    <id>6396</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>32</pageFirst>
    <pageLast>49</pageLast>
    <pageNumber/>
    <edition/>
    <issue>109</issue>
    <volume>2025</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of gaze strategies and cognitive load on safeguarding performance of motorists in right-turning scenarios involving potential conflicts with vulnerable road users</title>
    <abstract language="eng">In urban traffic, while the fraction of collisions involving Vulnerable Road Users (VRU) is low, their importance is high due to the higher injury risk for VRU. Their infrequent occurrence on average (compared with far more common individual perceptual and behavioral errors by both drivers and VRUs) reflects an underlying fault tolerance in traffic processes. However, the degree of fault tolerance varies among traffic situations. The underlying perceptual and cognitive processes involved are complex and can require a high level of attention and concentration, particularly in situations with intersecting trajectories. These processes can occasionally fail, leading to collision risk. The situation of right-turning motorists (in right-hand-drive countries) encountering cyclists moving straight on a bike lane (with right of way) has a particularly low error tolerance, since motorists must actively scan for cyclists approaching from behind. In order to develop, test and assess solutions that mitigate collision risk in this situation, the behavior-related causation mechanisms need investigation. This is the focus of this article. We conducted a trial on our closed test track with n = 35 subjects. The experiment was designed as a within-subject design with three independent factors: maneuver, target velocity, and cognitive load in an n-back task. The trial included observations of participants' gaze control. A primary research focus was the quality and efficiency of the safeguarding gaze behavior of participants in order to draw conclusions on the causation mechanisms of collisions in this situation. For this purpose we define metrics in order to quantify the quality and efficiency of a specific gaze behavior. Furthermore, we studied the effect of factors cognitive load and target velocity on safety and secondary (n-back) task performance. Remarkably, only four out of 35 participants reached a collision risk of 0% relating to the defined quality metric. Furthermore, we identified four distinct gaze strategy groups through hierarchical clustering, where one group performed particularly few glances overall. This group showed significant differences with respect to the defined quality metric whereas the other groups showed only slight differences to each other. The results have implications on subsequent crash causation model development.</abstract>
    <parentTitle language="eng">Transportation Research Part F: Traffic Psychology and Behaviour</parentTitle>
    <identifier type="issn">1873-5517</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63967</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PublisherName,TitleMain_1,Language,TitleParent_1,PageNumber,PageFirst,PageLast,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.trf.2024.11.012</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Florian</first_name>
      <last_name>Denk</last_name>
    </author>
    <author>
      <first_name>Felix</first_name>
      <last_name>Fröhling</last_name>
    </author>
    <author>
      <first_name>Pascal</first_name>
      <last_name>Brunner</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Margreiter</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Bogenberger</last_name>
    </author>
    <author>
      <first_name>Ronald</first_name>
      <last_name>Kates</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6396/1-s2.0-S1369847824003140-main.pdf</file>
  </doc>
  <doc>
    <id>6392</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>10152</pageFirst>
    <pageLast>10164</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>20</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Universiti Malaysia Pahang Al-Sultan Abdullah Publishing</publisherName>
    <publisherPlace>Pekan</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Experimental Investigation on Performance Characteristics of Dry Centrifugal Clutch with Grooved Friction Liners</title>
    <abstract language="eng">The deteriorated condition of friction liners after prolonged use is one of the primary causes of judder in centrifugal clutches. The friction characteristics can be retained by generating specific textures or grooves on the friction liner. An attempt has been made to study the characteristics of centrifugal clutch using grooved friction liners. A test cycle for centrifugal clutch has been developed using a number of engagements as the basis. A vehicle test bench was used for the experiment where the developed test cycle was automated. The performance characteristics of the centrifugal clutch have been recorded and analyzed with normal friction liners and grooved friction liners for 100 test cycles. For this study, the groove area ratio was retained at 0.15, and the grooves were cut at 90°. After completing 100 test cycles, the clutch with a grooved friction liner exhibited better characteristics. After completing 100 test cycles, the surface roughness reduction at the leading section of the grooved friction liner and normal friction liner has been found to be 6.44% and 8.11%, respectively. The thickness reduction at the leading section of the grooved friction liner and normal friction liner has been reported to be 3.73% and 4.98%, respectively. Throughout the run of 100 test cycles, the higher clutch housing temperature has been witnessed in the case of a clutch with a grooved friction liner. At the 100th test cycle, the clutch torque with a grooved friction liner was 15.22% more than the clutch torque with a normal friction liner. Even after prolonged use, the clutch with grooved friction liner exhibited better judder characteristics and also provided higher fuel economy for vehicles.</abstract>
    <parentTitle language="eng">International Journal of Automotive and Mechanical Engineering</parentTitle>
    <identifier type="issn">2180-1606</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63929</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">http://creativecommons.org/licenses/by/4.0</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PublisherName,TitleMain_1,TitleAbstract_1,TitleParent_1,PageNumber,PageFirst,PageLast,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.15282/ijame.20.1.2023.01.0786</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Dhruv</first_name>
      <last_name>Panchal</last_name>
    </author>
    <author>
      <first_name>Bhavesh</first_name>
      <last_name>Patel</last_name>
    </author>
    <author>
      <first_name>Hardik</first_name>
      <last_name>Gohil</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6392/1-2.pdf</file>
  </doc>
  <doc>
    <id>6386</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>23</pageFirst>
    <pageLast>33</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">INR Meets Multi-contrast MRI Reconstruction</title>
    <parentTitle language="eng">Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis: First International Workshop, RIME 2025, and 7th International Workshop, GRAIL 2025, Daejeon, South Korea, September 27, 2025, Proceedings</parentTitle>
    <identifier type="isbn">978-3-032-06103-4</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">book-chapter</enrichment>
    <enrichment key="local_crossrefLicence">https://www.springernature.com/gp/researchers/text-and-data-mining</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,PersonAuthorFirstName_9,PersonAuthorLastName_9,PublisherName,PublisherPlace,TitleMain_1,Language,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIsbn,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_DownloadUrl">https://github.com/compai-lab/2025-miccai-niessen</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/978-3-032-06103-4_3</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">1st International Workshop on Reconstruction and Imaging Motion Estimation (RIME 2025), Daejeon (South Korea), 27.09.2025</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Natascha</first_name>
      <last_name>Niessen</last_name>
    </author>
    <editor>
      <first_name>Lina</first_name>
      <last_name>Felsner</last_name>
    </editor>
    <author>
      <first_name>Carolin</first_name>
      <last_name>Pirkl</last_name>
    </author>
    <editor>
      <first_name>Thomas</first_name>
      <last_name>Küstner</last_name>
    </editor>
    <author>
      <first_name>Ana Beatriz</first_name>
      <last_name>Solana</last_name>
    </author>
    <editor>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </editor>
    <author>
      <first_name>Hannah</first_name>
      <last_name>Eichhorn</last_name>
    </author>
    <editor>
      <first_name>Chen</first_name>
      <last_name>Qin</last_name>
    </editor>
    <author>
      <first_name>Veronika</first_name>
      <last_name>Spieker</last_name>
    </author>
    <editor>
      <first_name>Seyed-Ahmad</first_name>
      <last_name>Ahmadi</last_name>
    </editor>
    <author>
      <first_name>Wenqi</first_name>
      <last_name>Huang</last_name>
    </author>
    <editor>
      <first_name>Anees</first_name>
      <last_name>Kazi</last_name>
    </editor>
    <author>
      <first_name>Tim</first_name>
      <last_name>Sprenger</last_name>
    </author>
    <editor>
      <first_name>Xiaoling</first_name>
      <last_name>Hu</last_name>
    </editor>
    <author>
      <first_name>Marion Irene</first_name>
      <last_name>Menzel</last_name>
    </author>
    <author>
      <first_name>Julia A.</first_name>
      <last_name>Schnabel</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="44549">Menzel, Marion</collection>
  </doc>
  <doc>
    <id>6388</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cyberbullying Detection in Bengali Social Media Using TF-IDF and Supervised Machine Learning Techniques</title>
    <parentTitle language="eng">2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)</parentTitle>
    <identifier type="isbn">979-8-3315-9694-1</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN)</enrichment>
    <enrichment key="conference_place">Rangpur, Bangladesh</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/QPAIN66474.2025.11171789</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">International Conference on Quantum Photonics, Artificial Intelligence, and Networking (QPAIN 2025), Rangpur (Bangladesh), 31.07. - 02.08.2025</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Shihab Hossain</first_name>
      <last_name>Shuvo</last_name>
    </author>
    <author>
      <first_name>Abdul Al Mahmud</first_name>
      <last_name>Riaz</last_name>
    </author>
    <author>
      <first_name>Masud Rana</first_name>
      <last_name>Paban</last_name>
    </author>
    <author>
      <first_name>Sajib</first_name>
      <last_name>Howlader</last_name>
    </author>
    <author>
      <first_name>Ratul</first_name>
      <last_name>Bhattacharjee</last_name>
    </author>
    <author>
      <first_name>Md Abu</first_name>
      <last_name>Talha</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
  </doc>
  <doc>
    <id>6382</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>28</issue>
    <volume>2025</volume>
    <articleNumber>107377</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A multidimensional assessment of electrification in automotive powertrains: Technical, operational, and strategic perspectives</title>
    <abstract language="eng">Global transportation is undergoing a profound transformation, driven by the convergence of decarbonization targets and advances in electrification technologies. Electrification, ranging from mild Hybrid Electric Vehicles (mHEVs) to Battery Electric Vehicles (BEVs), represents the main technological path in the transition from internal combustion engines. This transition is inherently multidimensional, simultaneously reshaping vehicle design, manufacturing systems, supply chains, customer value, and environmental outcomes in interconnected ways. Each architecture, including mHEV, Hybrid Electric Vehicle (HEV), Plug-in Hybrid Electric Vehicle (PHEV), and BEV, presents different tradeoffs in these dimensions, requiring a structured framework for systematic comparison. To address this gap, this work details an integrated framework that systematically compares electrification pathways across five interrelated dimensions: vehicle concepts, research and development, manufacturing and logistics, customer experience, and environmental footprint. This framework culminates in a decision matrix that consolidates trade-offs into a practical SWOT based tool for strategic planning.</abstract>
    <parentTitle language="eng">Results in Engineering</parentTitle>
    <identifier type="issn">2590-1230</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63829</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorIdentifierOrcid_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorIdentifierOrcid_6,PublisherName,TitleMain_1,Language,TitleParent_1,ArticleNumber,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.rineng.2025.107377</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Cassiano</first_name>
      <last_name>Santos</last_name>
    </author>
    <author>
      <first_name>Milad Kalume</first_name>
      <last_name>Neto</last_name>
    </author>
    <author>
      <first_name>João Marcos</first_name>
      <last_name>Kanieski</last_name>
    </author>
    <author>
      <first_name>Carlos Antônio</first_name>
      <last_name>Rufino Júnior</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <author>
      <first_name>Hudson</first_name>
      <last_name>Zanin</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6382/1-s2.0-S2590123025034322-main.pdf</file>
  </doc>
  <doc>
    <id>6360</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue>141, Part B</issue>
    <volume>2026</volume>
    <articleNumber>119101</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Thermal fault detection in battery systems using principal component analysis with adaptive thresholding</title>
    <abstract language="eng">Lithium-ion cells pose serious safety risks when they enter a state with highly exothermic reactions known as thermal runaway. Because elevated temperature is the ultimate trigger for this failure mode, reliable and timely detection of abnormal cell temperature is critical. Early detection enables, user warning, fast emergency response, and provides the basis for effective active prevention strategies. In this work, we present an unsupervised data-driven approach that detects thermal faults by monitoring inter-cell voltage deviations. We apply principal component analysis (PCA) to capture systematic changes in voltage homogeneity that occur when a cell within a battery module heats abnormally. By systematically analyzing the effects of thermal stress on voltage homogeneity under varying operating conditions, we reveal requirements for a reliable detection method. Leveraging these insights, we introduce an adaptive thresholding mechanism. This novel approach significantly boosts the sensitivity to faults for a wide range of operating conditions while maintaining detection robustness. We validate the method through extensive experiments in which we externally heat a single cell within a module with the power of 1 W. Compared to both conventional linear PCA and nonlinear kernel PCA with a constant threshold, linear PCA with adaptive thresholding achieves a significantly better balance between sensitivity and robustness across the full range of test conditions.</abstract>
    <parentTitle language="eng">Journal of Energy Storage</parentTitle>
