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  <doc>
    <id>3763</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>16</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>Research Square</publisherName>
    <publisherPlace>Durham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-07-03</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">Research Square</parentTitle>
    <identifier type="issn">2693-5015</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-37632</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.21203/rs.3.rs-3004299/v1</enrichment>
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    <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>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>virtual vehicle models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>crash tests</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>crash simulations</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>surrogate model</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>crash-detection algorithm</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>reinforcement Learning</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/3763/Afraj-Vehicle_Crash_Simulation_Models.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>
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    <enrichment key="local_crossrefLicence">https://creativecommons.org/licenses/by/4.0/</enrichment>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.36227/techrxiv.175099959.99453472/v1</enrichment>
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    <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>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>
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    <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>
</export-example>
