<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <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>5882</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>2025-04-30</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Generation of realistic traffic scenarios for virtual and real test drives based on a hybrid machine learning framework</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="THI_relatedIdentifier">https://doi.org/10.1109/ICVES61986.2024.10927934</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</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>Peter</first_name>
      <last_name>Riegl</last_name>
    </author>
    <author>
      <first_name>Karthikeyan</first_name>
      <last_name>Chandra Sekaran</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="persons" number="26835">Botsch, Michael</collection>
  </doc>
  <doc>
    <id>3814</id>
    <completedYear/>
    <publishedYear>2023</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>2023-08-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Metric Learning Based Class Specific Experts for Open-Set Recognition of Traffic Participants in Urban Areas Using Infrastructure Sensors</title>
    <parentTitle language="eng">IEEE IV 2023 Symposium Proceedings</parentTitle>
    <identifier type="isbn">979-8-3503-4691-6</identifier>
    <identifier type="issn">2642-7214</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV55152.2023.10186527</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_conferenceName">2023 IEEE Intelligent Vehicles Symposium (IV), Anchorage (USA), 04.-07.06.2023</enrichment>
    <author>
      <first_name>Karthikeyan</first_name>
      <last_name>Chandra Sekaran</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>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Measurement</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Head</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Urban areas</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Object detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Thermal sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cameras</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor systems</value>
    </subject>
    <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>4037</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>333</pageFirst>
    <pageLast>336</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2023-10-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Data Collection and Safety Use Cases in Smart Infrastructures</title>
    <parentTitle language="eng">15th International ACM Conference on Automotive User Interfaces: Adjunct Conference Proceedings</parentTitle>
    <identifier type="isbn">979-8-4007-0112-2</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1145/3581961.3609858</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_conferenceName">AutomotiveUI '23: 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, Ingolstadt (Germany), 18.-21.09.2023</enrichment>
    <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>Lakshman</first_name>
      <last_name>Balasubramanian</last_name>
    </author>
    <author>
      <first_name>Alberto</first_name>
      <last_name>Flores Fernandez</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Geisler</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Gudera</last_name>
    </author>
    <author>
      <first_name>Mauricio Rene</first_name>
      <last_name>Morales Gomez</last_name>
    </author>
    <author>
      <first_name>Peter</first_name>
      <last_name>Riegl</last_name>
    </author>
    <author>
      <first_name>Eduardo</first_name>
      <last_name>Sánchez Morales</last_name>
    </author>
    <author>
      <first_name>Karthikeyan</first_name>
      <last_name>Chandra Sekaran</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Weinzierl</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>smart infrastructure</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>infrared camera</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>LiDAR sensor</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sensor data fusion</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>safety use case</value>
    </subject>
    <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>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>4792</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1643</pageFirst>
    <pageLast>1650</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2024-06-14</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Open-Set Object Detection for the Identification and Localization of Dissimilar Novel Classes by means of Infrastructure Sensors</title>
    <parentTitle language="eng">2024 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">979-8-3503-4881-1</identifier>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_conferenceName">35th IEEE Intelligent Vehicles Symposium (IV 2024), Jeju Island (South Korea), 02.-05.06.2024</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV55156.2024.10588872</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>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>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>
</export-example>