    <identifier type="issn">2352-1538</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63603</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PublisherName,TitleMain_1,Language,TitleParent_1,ArticleNumber,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.est.2025.119101</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Michael</first_name>
      <last_name>Theiler</last_name>
    </author>
    <author>
      <first_name>Felix</first_name>
      <last_name>Nörpel</last_name>
    </author>
    <author>
      <first_name>Alexander</first_name>
      <last_name>Baumann</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6360/1-s2.0-S2352152X25038149-main.pdf</file>
  </doc>
  <doc>
    <id>6345</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue/>
    <volume>15</volume>
    <articleNumber>38489</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-11-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep segmentation of 3+1D radar point cloud for real-time roadside traffic user detection</title>
    <abstract language="eng">Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin).</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63458</identifier>
    <enrichment key="opus.import.date">2025-11-06T01:21:21+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">bd9298f4e4bdc7bb0c5d512fa98ea449</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-025-23019-6</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_DownloadUrl">https://github.com/bhanderisavan/roadside-radar-seg</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6199</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
    </author>
    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6345/41598_2025_Article_23019.pdf</file>
  </doc>
  <doc>
    <id>6333</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>SATC</publisherName>
    <publisherPlace>Johannesburg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Vehicle rescue sheets: opportunities and barriers in the South African context</title>
    <parentTitle language="eng">43rd Annual Southern African Transport Conference 2025</parentTitle>
    <identifier type="url">http://hdl.handle.net/2263/104908</identifier>
    <identifier type="isbn">978-0-0370-8021-0</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">43rd International Southern African Transport Conference (SATC 2025), Pretoria (South Africa), 07.-10.07.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Maria</first_name>
      <last_name>Vanderschuren</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Alexandra</first_name>
      <last_name>Newlands</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
  </doc>
  <doc>
    <id>6332</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>SATC</publisherName>
    <publisherPlace>Johannesburg</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Potential of infrastructure-based sensors to road safety</title>
    <parentTitle language="eng">43rd Annual Southern African Transport Conference 2025</parentTitle>
    <identifier type="isbn">978-0-0370-8021-0</identifier>
    <identifier type="url">http://hdl.handle.net/2263/104958</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">43rd International Southern African Transport Conference (SATC 2025), Pretoria (South Africa), 07.-10.07.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Aditya</first_name>
      <last_name>Haryanto</last_name>
    </author>
    <author>
      <first_name>Thiago</first_name>
      <last_name>de Borba</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
  </doc>
  <doc>
    <id>6164</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber>2025-01-0294</articleNumber>
    <type>article</type>
    <publisherName>SAE</publisherName>
    <publisherPlace>Warrendale</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-08-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">DISRUPT - Decentralized Intelligent System for Road User Prediction and Tracking</title>
    <parentTitle language="eng">SAE Technical Paper</parentTitle>
    <identifier type="issn">0148-7191</identifier>
    <identifier type="issn">2688-3627</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,PersonAuthorFirstName_9,PersonAuthorLastName_9,PersonAuthorFirstName_10,PersonAuthorLastName_10,PersonAuthorFirstName_11,PersonAuthorLastName_11,Enrichmentconference_title,Enrichmentconference_place,PublisherName,PublisherPlace,TitleMain_1,TitleAbstract_1,TitleParent_1,ArticleNumber,Volume,CompletedYear,IdentifierIssn</enrichment>
    <enrichment key="conference_title">2025 Stuttgart International Symposium</enrichment>
    <enrichment key="conference_place">Stuttgart, Germany</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.4271/2025-01-0294</enrichment>
    <enrichment key="THI_conferenceName">2025 Stuttgart International Symposium on Automotive and Engine Technology, Stuttgart (Germany), 02.-03.07.2025</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <author>
      <first_name>Frank</first_name>
      <last_name>Beutenmüller</last_name>
    </author>
    <author>
      <first_name>Lukas</first_name>
      <last_name>Brostek</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Doberstein</last_name>
    </author>
    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Obstbaum</last_name>
    </author>
    <author>
      <first_name>Antonia</first_name>
      <last_name>Pawlowski</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Rössert</last_name>
    </author>
    <author>
      <first_name>Lucas</first_name>
      <last_name>Sas-Brunschier</last_name>
    </author>
    <author>
      <first_name>Thilo</first_name>
      <last_name>Schön</last_name>
    </author>
    <author>
      <first_name>Jörg</first_name>
      <last_name>Sichermann</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
  </doc>
  <doc>
    <id>5985</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Accelerated Real-Life (ARL) Testing and Characterization of Automotive LiDAR Sensors to facilitate the Development and Validation of Enhanced Sensor Models</title>
    <abstract language="eng">In the realm of automated driving simulation and sensor modeling, the need for highly accurate sensor models is paramount for ensuring the reliability and safety of advanced driving assistance systems (ADAS). Hence, numerous works focus on the development of high-fidelity models of ADAS sensors, such as camera, Radar as well as modern LiDAR systems to simulate the sensor behavior in different driving scenarios, even under varying environmental conditions, considering for example adverse weather effects. However, aging effects of sensors, leading to suboptimal system performance, are mostly overlooked by current simulation techniques. This paper introduces a cutting-edge Hardware-in-the-Loop (HiL) test bench designed for the automated, accelerated aging and characterization of Automotive LiDAR sensors. The primary objective of this research is to address the aging effects of LiDAR sensors over the product life cycle, specifically focusing on aspects such as laser beam profile deterioration, output power reduction and intrinsic parameter drift, which are mostly neglected in current sensor models. By that, this proceeding research is intended to path the way, not only towards identifying and modeling respective degradation effects, but also to suggest quantitative model validation metrics.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59856</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2312.04229</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Marcel</first_name>
      <last_name>Kettelgerdes</last_name>
    </author>
    <author>
      <first_name>Tjorven</first_name>
      <last_name>Hillmann</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Hirmer</last_name>
    </author>
    <author>
      <first_name>Hüseyin</first_name>
      <last_name>Erdogan</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5985/2312.04229v1.pdf</file>
  </doc>
  <doc>
    <id>6319</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>179817</pageFirst>
    <pageLast>179838</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>13</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Impact of Adverse Weather on Road Safety: A Survey of Test Methods for Enhancing Safety of Automated Vehicles and Sensor Robustness in Challenging Environmental Conditions</title>
    <abstract language="eng">Adverse weather conditions can significantly affect environmental sensors and reduce the ability of automated vehicles to interpret the environment. This can lead to the failure of driving and safety functions. To validate and increase the robustness of these, several adverse weather test methods have been introduced in recent years. The survey first gives an overview of the current traffic and accident analysis with a focus on adverse weather influences to assess the most relevant weather phenomena. Overall, rain and glare are the statistically most important adverse weather phenomena in terms of accidents. Heavy fog is rare but can cause very serious accidents. In a second step, meteorological knowledge is incorporated and the key environmental indicators for the respective weather conditions are determined, particularly taking the environmental sensor characteristics into account. Both sets of information are then utilized to provide a qualified overview of simulative and physical test methods used to reproduce critical adverse weather situations. These methods are employed to investigate the disturbance effects of inclement weather on sensors and to test the robustness of automated vehicles. The paper reveals that various approaches are used to reproduce weather effects and that test methods, each with different advantages and disadvantages, have been developed to varying degrees depending on the type of the weather phenomena. Overall, this review integrates perspectives from road safety research, meteorology, and testing methodologies to provide new insights into the validation of automated driving systems in challenging weather conditions.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63190</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ACCESS.2025.3622501</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Michael</first_name>
      <last_name>Graf</last_name>
    </author>
    <author>
      <first_name>Dagmar</first_name>
      <last_name>Steinhauser</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26848">Brandmeier, Thomas</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
    <collection role="persons" number="27056">Steinhauser, Dagmar</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6319/Impact_of_Adverse_Weather.pdf</file>
  </doc>
  <doc>
    <id>6323</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>34</pageFirst>
    <pageLast>41</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>EVU</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Impact of vehicle electrification on fundamental accident reconstruction parameters</title>
    <parentTitle language="eng">Proceedings of the 30th Annual Congress of the EVU</parentTitle>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">30th Annual Congress of the EVU, Strasbourg (France), 26.-28.10.2022</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Paula</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>König</last_name>
    </author>
    <author>
      <first_name>Yannick</first_name>
      <last_name>Dengler</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Böhm</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
  </doc>
  <doc>
    <id>6322</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>253</pageFirst>
    <pageLast>261</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>EVU</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Systematic investigation of influences on advanced driver assistance systems for accident reconstruction</title>
    <parentTitle language="eng">Proceedings of the 31st Annual Congress of the EVU</parentTitle>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">31st Annual Congress of the EVU, Limassol (Cyprus), 05.-07.10.2023</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Paula</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>König</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
  </doc>
  <doc>
    <id>6321</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>450</pageFirst>
    <pageLast>457</pageLast>
    <pageNumber/>
    <edition/>
    <issue>74</issue>
    <volume>2023</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-24</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluation of the vehicle behaviour when not responding to the take-over request of Tesla Autopilot and Volkswagen Travel Assist</title>
    <abstract language="eng">In this work, driving tests were carried out with a Tesla Model 3 and VW ID.4. During the tests, the vehicles were controlled by an SAE Level 2 system (Tesla Autopilot and VW Travel Assist). The study aimed to investigate the behaviour of the vehicles when the driver does not react to the take-over request of the vehicle while it is in a non-critical driving situation. The aim was to clarify whether this can lead to accidents from a purely technical point of view and whether there is still room for improvement. As a result, it could be determined that a non-reaction of the driver to the take-over request leads to system-initiated braking decelerations, which can lead to a rear-end collision by the following traffic. Furthermore, secondary accidents are possible if the driver fails to act as a fallback. This could be reduced by improving the applicable UNECE standard R79 by specifying deceleration values for this case. In addition, the risk of an accident could be reduced by decelerating earlier and warning the environment. For this purpose, a driver monitoring system would also be recommended to allow situational decisions by the system. The implementation of the measures proposed could result in a significant increase in road safety while using these systems.</abstract>
    <parentTitle language="eng">Transportation Research Procedia</parentTitle>
    <identifier type="issn">2352-1465</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63210</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PublisherName,TitleMain_1,Language,TitleParent_1,PageNumber,PageFirst,PageLast,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.trpro.2023.11.167</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_conferenceName">TRANSCOM 2023: 15th International Scientific Conference on Sustainable, Modern and Safe Transport, Mikulov (Czech Republic), 29.-31.05.2023</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Paula</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6321/1-s2.0-S2352146523004647-main.pdf</file>
  </doc>
  <doc>
    <id>6320</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>133</pageFirst>
    <pageLast>140</pageLast>
    <pageNumber/>
    <edition/>
    <issue>74</issue>
    <volume>2023</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of a model environment for autonomous driving</title>
    <abstract language="eng">Developing automated driving functions can be elaborate and cost-intensive. Simulation helps to decrease both, the effort and the costs. With physical model environments of a smaller scale, automotive research can be improved even further.&#13;
&#13;
This work presents a method with five steps for building a scaled model environment to answer research questions regarding autonomous driving functions in the automotive domain. For this purpose, the five steps of the method are first introduced and explained. Each step comes with indications that can be further extended. Then, the method is performed by creating a model environment.&#13;
&#13;
The model environment build in this work is a recreation of an urban intersection in Ingolstadt, Germany, in the scale of 1:10. In addition to two model vehicles, the model environment consists of pedestrians and traffic control elements such as signs, signals, various lanes and road markings. The advantages of the model environment are low costs and high reproducibility. On the other hand it comes with limitations. For example, the materials used for construction (polylactic acid) are different from the materials in reality. The aim of this work was achieved by providing a method for building a model environment for automotive domain. The limitations of the built model environment and actual testing of automated driving functions will be performed in the future.</abstract>
    <parentTitle language="eng">Transportation Research Procedia</parentTitle>
    <identifier type="issn">2352-1465</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63201</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PublisherName,TitleMain_1,Language,TitleParent_1,PageNumber,PageFirst,PageLast,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.trpro.2023.11.122</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">TRANSCOM 2023: 15th International Scientific Conference on Sustainable, Modern and Safe Transport, Mikulov (Czech Republic), 29.-31.05.2023</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Robin</first_name>
      <last_name>Langer</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Bauder</last_name>
    </author>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Paula</last_name>
    </author>
    <author>
      <first_name>Tibor</first_name>
      <last_name>Kubjatko</last_name>
    </author>
    <author>
      <first_name>Hans-Georg</first_name>
      <last_name>Schweiger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="26111">Schweiger, Hans-Georg</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6320/1-s2.0-S2352146523004180-main.pdf</file>
  </doc>
  <doc>
    <id>6305</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue>10</issue>
    <volume>172</volume>
    <articleNumber>100529</articleNumber>
    <type>article</type>
    <publisherName>IOP Publishing</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-20</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Evaluation of Calendar Aging in Cells with Graphite: Silicon Anode Using Float Current Analysis Under the Influence of Voltage Hysteresis</title>
    <abstract language="eng">This study investigates the calendar aging of lithium-ion batteries with graphite-silicon anodes using float current analysis. While float current analysis is already a proven method for assessing aging in cells with graphite-based anodes, the presence of silicon introduces additional complexities due to its voltage hysteresis. We address this by comparing the results for the scaling factor separately for charge and discharge. The scaling factor is initially derived from charge and discharge GITT measurements on fresh cells, including an aging-induced shift among both electrode curves. This approach enables quantification of SEI growth ISEI growth, and cathode lithiation current ICL bridging measured results for float currents with capacity loss rate. As a result, the scaling factor during charge delivered the most meaningful results regarding fitted aging currents. By extending the estimation method based on the Arrhenius equation across temperatures from 5 °C to 50 °C, our model is validated against measured float currents, improving the predictive accuracy of long-term aging trends in silicon-containing anodes. Electrochemical impedance spectroscopy provided further insights into degradation mechanisms, revealing a strong correlation between cathode lithiation by salt decomposition and resistance increase at high voltages (⩾4.15 V), confirmed by pulse tests at 100% SOC showing a sharp resistance increase at&#13;
elevated voltages.</abstract>
    <parentTitle language="eng">Journal of The Electrochemical Society</parentTitle>
    <identifier type="issn">1945-7111</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-63053</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1149/1945-7111/ae0fe8</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Iqra</first_name>
      <last_name>Kiran</last_name>
    </author>
    <author>
      <first_name>Mohamed</first_name>
      <last_name>Azzam</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Nauman Zafar</first_name>
      <last_name>Butt</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6305/Kiran_2025_J_Electrochem.pdf</file>
  </doc>
  <doc>
    <id>6294</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>4</issue>
    <volume>9</volume>
    <articleNumber>e202500559</articleNumber>
    <type>article</type>
    <publisherName>Wiley</publisherName>
    <publisherPlace>Weinheim</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Analyzing the Impact of Electrolyte Motion Induced Salt Inhomogeneity Effect on Apparent Aging: Role of Current Rates and Temperature Effects in Accelerated Cyclic Aging Tests in Li-Ion Batteries</title>
    <abstract language="eng">Accurate and rapid assessment of lithium-ion battery lifetime is essential for predicting remaining lifespan, enabling the selection of appropriate cells for specific applications and determining suitability for second-life use. However, accelerated cyclic aging tests may underestimate a cell's total lifespan due to exaggerated capacity fade that does not occur under real-world conditions. This increased capacity fade is primarily driven by electrolyte motion induced salt inhomogeneity (EMSI) and loss of homogeneity of lithium distribution (HLD). This study investigates the impact of varying charge and discharge currents on capacity loss during accelerated testing in compressed NMC-Gr pouch cells. Most of the capacity loss observed during cycling is fully recoverable after a resting period, with some cells regaining up to 81% of their lost capacity. Contrary to expectations, cells subjected to the highest cycling currents do not exhibit the greatest recoverable capacity loss. This phenomenon can be attributed to the interplay between current and temperature: While higher cycling currents exacerbate EMSI and HLD loss, they simultaneously elevate cell temperature, which mitigates EMSI by weakening polarization, enhancing electrolyte salt diffusion and homogenizing lithium distribution in the anode. Consequently, higher temperatures counteract HLD and EMSI-effect and therefore reduce apparent capacity loss.</abstract>
    <parentTitle language="eng">Batteries &amp; Supercaps</parentTitle>
    <identifier type="issn">2566-6223</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-62944</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1002/batt.202500559</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Pablo</first_name>
      <last_name>Morales Torricos</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Gallenberger</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Droese</last_name>
    </author>
    <author>
      <first_name>Julia</first_name>
      <last_name>Kowal</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6294/Morales_Torricos_Analyzing_the_Impact.pdf</file>
  </doc>
  <doc>
    <id>6241</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-10-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Quantitative Kernel Estimation from Traffic Signs using Slanted Edge Spatial Frequency Response as a Sharpness Metric</title>
    <abstract language="eng">The sharpness is a critical optical property of automotive cameras, measured by the Spatial Frequency Response (SFR) within the end of line (EOL) test after manufacturing. This work presents a method to estimate the blurring kernel of automotive camera for state monitoring. To achieve this, Principal Component Analysis (PCA) is performed, using synthetic kernels generated by Zemax. The PCA model is built with approximately 1300 base kernels representing spatially variant point spread functions (PSFs). This model generates kernel samples during the estimation process. Synthetic images are created by convolving the synthetic kernels with reference traffic sign images and compared with real-life data captured by an automotive camera. These synthetic data are utilized for algorithm development, and later on validation is performed on real-life data. The algorithm extracts two 45 x 45 pixels regions of interest (ROIs) containing slanted edges from the blurred image and crops matching ROIs from a reference sharp image. Each candidate kernel blurs the reference ROIs, and the resulting Spatial Frequency Response (SFR) is compared with the blurred ROIs’ SFR. Differential evolution optimization minimizes the SFR difference, selecting the kernel that best matches the observed blur. The final kernel is evaluated against the true kernel for accuracy. Structural similarity index measure (SSIM) between the original and estimated blurred ROIs ranges from 0.808 to 0.945. For true vs. estimated kernels, SSIM varies from 0.92 to 0.98. Pearson correlation coefficients range from 0.84 to 0.99, Cosine similarity from 0.86 to 0.98, and mean squared error (MSE) from 1.1 x 10-5 to 8.3 x 10-5. Validation on real-life camera images shows that the SSIM between estimated ROI is 0.82 indicating a sufficient level of accuracy in kernel estimation to detect potential degradation of the camera.</abstract>
    <parentTitle language="eng">Research Square</parentTitle>
    <identifier type="issn">2693-5015</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-62419</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">posted-content/preprint</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PublisherName,TitleMain_1,TitleAbstract_1,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.21203/rs.3.rs-6725582/v1</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6691</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Amit</first_name>
      <last_name>Pandey</last_name>
    </author>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Nandana Kappuva</first_name>
      <last_name>Veettil</last_name>
    </author>
    <author>
      <first_name>Bernhard</first_name>
      <last_name>Wunderle</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6241/Quantitative_Kernel_Estimation.pdf</file>
  </doc>
  <doc>
    <id>6235</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>779</pageFirst>
    <pageLast>785</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-10-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AWARE2ALL: Human Centric Interaction and Safety Systems for Increasing the Share of Automated Vehicles</title>
    <abstract language="eng">The AWARE2ALL project is designed to address the new challenges of Highly Automated Vehicles (HAVs) from a human-centric perspective. These vehicles will allow occupants to engage in non-driving activities, rising research questions about occupant behavior, activities, and Human-Machine Interfaces (HMI) to keep them aware of the situation and the automation mode. The project aims to ensure safe operation of HAVs by developing safety and HMI systems that provide a holistic understanding of the scene. This includes continuous monitoring of the interior situation and advanced passive safety systems for occupant safety, as well as a surround perception system and external HMI for the safety of Human Road Users (HRUs). AWARE2ALL is paving the way for HAV deployment by effectively addressing changes in road safety and interactions between different road users caused by the emergence of HAVs. It is developing innovative technologies, assessment tools, and methodologies to adapt to new scenarios in mixed traffic. The project builds on previous research and aims to mitigate new safety risks associated with the introduction of HAVs.</abstract>
    <parentTitle language="eng">Transport Transitions: Advancing Sustainable and Inclusive Mobility, Proceedings of the 10th TRA Conference, 2024, Dublin, Ireland-Volume 1: Safe and Equitable Transport</parentTitle>
    <identifier type="isbn">978-3-031-88974-5</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-62356</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">book-chapter</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorIdentifierOrcid_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorIdentifierOrcid_6,PublisherName,PublisherPlace,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,IdentifierIsbn,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/978-3-031-88974-5_112</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">10th Transport Research Arena (TRA 2024), Dublin (Ireland), 15.-18.04.2024</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Marcos</first_name>
      <last_name>Nieto</last_name>
    </author>
    <editor>
      <first_name>Ciaran</first_name>
      <last_name>McNally</last_name>
    </editor>
    <author>
      <first_name>Oihana</first_name>
      <last_name>Otaegui</last_name>
    </author>
    <editor>
      <first_name>Páraic</first_name>
      <last_name>Carroll</last_name>
    </editor>
    <author>
      <first_name>Maria</first_name>
      <last_name>Panou</last_name>
    </author>
    <editor>
      <first_name>Beatriz</first_name>
      <last_name>Martinez-Pastor</last_name>
    </editor>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <editor>
      <first_name>Bidisha</first_name>
      <last_name>Ghosh</last_name>
    </editor>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <editor>
      <first_name>Marina</first_name>
      <last_name>Efthymiou</last_name>
    </editor>
    <author>
      <first_name>Ariadna</first_name>
      <last_name>Rodríguez</last_name>
    </author>
    <editor>
      <first_name>Nikolaos</first_name>
      <last_name>Valantasis-Kanellos</last_name>
    </editor>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6235/978-3-031-88974-5_112.pdf</file>
  </doc>
  <doc>
    <id>6228</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-09-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison of Localization Algorithms between Reduced-Scale and Real-Sized Vehicles Using Visual and Inertial Sensors</title>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2507.11241</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Kern</last_name>
    </author>
    <author>
      <first_name>Leon</first_name>
      <last_name>Tolksdorf</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
  </doc>
  <doc>
    <id>6223</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-09-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Convolutional Neural Networks and Thresholding Approaches for Single and Multi-Sensor Detection of Partial Discharges in Electrical Machine Stators</title>
    <parentTitle language="eng">Proceedings of the 15th International 2025 IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED)</parentTitle>
    <identifier type="isbn">979-8-3503-8820-6</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/SDEMPED53223.2025.11153974</enrichment>
    <enrichment key="THI_conferenceName">15th International IEEE Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED 2025), Dallas (USA), 24.-27.08.2025</enrichment>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Rauscher</last_name>
    </author>
    <author>
      <first_name>Julian</first_name>
      <last_name>Braun</last_name>
    </author>
    <author>
      <first_name>Rainer</first_name>
      <last_name>Hiemer</last_name>
    </author>
    <author>
      <first_name>Marcelo Lobo</first_name>
      <last_name>Heldwein</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>6206</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1668</pageFirst>
    <pageLast>1673</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-09-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Domain Awareness via Spectral-normalized Neural Gaussian Processes for E2E Autonomous Vehicle Control</title>
    <parentTitle language="eng">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">979-8-3315-3803-3</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE Intelligent Vehicles Symposium (IV)</enrichment>
    <enrichment key="conference_place">Cluj-Napoca, Romania</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV64158.2025.11097444</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">36th IEEE Intelligent Vehicles Symposium (IV 2025), Cluj-Napoca (Romania), 22.-25.06.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Carla</first_name>
      <last_name>Roth</last_name>
    </author>
    <author>
      <first_name>Fabian</first_name>
      <last_name>Ulreich</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Ebert</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="27210">Ebert, Martin</collection>
  </doc>
  <doc>
    <id>6202</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1033</pageFirst>
    <pageLast>1039</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-09-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">V2X-Gaussians: Gaussian Splatting for Multi-Agent Cooperative Dynamic Scene Reconstruction</title>
    <parentTitle language="eng">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">979-8-3315-3803-3</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE Intelligent Vehicles Symposium (IV)</enrichment>
    <enrichment key="conference_place">Cluj-Napoca, Romania</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV64158.2025.11097436</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">36th IEEE Intelligent Vehicles Symposium (IV 2025), Cluj-Napoca (Romania), 22.-25.06.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Abhishek Dinkar</first_name>
      <last_name>Jagtap</last_name>
    </author>
    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
    </author>
    <author>
      <first_name>Sanath Tiptur</first_name>
      <last_name>Sadashivaiah</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
  </doc>
  <doc>
    <id>6199</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>18</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-09-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Deep Segmentation of 3+1D Radar Point Cloud for Real-Time Roadside Traffic User Detection</title>
    <abstract language="eng">Smart cities rely on intelligent infrastructure to enhance road safety, optimize traffic flow, and enable vehicle-to-infrastructure (V2I) communication. A key component of such infrastructure is an efficient and real-time perception system that accurately detects diverse traffic participants. Among various sensing modalities, automotive radar is one of the best choices due to its robust performance in adverse weather and low-light conditions. However, due to low spatial resolution, traditional clustering-based approaches for radar object detection often struggle with vulnerable road user detection and nearby object separation. Hence, this paper proposes a deep learning-based 3+1D radar point cloud clustering methodology tailored for smart infrastructure-based perception applications. This approach first performs semantic segmentation of the radar point cloud, followed by instance segmentation to generate well-formed clusters with class labels using a deep neural network. It also detects single-point objects that conventional methods often miss. The described approach is developed and experimented using a smart infrastructure-based sensor setup and it performs segmentation of the point cloud in real-time. Experimental results demonstrate 95.35% F1-macro score for semantic segmentation and 91.03% mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 for instance segmentation. Further, the complete pipeline operates at 43.61 frames per second with a memory requirement of less than 0.7 MB on the edge device (Nvidia Jetson AGX Orin).</abstract>
    <parentTitle language="eng">Research Square</parentTitle>
    <identifier type="issn">2693-5015</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-61996</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">posted-content/preprint</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PublisherName,TitleMain_1,TitleAbstract_1,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.21203/rs.3.rs-7222130/v1</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6345</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
    </author>
    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6199/deep_segmentation.pdf</file>
  </doc>
  <doc>
    <id>6198</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>136584</pageFirst>
    <pageLast>136597</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>13</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-09-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data-Driven Vehicle Dynamics: Lever-Aging SINDy for Optimization-Based Vehicular Motion Planning</title>
    <abstract language="eng">Motion planning remains a crucial challenge for the widespread adoption of autonomous vehicles. This paper presents a novel approach that integrates an empirical plant model within an optimization-based motion planning architecture. The model prioritizes performance and efficiency while maintaining interpretability. We introduce a methodology that utilizes a data-driven approach to derive an interpretable description of the evolution of vehicle states over time using sparse regression. This method allows effective learning from limited datasets, eliminating the need for extensive and expensive data collection. Our approach addresses the trade-off between performance and accuracy, enabling adaptation to diverse driving scenarios. We affirm the efficacy of our methodology via an extensive analysis, evaluating the independent prediction performance across diverse metrics. Additionally, we examine the overall tracking performance when incorporated into an optimization-based framework. Finally, we present a comparative analysis and discuss the subsequent impact on overall motion planning and decision-making in relation to a state-of-the-art single-track model.</abstract>
    <parentTitle language="eng">IEEE Access</parentTitle>
    <identifier type="issn">2169-3536</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-61981</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/legalcode</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ACCESS.2025.3594892</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Daniel</first_name>
      <last_name>Weihmayr</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <author>
      <first_name>Hormoz</first_name>
      <last_name>Marzbani</last_name>
    </author>
    <author>
      <first_name>Reza</first_name>
      <last_name>Jazar</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6198/Data-Driven_Vehicle.pdf</file>
  </doc>
  <doc>
    <id>6195</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1517</pageFirst>
    <pageLast>1522</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-09-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Influence of Autonomous Vehicle Interior Design on Occupant Injuries</title>
    <parentTitle language="eng">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">979-8-3315-3803-3</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE Intelligent Vehicles Symposium (IV)</enrichment>
    <enrichment key="conference_place">Cluj-Napoca, Romania</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV64158.2025.11097519</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">36th IEEE Intelligent Vehicles Symposium (IV 2025), Cluj-Napoca (Romania), 22.-25.06.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Lukas</first_name>
      <last_name>Rozek</last_name>
    </author>
    <author>
      <first_name>Andrew</first_name>
      <last_name>Harrison</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
  </doc>
  <doc>
    <id>6186</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>51</pageFirst>
    <pageLast>58</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-09-03</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cooperative Maneuver Coordination: Smart Infrastructure for VRUs Collision Avoidance with Trajectory Planning</title>
    <parentTitle language="eng">Proceedings: 2025 IEEE 3rd International Conference on Mobility, Operations, Services and Technologies, MOST 2025</parentTitle>
    <identifier type="isbn">979-8-3315-1160-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE 3rd International Conference on Mobility, Operations, Services and Technologies (MOST)</enrichment>
    <enrichment key="conference_place">Newark, DE, USA</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/MOST65065.2025.00015</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">3rd IEEE International Conference on Mobility: Operations, Services, and Technologies (MOST 2025), Newark (USA), 04.-06.05.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Thiago</first_name>
      <last_name>de Borba</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Hormoz</first_name>
      <last_name>Marzbani</last_name>
    </author>
    <author>
      <first_name>Reza</first_name>
      <last_name>Jazar</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
  </doc>
  <doc>
    <id>6161</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-08-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Collision Probability Estimation for Optimization-based Vehicular Motion Planning</title>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2505.21161</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Leon</first_name>
      <last_name>Tolksdorf</last_name>
    </author>
    <author>
      <first_name>Arturo</first_name>
      <last_name>Tejada</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Birkner</last_name>
    </author>
    <author>
      <first_name>Nathan</first_name>
      <last_name>van de Wouw</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="23939">Birkner, Christian</collection>
  </doc>
  <doc>
    <id>6112</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1787</pageFirst>
    <pageLast>1794</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-08-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-Based Analysis</title>
    <parentTitle language="eng">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">979-8-3315-3803-3</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T05:40:09Z","timestamp":1754545209112,"version":"3.41.3"},"reference-count":31,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,22]]},"DOI":"10.1109\/iv64158.2025.11097767","type":"proceedings-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T17:54:55Z","timestamp":1754502895000},"page":"1787-1794","source":"Crossref","is-referenced-by-count":0,"title":["Assessing the Completeness of Traffic Scenario Categories for Automated Highway Driving Functions via Cluster-Based Analysis"],"prefix":"10.1109","author":[{"given":"Niklas","family":"Ro\u00dfberg","sequence":"first","affiliation":[{"name":"Technische Hochschule Ingolstadt,Ingolstadt,Germany,85049"}]},{"given":"Marion","family":"Neumeier","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt,Ingolstadt,Germany,85049"}]},{"given":"Sinan","family":"Hasirlioglu","sequence":"additional","affiliation":[{"name":"AUDI AG,Ingolstadt,Germany,85045"}]},{"given":"Mohamed Essayed","family":"Bouzouraa","sequence":"additional","affiliation":[{"name":"AUDI AG,Ingolstadt,Germany,85045"}]},{"given":"Michael","family":"Botsch","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt,Ingolstadt,Germany,85049"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121983"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC55140.2022.9922127"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/itsc55140.2022.9922419"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3274536"},{"journal-title":"Gpt-driver: Learning to drive with gpt","year":"2023","author":"Mao","key":"ref5"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813994"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW63382.2024.00350"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2019.8917326"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1016\/j.mlwa.2022.100377"},{"journal-title":"Unsupervised graph attention autoencoder for attributed networks using k-means loss","year":"2023","author":"Bekkair","key":"ref10"},{"journal-title":"Deep representation learning and clustering of traffic scenarios","year":"2020","author":"Harmening","key":"ref11"},{"key":"ref12","article-title":"Neural discrete representation learning","author":"Van den Oord","year":"2017","journal-title":"Neural Information Processing Systems (NIPS)"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.02084"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569552"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/vtc2021-spring51267.2021.9449032"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/IV48863.2021.9575441"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/IVWorkshops54471.2021.9669219"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/IAVVC63304.2024.10786405"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2022.11.019"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1145\/3618257.3624820"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110176"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2023.3260270"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IV55152.2023.10186536"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i8.20796"},{"journal-title":"Auto-encoding variational bayes","year":"2013","author":"Kingma","key":"ref25"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/IROS58592.2024.10801610"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.21437\/interspeech.2019-3232"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1080\/15389588.2019.1602727"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/IV55156.2024.10588471"},{"journal-title":"Tree-based sce-nario classification: A formal framework for coverage analysis on test drives of autonomous vehicles","year":"2023","author":"Schallau","key":"ref30"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC48978.2021.9565120"}],"event":{"name":"2025 IEEE Intelligent Vehicles Symposium (IV)","start":{"date-parts":[[2025,6,22]]},"location":"Cluj-Napoca, Romania","end":{"date-parts":[[2025,6,25]]}},"container-title":["2025 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11097351\/11097337\/11097767.pdf?arnumber=11097767","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T05:17:18Z","timestamp":1754543838000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11097767\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":31,"URL":"https:\/\/doi.org\/10.1109\/iv64158.2025.11097767","relation":{},"subject":[],"published":{"date-parts":[[2025,6,22]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE Intelligent Vehicles Symposium (IV)</enrichment>
    <enrichment key="conference_place">Cluj-Napoca, Romania</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV64158.2025.11097767</enrichment>
    <enrichment key="THI_conferenceName">36th IEEE Intelligent Vehicles Symposium (IV 2025), Cluj-Napoca (Romania), 22.-25.06.2025</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Niklas</first_name>
      <last_name>Roßberg</last_name>
    </author>
    <author>
      <first_name>Marion</first_name>
      <last_name>Neumeier</last_name>
    </author>
    <author>
      <first_name>Sinan</first_name>
      <last_name>Hasirlioglu</last_name>
    </author>
    <author>
      <first_name>Mohamed Essayed</first_name>
      <last_name>Bouzouraa</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
  </doc>
  <doc>
    <id>6111</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>663</pageFirst>
    <pageLast>670</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-08-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Validation of a POMDP Framework for Interaction-aware Trajectory Prediction in Vehicle Safety</title>
    <parentTitle language="eng">2025 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">979-8-3315-3803-3</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T18:10:05Z","timestamp":1754503805725,"version":"3.41.3"},"reference-count":0,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,22]],"date-time":"2025-06-22T00:00:00Z","timestamp":1750550400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,22]]},"DOI":"10.1109\/iv64158.2025.11097498","type":"proceedings-article","created":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T17:54:55Z","timestamp":1754502895000},"page":"663-670","source":"Crossref","is-referenced-by-count":0,"title":["Validation of a POMDP Framework for Interaction-aware Trajectory Prediction in Vehicle Safety"],"prefix":"10.1109","author":[{"given":"Tim","family":"Elter","sequence":"first","affiliation":[{"name":"Technische Hochschule Ingolstadt,CARISSMA,Ingolstadt,Germany,85049"}]},{"given":"Tobias","family":"Dirndorfer","sequence":"additional","affiliation":[{"name":"CARIAD SE,Wolfsburg,Germany,38440"}]},{"given":"Michael","family":"Botsch","sequence":"additional","affiliation":[{"name":"Technische Hochschule Ingolstadt,CARISSMA,Ingolstadt,Germany,85049"}]},{"given":"Wolfgang","family":"Utschick","sequence":"additional","affiliation":[{"name":"Technical University of Munich,Munich,Germany,80333"}]}],"member":"263","event":{"name":"2025 IEEE Intelligent Vehicles Symposium (IV)","location":"Cluj-Napoca, Romania","start":{"date-parts":[[2025,6,22]]},"end":{"date-parts":[[2025,6,25]]}},"container-title":["2025 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11097351\/11097337\/11097498.pdf?arnumber=11097498","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,6]],"date-time":"2025-08-06T17:54:59Z","timestamp":1754502899000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11097498\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,22]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1109\/iv64158.2025.11097498","relation":{},"subject":[],"published":{"date-parts":[[2025,6,22]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2025 IEEE Intelligent Vehicles Symposium (IV)</enrichment>
    <enrichment key="conference_place">Cluj-Napoca, Romania</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV64158.2025.11097498</enrichment>
    <enrichment key="THI_conferenceName">36th IEEE Intelligent Vehicles Symposium (IV 2025), Cluj-Napoca (Romania), 22.-25.06.2025</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_DownloadUrl">https://zenodo.org/records/15227907</enrichment>
    <author>
      <first_name>Tim</first_name>
      <last_name>Elter</last_name>
    </author>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Dirndorfer</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
  </doc>
  <doc>
    <id>6096</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue/>
    <volume>15</volume>
    <articleNumber>24340</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-08-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">AI-driven point cloud framework for predicting solder joint reliability using 3D FEA data</title>
    <abstract language="eng">Crack propagation in solder joints remains a major challenge impacting the thermo-mechanical reliability of electronic devices, underscoring the importance of optimizing package and solder pad designs. Traditional Finite Element Analysis (FEA) techniques for predicting solder joint lifespan often rely on manual post-processing to identify high-risk regions for plastic strain accumulation. However, this manual process can fail to detect complex and subtle failure mechanisms and purely based on averaging the creep strain and correlating it to lifetime values collected from experiments using Coffin Manson equation. To address these limitations, this study presents an Artificial Intelligence (AI) framework designed for automated 3D FEA post-processing of surface-mounted devices (SMDs) assembled to Printed Circuit Board (PCB). This framework integrates 3D Convolutional Neural Networks (CNNs) and PointNet architectures to automatically extract complex spatial features from 3D FEA data. These learned features are then linked to experimentally measured solder joint lifetimes through fully connected neural network layers, allowing the model to capture complex and nonlinear failure behaviours. The research specifically targets crack development in solder joints of ceramic-based high-power LED packages used in automotive lighting systems. This dataset included variations in two-pad and three-pad configurations, as well as thin and thick film metallized ceramic substrates. Results from the study demonstrate that the PointNet model outperforms the 3D CNN, achieving a high correlation with experimental data (R2 = 99.91%). This AI-driven, automated feature extraction approach significantly improves the accuracy and provide the more reliable models for solder joint lifetime predictions, offering a substantial improvement over traditional method.</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="issn">2045-2322</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-60961</identifier>
    <enrichment key="opus.import.date">2025-07-29T12:55:28+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">535624b53ec0aa8bfaebb362840e8c71</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-025-06902-0</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5830</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Zubair Akhtar</first_name>
      <last_name>Mohd</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Schmid</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <collection role="persons" number="26880">Schmid, Maximilian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6096/41598_2025_Article_6902.pdf</file>
  </doc>
  <doc>
    <id>6092</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue>7</issue>
    <volume>172</volume>
    <articleNumber>070529</articleNumber>
    <type>article</type>
    <publisherName>IOP Publishing</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-07-29</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Float Current Analysis for Lithium-Ion Battery Aging: Insights into SEI Growth and Cathode Lithiation with EIS and ICP OES</title>
    <abstract language="eng">This study investigates calendar-aging mechanisms in lithium-ion batteries, focusing on cathode lithiation due to decomposition of conductive salt and SEI growth, by correlating quantified float currents, capacity loss rates, and pulse resistances with changes in electrochemical impedance spectroscopy (EIS) spectra. Seven SamSung 25 R cells are aged at different float voltages with periodic EIS measurements at 30 °C. Using a pre-characterization cell, the internal processes via EIS are allocated across various states of charge and temperatures and GITT measurements are performed to derive scaling factors. GITT, float currents and capacity loss rate measurements at 30 °C enables the separation of SEI growth ISEI growth and cathode lithiation current ICL based on float current behavior across a temperature range of 5 °C to 50 °C. The distribution of relaxation times (DRT) method is employed to deconvolute overlapping electrochemical processes. EIS and DRT analyses showed significant changes in cathode charge transfer resistance and diffusion, confirming that cathode lithiation correlates substantially to elevated internal resistance at high cell voltages. The theory of ISEI growth and ICL is further supported using inductively coupled plasma atomic emission spectroscopy by quantifying elemental inventory changes and linking phosphorus release and lithium consumption to degradation mechanisms.</abstract>
    <parentTitle language="eng">Journal of The Electrochemical Society</parentTitle>
    <identifier type="issn">1945-7111</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-60922</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1149/1945-7111/adef85</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Mohamed</first_name>
      <last_name>Azzam</last_name>
    </author>
    <author>
      <first_name>Atakhan</first_name>
      <last_name>Aydin</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Dirk Uwe</first_name>
      <last_name>Sauer</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6092/Azzam_2025_J._Electrochem._Soc._172_070529.pdf</file>
  </doc>
  <doc>
    <id>6086</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>27</pageNumber>
    <edition/>
    <issue>1</issue>
    <volume>12</volume>
    <articleNumber>17</articleNumber>
    <type>article</type>
    <publisherName>SpringerOpen</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-08-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Vehicle crash simulation models for reinforcement learning driven crash-detection algorithm calibration</title>
    <abstract language="eng">The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle’s geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which is adapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time implementation of the methodology is illustrated using an actual vehicle model.</abstract>
    <parentTitle language="eng">Advanced Modeling and Simulation in Engineering Sciences</parentTitle>
    <identifier type="issn">2213-7467</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-60861</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PublisherName,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,ArticleNumber,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/3763</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1186/s40323-025-00288-4</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Böhmländer</last_name>
    </author>
    <author>
      <first_name>Luděk</first_name>
      <last_name>Hynčík</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6086/s40323-025-00288-4.pdf</file>
  </doc>
  <doc>
    <id>6055</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>14</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>TechRxiv</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-07-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">YoFlow Method for Scenario Based Automatic Accident Detection</title>
    <abstract language="eng">Recent advances in sensor and computing technologies have enabled roadside units (RSUs) to not only monitor traffic flow but also process data in real time to improve road safety. However, leveraging RSUs for proactive accident detection remains a challenging and underexplored task, partly due to the lack of diverse accident data. To address this, this study proposes two key contributions: (i) a scenario based synthetic data generation framework, and (ii) YoFlow, a novel system for vehicle-to-vehicle accident detection from a simulated RSU camera perspective. The proposed framework leverages the PEGASUS methodology and the BeamNG.tech simulation platform to create the SB-SIF dataset, which includes five representative intersection crash scenarios derived from German accident data. SB-SIF dataset contains 914 crash videos, 123 near-miss events, and 924 normal traffic instances and is publicly available at: https://doi.org/10.5281/zenodo.15267252. The proposed YoFlow system identifies accidents by analyzing temporal variations in vehicle speed vectors, using YOLO for vehicle classification and CUDA-accelerated dense optical flow to capture abrupt motion changes. Extracted features are processed and classified using an XGBoost model, achieving 94% recall and 90% precision in accident detection.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-60556</identifier>
    <enrichment key="local_crossrefDocumentType">posted-content/preprint</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PublisherName,TitleMain_1,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.36227/techrxiv.175099959.99453472/v1</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_DownloadUrl">https://doi.org/10.5281/zenodo.15267252</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6530</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Aditya</first_name>
      <last_name>Haryanto</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6055/1308057.pdf</file>
  </doc>
  <doc>
    <id>6050</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>13</pageNumber>
    <edition/>
    <issue>6</issue>
    <volume>172</volume>
    <articleNumber>060523</articleNumber>
    <type>article</type>
    <publisherName>IOP Publishing</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-07-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Assigning Calendar Aging during Open Circuit and Float Current Analysis to Anode and Cathode Aging Using Three-Electrode Cells</title>
    <abstract language="eng">Analyzing calendar aging by float current analysis, instead of solely repeating capacity tests, gains more insights into aging. However, the underlying mechanisms of the recharge current required to keep the voltage constant are not yet fully understood. Therefore, this study performs float current and additional open circuit voltage (OCV) tests on 3-electrode cells for four different voltages at 30 °C. By monitoring the potentials of the anode and the cathode over time individually, the aging rate on both electrodes and the present capacity can be calculated based on adjusting the electrode-specific OCV curves according to the voltage slippery theory. The electrode-specific aging and the capacity prediction show high agreement among both methods and the capacity tests at the end of the experiment. As a result, the capacity loss appears linear-like and is associated with the loss of active lithium mainly due to SEI formation on the anode. Additionally, a significant cathode voltage decay is observed especially for higher cell voltage. According to the literature, this is associated with cathode lithiation where the conductive salt is decomposed by inserting active lithium into the cathode masking SEI losses. Finally, OCV and float current analysis return comparable results and support present 2-electrode evaluation approaches.</abstract>
    <parentTitle language="eng">Journal of the Electrochemical Society</parentTitle>
    <identifier type="issn">1945-7111</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-60504</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by-nc-nd/4.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorIdentifierOrcid_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorIdentifierOrcid_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,PublisherName,TitleMain_1,TitleAbstract_1,TitleParent_1,ArticleNumber,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1149/1945-7111/ade295</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Qing</first_name>
      <last_name>Yu</last_name>
    </author>
    <author>
      <first_name>Iqra</first_name>
      <last_name>Kiran</last_name>
    </author>
    <author>
      <first_name>Moritz</first_name>
      <last_name>Ehrensberger</last_name>
    </author>
    <author>
      <first_name>Luiza</first_name>
      <last_name>Streck</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Roth</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Jossen</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6050/J._Electrochem._Soc._172_060523.pdf</file>
  </doc>
  <doc>
    <id>6042</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-27</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">An Unsupervised Random Forest Clustering Technique for Automatic Traffic Scenario Categorization</title>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1528</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2004.02121</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Friedrich</first_name>
      <last_name>Kruber</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Wurst</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
  </doc>
  <doc>
    <id>6028</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-26</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Modular Production Control with Multi-Agent Deep Q-Learning</title>
    <parentTitle language="eng">Proceedings: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)</parentTitle>
    <identifier type="isbn">978-1-7281-2989-1</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2021 IEEE 26th International Conference on Emerging Technologies and Factory Automation (ETFA)</enrichment>
    <enrichment key="conference_place">Vasteras, Sweden</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ETFA45728.2021.9613177</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vasteras (Sweden), 07.-10.09.2021</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Dennis</first_name>
      <last_name>Gankin</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Mayer</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Zinn</last_name>
    </author>
    <author>
      <first_name>Birgit</first_name>
      <last_name>Vogel-Heuser</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>6017</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>15</pageNumber>
    <edition/>
    <issue>131, Part A</issue>
    <volume>2025</volume>
    <articleNumber>117323</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">The important role of lithiation-induced pressure change on apparent aging of lithium-ion batteries during accelerated cyclic aging tests</title>
    <abstract language="eng">In assessing the lifespan of lithium-ion batteries in laboratory conditions, the measured capacity declines during accelerated cyclic aging tests but increases when the cells are left idle. This recoverable capacity loss, known in literature as apparent aging, results from an inhomogeneous lithium distribution in the anode appearing in dV/dQ and capacity difference analysis. This study explores the origin of the apparent aging by correlating lithium distribution in the anode and recoverable capacity loss, with the lithiation induced pressure change and generated electrolyte motion during cycling. To this end, we compressed five commercial pouch lithium-ion cells (NMC/Gr) with holder stiffnesses ranging from 0.9 to 15.4 MPa/mm and offset pressure from 289 to 681 kPa and subjected them to continuous cyclic aging tests. While offset pressure had no significant impact, we observed, that holder stiffnesses over 3.3 MPa/mm induces an apparent capacity loss, which is recoverable by 35 % during a resting phase. The root cause is the so-called electrolyte motion induced salt inhomogeneity (EMSI), driven by continuous high current cycling and electrolyte motion, which is intensified by higher holder stiffness but not by offset pressure. These insights are crucial for improving the accuracy of accelerated aging tests and enhancing battery lifespan predictions.</abstract>
    <parentTitle language="eng">Journal of Energy Storage</parentTitle>
    <identifier type="issn">2352-1538</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-60173</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.est.2025.117323</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Pablo</first_name>
      <last_name>Morales Torricos</last_name>
    </author>
    <author>
      <first_name>Sascha</first_name>
      <last_name>Berg</last_name>
    </author>
    <author>
      <first_name>Egbert</first_name>
      <last_name>Figgemeier</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6017/1-s2.0-S2352152X25020365-main.pdf</file>
  </doc>
  <doc>
    <id>6006</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>17</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">ResFed: Communication Efficient Federated Learning by Transmitting Deep Compressed Residuals</title>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4637</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2212.05602</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
    </author>
    <author>
      <first_name>Liguo</first_name>
      <last_name>Zhou</last_name>
    </author>
    <author>
      <first_name>Lingjuan</first_name>
      <last_name>Lyu</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Alois</first_name>
      <last_name>Knoll</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
  </doc>
  <doc>
    <id>6015</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>The Eurographics Association</publisherName>
    <publisherPlace>Goslar</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">No More Shading Languages: Compiling C++ to Vulkan Shaders</title>
    <abstract language="eng">Graphics APIs have traditionally relied on shading languages, however, these languages have a number of fundamental defects and limitations. By contrast, GPU compute platforms offer powerful, feature-rich languages suitable for heterogeneous compute. We propose reframing shading languages as embedded domain-specific languages, layered on top of a more general language like C++, doing away with traditional limitations on pointers, functions, and recursion, to the benefit of programmability. This represents a significant compilation challenge because the limitations of shaders are reflected in their lower-level representations. We present the Vcc compiler, which allows conventional C and C++ code to run as Vulkan shaders. Our compiler is complemented by a simple shading library and exposes GPU particulars as intrinsics and annotations. We evaluate the performance of our compiler using a selection of benchmarks, including a real-time path tracer, achieving competitive performance compared to their native CUDA counterparts.</abstract>
    <parentTitle language="eng">High-Performance Graphics - Symposium Papers</parentTitle>
    <identifier type="isbn">978-3-03868-291-2</identifier>
    <identifier type="url">https://diglib.eg.org/handle/10.2312/hpg20251167</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-60151</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.2312/hpg.20251167</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">High-Performance Graphics (HPG 2025), Copenhagen (Denmark), 23.-25.06.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_DownloadUrl">https://diglib.eg.org/bitstreams/284640db-e935-40da-afd6-c475e0cb741b/download</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Hugo</first_name>
      <last_name>Devillers</last_name>
    </author>
    <editor>
      <first_name>Aaron</first_name>
      <last_name>Knoll</last_name>
    </editor>
    <author>
      <first_name>Matthias</first_name>
      <last_name>Kurtenacker</last_name>
    </author>
    <editor>
      <first_name>Christoph</first_name>
      <last_name>Peters</last_name>
    </editor>
    <author>
      <first_name>Richard</first_name>
      <last_name>Membarth</last_name>
    </author>
    <author>
      <first_name>Stefan</first_name>
      <last_name>Lemme</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Kenzel</last_name>
    </author>
    <author>
      <first_name>Ömercan</first_name>
      <last_name>Yazici</last_name>
    </author>
    <author>
      <first_name>Philipp</first_name>
      <last_name>Slusallek</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43674">Membarth, Richard</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6015/hpg20251167.pdf</file>
  </doc>
  <doc>
    <id>5886</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1777</pageFirst>
    <pageLast>1791</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-05-01</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">GPUs All Grown-Up: Fully Device-Driven SpMV Using GPU Work Graphs</title>
    <abstract language="eng">Sparse matrix-vector multiplication (SpMV) is a key operation across high-performance computing, graph analytics, and many more applications. In these applications, the matrix characteristics, notably non-zero elements per row, can vary widely and impact which algorithm performs best. Thus, Graphics Processing Unit (GPU) SpMV algorithms often rely on costly preprocessing to determine what per-row algorithm to select to achieve high performance. In this work we combine SpMV preprocessing and the subsequent per-row processing on the GPU by leveraging the novel “Work Graphs” GPU programming model—initially designed for graphics applications—for dynamic on-device self-scheduling. Work Graphs allow for fine-grain dataflow execution of individual workgroups using emerging hardware and firmware support. As soon as preprocessing has generated sufficient work, workgroups of individual processing kernels are self-scheduled and executed, interleaved with those of other kernels. This improves cache locality and eliminates host interaction altogether. Across a suite of 59 sparse matrices, the best of various novel Work Graphs SpMV implementations outperforms state-of-the-art rocSPARSE “LRB” for a single SpMV by up to 7.19 × (mean: 3.35 ×, SD: 1.89). Furthermore, it achieves much more stable performance across various sparsity patterns than the rocSPARSE CSR-General algorithm, and even beats the advanced rocSPARSE CSR-Adaptive algorithm for up to 92 consecutive SpMV calculations. In addition, compared to rocSPARSE LRB, it reduces code complexity by 75%. Its memory footprint for supporting data structures is a fixed ∼ 25 MiB independent of matrix size, compared to rocSPARSE LRB’s data structures that scale with matrix size to hundreds of megabytes. Overall, this work showcases the performance potential of emerging dynamic on-device scheduling techniques for GPU compute applications.</abstract>
    <parentTitle language="eng">ISCA ´25: Proceedings of the 52nd Annual International Symposium on Computer Architecture</parentTitle>
    <identifier type="isbn">979-8-4007-1261-6</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-58868</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1145/3695053.3731060</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">52nd Annual International Symposium on Computer Architecture (ISCA 2025), Tokyo (Japan), 21.-25.06.2025</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-NC-SA 4.0</licence>
    <author>
      <first_name>Fabian</first_name>
      <last_name>Wildgrube</last_name>
    </author>
    <author>
      <first_name>Pete</first_name>
      <last_name>Ehrett</last_name>
    </author>
    <author>
      <first_name>Paul</first_name>
      <last_name>Trojahn</last_name>
    </author>
    <author>
      <first_name>Richard</first_name>
      <last_name>Membarth</last_name>
    </author>
    <author>
      <first_name>Bradford</first_name>
      <last_name>Beckmann</last_name>
    </author>
    <author>
      <first_name>Dominik</first_name>
      <last_name>Baumeister</last_name>
    </author>
    <author>
      <first_name>Matthäus</first_name>
      <last_name>Chajdas</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43674">Membarth, Richard</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5886/3695053.3731060.pdf</file>
  </doc>
  <doc>
    <id>6005</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-17</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Federated Learning via Decentralized Dataset Distillation in Resource Constrained Edge Environments</title>
    <abstract language="eng">In federated learning, all networked clients contribute to the model training cooperatively. However, with model sizes increasing, even sharing the trained partial models often leads to severe communication bottlenecks in underlying networks, especially when communicated iteratively. In this paper, we introduce a federated learning framework FedD3 requiring only one-shot communication by integrating dataset distillation instances. Instead of sharing model updates in other federated learning approaches, FedD3 allows the connected clients to distill the local datasets independently, and then aggregates those decentralized distilled datasets (e.g. a few unrecognizable images) from networks for model training. Our experimental results show that FedD3 significantly outperforms other federated learning frameworks in terms of needed communication volumes, while it provides the additional benefit to be able to balance the trade-off between accuracy and communication cost, depending on usage scenario or target dataset. For instance, for training an AlexNet model on CIFAR-10 with 10 clients under non-independent and identically distributed (Non-IID) setting, FedD3 can either increase the accuracy by over 71% with a similar communication volume, or save 98% of communication volume, while reaching the same accuracy, compared to other one-shot federated learning approaches.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-60054</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2208.11311</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/3936</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Rui</first_name>
      <last_name>Song</last_name>
    </author>
    <author>
      <first_name>Dai</first_name>
      <last_name>Liu</last_name>
    </author>
    <author>
      <first_name>Dave Zhenyu</first_name>
      <last_name>Chen</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Carsten</first_name>
      <last_name>Trinitis</last_name>
    </author>
    <author>
      <first_name>Martin</first_name>
      <last_name>Schulz</last_name>
    </author>
    <author>
      <first_name>Alois</first_name>
      <last_name>Knoll</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/6005/2208.11311v3-1.pdf</file>
  </doc>
  <doc>
    <id>5988</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">TWICE Dataset: Digital Twin of Test Scenarios in a Controlled Environment</title>
    <abstract language="eng">Ensuring the safe and reliable operation of autonomous vehicles under adverse weather remains a significant challenge. To address this, we have developed a comprehensive dataset composed of sensor data acquired in a real test track and reproduced in the laboratory for the same test scenarios. The provided dataset includes camera, radar, LiDAR, inertial measurement unit (IMU), and GPS data recorded under adverse weather conditions (rainy, night-time, and snowy conditions). We recorded test scenarios using objects of interest such as car, cyclist, truck and pedestrian -- some of which are inspired by EURONCAP (European New Car Assessment Programme). The sensor data generated in the laboratory is acquired by the execution of simulation-based tests in hardware-in-the-loop environment with the digital twin of each real test scenario. The dataset contains more than 2 hours of recording, which totals more than 280GB of data. Therefore, it is a valuable resource for researchers in the field of autonomous vehicles to test and improve their algorithms in adverse weather conditions, as well as explore the simulation-to-reality gap.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59887</identifier>
    <enrichment key="THI_DownloadUrl">https://twicedataset.github.io/site/</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5892</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2310.03895</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-NC-SA 4.0</licence>
    <author>
      <first_name>Leonardo</first_name>
      <last_name>Novicki Neto</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
    </author>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Parente Ribeiro</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Icking</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5988/2310.03895v1.pdf</file>
  </doc>
  <doc>
    <id>5987</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-13</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of Realistic Synthetic Raw Radar Data for Automated Driving Applications using Generative Adversarial Networks</title>
    <abstract language="eng">The main approaches for simulating FMCW radar are based on ray tracing, which is usually computationally intensive and do not account for background noise. This work proposes a faster method for FMCW radar simulation capable of generating synthetic raw radar data using generative adversarial networks (GAN). The code and pre-trained weights are open-source and available on GitHub. This method generates 16 simultaneous chirps, which allows the generated data to be used for the further development of algorithms for processing radar data (filtering and clustering). This can increase the potential for data augmentation, e.g., by generating data in non-existent or safety-critical scenarios that are not reproducible in real life. In this work, the GAN was trained with radar measurements of a motorcycle and used to generate synthetic raw radar data of a motorcycle traveling in a straight line. For generating this data, the distance of the motorcycle and Gaussian noise are used as input to the neural network. The synthetic generated radar chirps were evaluated using the Frechet Inception Distance (FID). Then, the Range-Azimuth (RA) map is calculated twice: first, based on synthetic data using this GAN and, second, based on real data. Based on these RA maps, an algorithm with adaptive threshold and edge detection is used for object detection. The results have shown that the data is realistic in terms of coherent radar reflections of the motorcycle and background noise based on the comparison of chirps, the RA maps and the object detection results. Thus, the proposed method in this work has shown to minimize the simulation-to-reality gap for the generation of radar data.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59872</identifier>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2308.02632</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-NC-SA 4.0</licence>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Fidelis</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Herick Y. S.</first_name>
      <last_name>Ribeiro</last_name>
    </author>
    <author>
      <first_name>Pietro</first_name>
      <last_name>Campos</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Icking</last_name>
    </author>
    <author>
      <first_name>Lester</first_name>
      <last_name>Faria</last_name>
    </author>
    <author>
      <first_name>Torsten</first_name>
      <last_name>Schön</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <collection role="persons" number="41270">Schön, Torsten</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5987/2308.02632v2.pdf</file>
  </doc>
  <doc>
    <id>5986</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Investigation of Factors Influencing the End-of-Line Partial Discharge Testing of Electrical Machines</title>
    <parentTitle language="eng">2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)</parentTitle>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/WEMDCD61816.2025.11014117</enrichment>
    <enrichment key="THI_conferenceName">7th IEEE Workshop on Electrical Machine Design, Control and Diagnostics (WEMDCD 2025), Valletta (Malta), 09.-10.04.2025</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Rauscher</last_name>
    </author>
    <author>
      <first_name>Peer</first_name>
      <last_name>Stenzel</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
  </doc>
  <doc>
    <id>5980</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Gradient Derivation for Learnable Parameters in Graph Attention Networks</title>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2304.10939</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Marion</first_name>
      <last_name>Neumeier</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Tollkühn</last_name>
    </author>
    <author>
      <first_name>Sebastian</first_name>
      <last_name>Dorn</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
  </doc>
  <doc>
    <id>5975</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Sigmoid-Based Method for Longitudinal Crash Pulse Prediction in Intelligent Vehicles</title>
    <parentTitle language="eng">2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</parentTitle>
    <identifier type="isbn">979-8-3503-8727-8</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</enrichment>
    <enrichment key="conference_place">Ahmedabad, India</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICVES61986.2024.10927899</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">18th IEEE International Conference on Vehicular Electronics and Safety (ICVES 2024), Ahmedabad (India), 16.-19.12.2024</enrichment>
    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Inderst</last_name>
    </author>
    <author>
      <first_name>Redjon</first_name>
      <last_name>Xhiku</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Lugner</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26848">Brandmeier, Thomas</collection>
    <collection role="persons" number="27080">Lugner, Robert</collection>
  </doc>
  <doc>
    <id>5974</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>423</pageFirst>
    <pageLast>430</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Adaptive Message Prioritization: How to Prioritize VRU Awareness Messages in a Congested V2X Network</title>
    <parentTitle language="eng">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-0592-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</enrichment>
    <enrichment key="conference_place">Edmonton, AB, Canada</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC58415.2024.10919704</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">27th International Conference on Intelligent Transportation Systems (ITSC 2024), Edmonton (Canada), 24.-27.09.24</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Silas</first_name>
      <last_name>Lobo</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Facchi</last_name>
    </author>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26695">Facchi, Christian</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
  </doc>
  <doc>
    <id>5973</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2021</pageFirst>
    <pageLast>2026</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploring Synthetic Radar Data and Deep Learning for Road User Classification in Autonomous Vehicles</title>
    <parentTitle language="eng">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-0592-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</enrichment>
    <enrichment key="conference_place">Edmonton, AB, Canada</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC58415.2024.10919565</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">27th International Conference on Intelligent Transportation Systems (ITSC 2024), Edmonton (Canada), 24.-27.09.24</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Diogo</first_name>
      <last_name>Wachtel Granado</last_name>
    </author>
    <author>
      <first_name>Sinan</first_name>
      <last_name>Tasabat</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Rothmeier</last_name>
    </author>
    <author>
      <first_name>Letícia</first_name>
      <last_name>Cristófoli Duarte Silva</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>5972</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4034</pageFirst>
    <pageLast>4039</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Navigating on Adverse Weather: Enhancing LiDAR-Based Detection with the DBSPRY Dataset</title>
    <parentTitle language="eng">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-0592-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</enrichment>
    <enrichment key="conference_place">Edmonton, AB, Canada</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC58415.2024.10920224</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">27th International Conference on Intelligent Transportation Systems (ITSC 2024), Edmonton (Canada), 24.-27.09.24</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Diogo</first_name>
      <last_name>Wachtel Granado</last_name>
    </author>
    <author>
      <first_name>Heitor Derder</first_name>
      <last_name>Trevisol</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Rothmeier</last_name>
    </author>
    <author>
      <first_name>Bogdan Tomoyuki</first_name>
      <last_name>Nassu</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>5971</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3997</pageFirst>
    <pageLast>4004</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Consideration of Safety Aspects in a Camera-Aided, Radar-Based Free Space Detection</title>
    <parentTitle language="eng">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-0592-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</enrichment>
    <enrichment key="conference_place">Edmonton, AB, Canada</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC58415.2024.10920194</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_conferenceName">27th International Conference on Intelligent Transportation Systems (ITSC 2024), Edmonton (Canada), 24.-27.09.24</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Tim</first_name>
      <last_name>Brühl</last_name>
    </author>
    <author>
      <first_name>Aravind Kumar</first_name>
      <last_name>Shanmuganathan</last_name>
    </author>
    <author>
      <first_name>Lukas</first_name>
      <last_name>Ewecker</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Schwager</last_name>
    </author>
    <author>
      <first_name>Tin Stribor</first_name>
      <last_name>Sohn</last_name>
    </author>
    <author>
      <first_name>Ondrej</first_name>
      <last_name>Vaculin</last_name>
    </author>
    <author>
      <first_name>Sören</first_name>
      <last_name>Hohmann</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19318">CARISSMA Institute of Safety in Future Mobility (C-ISAFE)</collection>
    <collection role="persons" number="26510">Vaculin, Ondrej</collection>
  </doc>
  <doc>
    <id>5970</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1410</pageFirst>
    <pageLast>1417</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-11</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Out of the Box: Weather Augmentation for Enhanced Detection in Bad Visibility Conditions</title>
    <parentTitle language="eng">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">979-8-3315-0592-9</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC)</enrichment>
    <enrichment key="conference_place">Edmonton, AB, Canada</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC58415.2024.10919529</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_conferenceName">27th International Conference on Intelligent Transportation Systems (ITSC 2024), Edmonton (Canada), 24.-27.09.24</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Rothmeier</last_name>
    </author>
    <author>
      <first_name>Mayara</first_name>
      <last_name>Dal Vesco Hoger</last_name>
    </author>
    <author>
      <first_name>Bogdan Tomoyuki</first_name>
      <last_name>Nassu</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Alois</first_name>
      <last_name>Knoll</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>5965</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>19</pageNumber>
    <edition/>
    <issue>127</issue>
    <volume>2025</volume>
    <articleNumber>116924</articleNumber>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Apparent vs. true battery aging: Impact of various load characteristics on accelerated aging tests</title>
    <abstract language="eng">Vehicle applications involve highly dynamic and variable operating conditions. These result from changing driving profiles and diverse driving behaviors. To accelerate battery aging tests, experiments are often based on constant load cycles. While efficient, this approach distorts the natural degradation behavior of the cells. This study compares cell aging under real-world and laboratory load conditions. For this purpose, we compare realistic driving profiles measured in a test vehicle in the field with synthetic constant power cycles. Average discharge power and charging sequences are kept identical for comparability. Results show that constant power cycles cause stronger cell aging than dynamic loads. Differential voltage analysis indicates more lithium inventory loss and active material loss at the negative electrode under static loads. Static loading also leads to strong inhomogeneity in the negative electrode and altered charge transfer and diffusion kinetics, as shown by impedance data. After 15 months of rest, the cells recovered up to 52  of lost capacity and 66  of the resistance increase. Impedance assessment and differential voltage analysis confirm the recovery and rehomogenization of the negative electrode. These findings show that conventional accelerated tests are strongly influenced by relaxation and load dynamics. As a result, their applicability to specific use cases, such as automotive applications, is strongly limited . Therefore, andraditional test strategies require reconsideration and redesign.</abstract>
    <parentTitle language="eng">Journal of Energy Storage</parentTitle>
    <identifier type="issn">2352-1538</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-59653</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://www.elsevier.com/tdm/userlicense/1.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorIdentifierOrcid_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorIdentifierOrcid_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PersonAuthorIdentifierOrcid_7,PersonAuthorFirstName_8,PersonAuthorLastName_8,PersonAuthorIdentifierOrcid_8,PersonAuthorFirstName_9,PersonAuthorLastName_9,PersonAuthorIdentifierOrcid_9,PersonAuthorFirstName_10,PersonAuthorLastName_10,PersonAuthorIdentifierOrcid_10,PersonAuthorFirstName_11,PersonAuthorLastName_11,PersonAuthorIdentifierOrcid_11,PublisherName,TitleMain_1,Language,TitleParent_1,ArticleNumber,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.est.2025.116924</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-NC 4.0</licence>
    <author>
      <first_name>Markus</first_name>
      <last_name>Schreiber</last_name>
    </author>
    <author>
      <first_name>Yilei</first_name>
      <last_name>Lin</last_name>
    </author>
    <author>
      <first_name>Alessandro</first_name>
      <last_name>Sommer</last_name>
    </author>
    <author>
      <first_name>Nikolaos</first_name>
      <last_name>Wassiliadis</last_name>
    </author>
    <author>
      <first_name>Pablo</first_name>
      <last_name>Morales Torricos</last_name>
    </author>
    <author>
      <first_name>Marcel</first_name>
      <last_name>Rogge</last_name>
    </author>
    <author>
      <first_name>Meinert</first_name>
      <last_name>Lewerenz</last_name>
    </author>
    <author>
      <first_name>Cristina</first_name>
      <last_name>Grosu</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Endisch</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Jossen</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Lienkamp</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26571">Endisch, Christian</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5965/1-s2.0-S2352152X25016378-main.pdf</file>
  </doc>
  <doc>
    <id>5964</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>5</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Decentralized Fusion of 3D Extended Object Tracking based on a B-Spline Shape Model</title>
    <abstract language="eng">Extended Object Tracking (EOT) exploits the high resolution of modern sensors for detailed environmental perception. Combined with decentralized fusion, it contributes to a more scalable and robust perception system. This paper investigates the decentralized fusion of 3D EOT using a B-spline curve based model. The spline curve is used to represent the side-view profile, which is then extruded with a width to form a 3D shape. We use covariance intersection (CI) for the decentralized fusion and discuss the challenge of applying it to EOT. We further evaluate the tracking result of the decentralized fusion with simulated and real datasets of traffic scenarios. We show that the CI-based fusion can significantly improve the tracking performance for sensors with unfavorable perspective.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59648</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2504.18708</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Longfei</first_name>
      <last_name>Han</last_name>
    </author>
    <author>
      <first_name>Klaus</first_name>
      <last_name>Kefferpütz</last_name>
    </author>
    <author>
      <first_name>Jürgen</first_name>
      <last_name>Beyerer</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="49574">Kefferpütz, Klaus</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5964/2504.18708v1.pdf</file>
  </doc>
  <doc>
    <id>5963</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>39</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-10</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Systematic Literature Review on Vehicular Collaborative Perception – A Computer Vision Perspective</title>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2504.04631</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/6470</enrichment>
    <author>
      <first_name>Lei</first_name>
      <last_name>Wan</last_name>
    </author>
    <author>
      <first_name>Jianxin</first_name>
      <last_name>Zhao</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Wiedholz</last_name>
    </author>
    <author>
      <first_name>Manuel</first_name>
      <last_name>Bied</last_name>
    </author>
    <author>
      <first_name>Mateus</first_name>
      <last_name>Martinez de Lucena</last_name>
    </author>
    <author>
      <first_name>Abhishek Dinkar</first_name>
      <last_name>Jagtap</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Festag</last_name>
    </author>
    <author>
      <first_name>Antônio</first_name>
      <last_name>Fröhlich</last_name>
    </author>
    <author>
      <first_name>Hannan Ejaz</first_name>
      <last_name>Keen</last_name>
    </author>
    <author>
      <first_name>Alexey</first_name>
      <last_name>Vinel</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19319">CARISSMA Institute of Electric, Connected and Secure Mobility (C-ECOS)</collection>
    <collection role="persons" number="25591">Festag, Andreas</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
  </doc>
  <doc>
    <id>5958</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>XII, 136</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace>Ingolstadt</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-06</completedDate>
    <publishedDate>2021-06-15</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Klassifizierung unterschiedlicher E-Scooter Fahrerprofile mit Hilfe eines selbstentwickelten Messfahrzeuges</title>
    <abstract language="deu">Die Zahl der E-Scooter Fahrer nimmt weltweit zu. Sowohl Sharing-Angebote als auch E-Scooter zum Kauf für den privaten Gebrauch gewinnen an Beliebtheit. Diese neue Art der Fortbewegung muss bei der Entwicklung moderner Fahrerassistenzsysteme gesondert berücksichtigt werden, da noch nicht belegt ist, ob sie die selbe Dynamik wie bestehende Verkehrsteilnehmer aufweist. Moderne Fahrzeugsysteme werden bereits früh in der Entwicklung durch Simulationsumgebungen unterstützt, abgesichert und auf Wirksamkeit geprüft. Solche Simulationen sollten dann Modelle beinhalten, die E-Scooter Fahrer inklusive ihres physikalischen Limits der Manövrierbarkeit abbilden und durch Realdaten parametrisiert sowie kalibriert werden. Dazu wird ein E-Scooter mit Sensorik und einem Datenlogger ausgestattet. Eine Probandenstudie im Realfahrversuch dient der Datengenerierung. Anhand dieser Studie wird das Fahrerverhalten erforscht und Empfehlungen zur Parametrierung einer Simulationsumgebung aufgestellt. Anhand der, durch die 36 Probanden gewonnen, Daten werden die Parameter Ausrollverzögerung, Reaktionszeit, mittlere Bremsverzögerung, maximale Bremsverzögerung, maximaler Rollwinkel, maximaler Lenkwinkel und maximale Querbeschleunigung kategorisiert und ausgewertet. Die Auswertung befasst sich unter anderem mit Unterschieden zwischen den Geschwindigkeitsstufen, Erfahrungsstufen mit anderen Verkehrsmitteln, sowie dem Geschlecht, Alter, Gewicht und Größe des Fahrers. Dadurch können signifikante Zusammenhänge aufgedeckt werden und in manchen Fällen Abhängigkeiten zur genaueren Kalibrierung der Simulation genutzt werden. Die Reaktionszeit weist eine direkte Proportionalität zur Fahrgeschwindigkeit auf. Dieser Zusammenhang kann zur Parametrisierung genutzt werden. Für die maximale Bremsverzögerung stellen Regressionsgleichungen mit der Prädikatorvariable Fahrergewicht die beste Lösung zur Kalibrierung dar. Die mittlere Bremsverzögerung hingegen wird in Abhängigkeit des Geschlechts und der Geschwindigkeit parametrisiert. Eine geschwindigkeitsabhängige Parametrisierung kann ebenso beim maximalen Rollwinkel angewendet werden. Die beiden Variablen maximaler Lenkwinkel und maximale Querbeschleunigung weisen in der Auswertung keine, für die Parametrisierung relevanten, signifikanten Zusammenhänge auf. Daher wird hier eine Kalibrierung anhand der berechneten Mittelwerte empfohlen. Im Nachgang der Arbeit sollte das Versuchsfahrzeug um weitere Sensorik erweitert und die Robustheit erhöht werden. Auch sollten die Ergebnisse dieser Arbeit in weiteren Studien validiert und verfeinert werden. Dazu sind größere Stichprobenumfänge und homogenere Verteilungen in der Stichprobe nötig.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59584</identifier>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Urheberrechtsschutz</licence>
    <author>
      <first_name>Tetmar</first_name>
      <last_name>von dem Bussche-Hünnefeld</last_name>
    </author>
    <advisor>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </advisor>
    <advisor>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </advisor>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="degree_programme" number="19348">Automatisiertes Fahren und Fahrzeugsicherheit (M. Eng.)</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <thesisGrantor>Technische Hochschule Ingolstadt</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-haw/files/5958/Masterarbeit_E_Scooter_Bussche_Huennefeld.pdf</file>
  </doc>
  <doc>
    <id>5957</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2025-06-06</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Uncertainty-Aware Hybrid Machine Learning in Virtual Sensors for Vehicle Sideslip Angle Estimation</title>
    <abstract language="eng">Precise vehicle state estimation is crucial for safe and reliable autonomous driving. The number of measurable states and their precision offered by the onboard vehicle sensor system are often constrained by cost. For instance, measuring critical quantities such as the Vehicle Sideslip Angle (VSA) poses significant commercial challenges using current optical sensors. This paper addresses these limitations by focusing on the development of high-performance virtual sensors to enhance vehicle state estimation for active safety. The proposed Uncertainty-Aware Hybrid Learning (UAHL) architecture integrates a machine learning model with vehicle motion models to estimate VSA directly from onboard sensor data. A key aspect of the UAHL architecture is its focus on uncertainty quantification for individual model estimates and hybrid fusion. These mechanisms enable the dynamic weighting of uncertainty-aware predictions from machine learning and vehicle motion models to produce accurate and reliable hybrid VSA estimates. This work also presents a novel dataset named Real-world Vehicle State Estimation Dataset (ReV-StED), comprising synchronized measurements from advanced vehicle dynamic sensors. The experimental results demonstrate the superior performance of the proposed method for VSA estimation, highlighting UAHL as a promising architecture for advancing virtual sensors and enhancing active safety in autonomous vehicles.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-59578</identifier>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2504.06105</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Abinav</first_name>
      <last_name>Kalyanasundaram</last_name>
    </author>
    <author>
      <first_name>Karthikeyan</first_name>
      <last_name>Chandra Sekaran</last_name>
    </author>
    <author>
      <first_name>Philipp</first_name>
      <last_name>Stäuber</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Lange</last_name>
    </author>
    <author>
      <first_name>Wolfgang</first_name>
      <last_name>Utschick</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Botsch</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26835">Botsch, Michael</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5957/2504.06105v1.pdf</file>
  </doc>
  <doc>
    <id>5942</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>26</pageNumber>
    <edition/>
    <issue>11</issue>
    <volume>25</volume>
    <articleNumber>3422</articleNumber>
    <type>article</type>
    <publisherName>MDPI</publisherName>
    <publisherPlace>Basel</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-06-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Infra-3DRC-FusionNet: Deep Fusion of Roadside Mounted RGB Mono Camera and Three-Dimensional Automotive Radar for Traffic User Detection</title>
    <abstract language="eng">Mono RGB cameras and automotive radar sensors provide a complementary information set that makes them excellent candidates for sensor data fusion to obtain robust traffic user detection. This has been widely used in the vehicle domain and recently introduced in roadside-mounted smart infrastructure-based road user detection. However, the performance of the most commonly used late fusion methods often degrades when the camera fails to detect road users in adverse environmental conditions. The solution is to fuse the data using deep neural networks at the early stage of the fusion pipeline to use the complete data provided by both sensors. Research has been carried out in this area, but is limited to vehicle-based sensor setups. Hence, this work proposes a novel deep neural network to jointly fuse RGB mono-camera images and 3D automotive radar point cloud data to obtain enhanced traffic user detection for the roadside-mounted smart infrastructure setup. Projected radar points are first used to generate anchors in image regions with a high likelihood of road users, including areas not visible to the camera. These anchors guide the prediction of 2D bounding boxes, object categories, and confidence scores. Valid detections are then used to segment radar points by instance, and the results are post-processed to produce final road user detections in the ground plane. The trained model is evaluated for different light and weather conditions using ground truth data from a lidar sensor. It provides a precision of 92%, recall of 78%, and F1-score of 85%. The proposed deep fusion methodology has 33%, 6%, and 21% absolute improvement in precision, recall, and F1-score, respectively, compared to object-level spatial fusion output.</abstract>
    <parentTitle language="eng">Sensors</parentTitle>
    <identifier type="issn">1424-8220</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-59421</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorIdentifierOrcid_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PublisherName,TitleMain_1,Language,TitleAbstract_1,TitleParent_1,ArticleNumber,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.3390/s25113422</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Shiva</first_name>
      <last_name>Agrawal</last_name>
    </author>
    <author>
      <first_name>Savankumar</first_name>
      <last_name>Bhanderi</last_name>
    </author>
    <author>
      <first_name>Gordon</first_name>
      <last_name>Elger</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19320">Institut für Innovative Mobilität (IIMo)</collection>
    <collection role="persons" number="26589">Elger, Gordon</collection>
    <collection role="institutes" number="19569">Fraunhofer-Anwendungszentrum "Vernetzte Mobilität und Infrastruktur"</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5942/sensors-25-03422.pdf</file>
  </doc>
  <doc>
    <id>5892</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>152</pageFirst>
    <pageLast>170</pageLast>
    <pageNumber/>
    <edition/>
    <issue>2</issue>
    <volume>19</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Inderscience</publisherName>
    <publisherPlace>Genf</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2025-05-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">TWICE dataset: digital twin of test scenarios in a controlled environment</title>
    <parentTitle language="eng">International Journal of Vehicle Systems Modelling and Testing (IJVSMT)</parentTitle>
    <identifier type="issn">1745-6436</identifier>
    <identifier type="issn">1745-6444</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorFirstName_6,PersonAuthorLastName_6,PersonAuthorFirstName_7,PersonAuthorLastName_7,PublisherName,TitleMain_1,Language,TitleParent_1,Issue,Volume,CompletedYear,IdentifierIssn</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1504/IJVSMT.2025.147353</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/5988</enrichment>
    <author>
      <first_name>Leonardo</first_name>
      <last_name>Novicki Neto</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
    </author>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Icking</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Parente Ribeiro</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>5465</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>51</pageFirst>
    <pageLast>61</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>37</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Gesellschaft für Informatik</publisherName>
    <publisherPlace>Bonn</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-12-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploration of Efficient Computation for Trajectory Planning via Fixed-Point Arithmetic</title>
    <parentTitle language="deu">PARS-Mitteilungen 2025</parentTitle>
    <identifier type="issn">0177-0454</identifier>
    <identifier type="url">https://dl.gi.de/items/6b2c0546-1081-40ab-b97b-052b60b8374f</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_conferenceName">30. PARS-Workshop, Ingolstadt (Germany), 25.-26.09.2024</enrichment>
    <author>
      <first_name>Wenguang</first_name>
      <last_name>Xu</last_name>
    </author>
    <author>
      <first_name>Richard</first_name>
      <last_name>Membarth</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43674">Membarth, Richard</collection>
  </doc>
  <doc>
    <id>5466</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>63</pageFirst>
    <pageLast>70</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume>37</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Gesellschaft für Informatik</publisherName>
    <publisherPlace>Bonn</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-12-12</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Exploiting Data Redundancy in Im2Col Convolutions</title>
    <parentTitle language="deu">PARS-Mitteilungen 2025</parentTitle>
    <identifier type="issn">0177-0454</identifier>
    <identifier type="url">https://dl.gi.de/items/156c727e-ec90-4809-9ce6-ee27cc89eeb0</identifier>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_conferenceName">30. PARS-Workshop, Ingolstadt (Germany), 25.-26.09.2024</enrichment>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Pfaller</last_name>
    </author>
    <author>
      <first_name>Richard</first_name>
      <last_name>Membarth</last_name>
    </author>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="persons" number="43674">Membarth, Richard</collection>
  </doc>
</export-example>
