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    <id>1315</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue>831</issue>
    <volume>2020</volume>
    <articleNumber>012019</articleNumber>
    <type>article</type>
    <publisherName>IOP</publisherName>
    <publisherPlace>Bristol</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">FEM-based methodology for crash severity estimation in frontal crash scenarios</title>
    <abstract language="eng">With the technological development of forward-looking sensors, researchers are exploring their use not only for advanced driver assistance systems but also to gain important pre-crash information. Based on this pre-crash information, if the occupant motion inside the vehicle structure can be predicted for the oncoming crash scenario, then an optimal restraint strategy can be planned before the crash. This paper introduces a two-step FEM simulation based methodology for predicting the occupant severity in head-on crash scenarios. In the first step, we simulate the vehicle level model with different impact positions and relative approach angles. The results of these simulations, linear velocities in the longitudinal and lateral direction and angular velocities (roll, pitch, and yaw) during in-crash phase are the loading conditions for next simulation step (occupant level). This step simulates the motion of the driver in different crash scenarios. In this paper, we investigate the head, neck, and chest injury risks from vehicle-to-vehicle crash both traveling at 50 kilometers per hour. Prediction of the head injury criterion, identifying the cases where additional deployment of side-airbags and discussion of injury criteria with contour plots are the main outcome of this paper.</abstract>
    <parentTitle language="eng">IOP Conference Series: Materials Science and Engineering</parentTitle>
    <identifier type="issn">1757-899X</identifier>
    <identifier type="issn">1757-8981</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-13159</identifier>
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    <enrichment key="THI_conferenceName">7th International Conference on Mechanical, Automotive and Materials Engineering (CMAME 2019), Melbourne (Australia), 08.-10.12.2019</enrichment>
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    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Akshay</first_name>
      <last_name>Patel</last_name>
    </author>
    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</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="open_access" number="">open_access</collection>
    <collection role="institutes" number="19311">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="persons" number="26848">Brandmeier, Thomas</collection>
    <collection role="institutes" number="19574">CARISSMA</collection>
    <collection role="persons" number="27080">Lugner, Robert</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1315/Sequeira_2020_IOP.pdf</file>
  </doc>
  <doc>
    <id>1372</id>
    <completedYear/>
    <publishedYear>2019</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">LiDAR based prediction and contact based validation of crash parameters for a preemptive restraint strategy</title>
    <parentTitle language="eng">2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES)</parentTitle>
    <identifier type="isbn">978-1-7281-3473-4</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICVES.2019.8906354</enrichment>
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    <enrichment key="THI_conferenceName">2019 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Cairo (Egypt), 04.-06.09.2019</enrichment>
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    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</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>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>vehicle safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>integrated safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>predictive safety system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>contour estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>crash scenario prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>crash validation</value>
    </subject>
    <collection role="institutes" number="15984">Fakultät Elektrotechnik und Informatik</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>2738</id>
    <completedYear/>
    <publishedYear>2020</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>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A novel concept for validation of pre-crash perception sensor information using contact sensor</title>
    <parentTitle language="eng">2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)</parentTitle>
    <identifier type="isbn">978-1-7281-4149-7</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ITSC45102.2020.9294242</enrichment>
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    <enrichment key="THI_conferenceName">2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC), online, 20.-23.09.2020</enrichment>
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    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Manasi</first_name>
      <last_name>Surve</last_name>
    </author>
    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Contacts</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Accidents</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Resistance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Electron tubes</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pins</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser radar</value>
    </subject>
    <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>2739</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
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    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway (NJ)</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">LiDAR point cloud analysis for vehicle contour estimation using polynomial approximation and curvature breakdown</title>
    <parentTitle language="eng">2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall) Proceedings</parentTitle>
    <identifier type="issn">2577-2465</identifier>
    <identifier type="isbn">978-1-7281-9484-4</identifier>
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    <enrichment key="THI_conferenceName">2020 IEEE 92nd Vehicular Technology Conference (VTC2020-Fall), online, 04.-07.10.2020</enrichment>
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    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</last_name>
    </author>
    <author>
      <first_name>Manasi</first_name>
      <last_name>Surve</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Three-dimensional displays</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Laser radar</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Electric breakdown</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Shape</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vehicles</value>
    </subject>
    <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>2741</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3925</pageFirst>
    <pageLast>3942</pageLast>
    <pageNumber/>
    <edition/>
    <issue>5</issue>
    <volume>23</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparison and Evaluation of Algorithms for LiDAR-Based Contour Estimation in Integrated Vehicle Safety</title>
    <parentTitle language="eng">IEEE Transactions on Intelligent Transportation Systems</parentTitle>
    <identifier type="issn">1558-0016</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/TITS.2020.3044753</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
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    <enrichment key="opus.source">publish</enrichment>
    <author>
      <first_name>David Michael</first_name>
      <last_name>Mothershed</last_name>
    </author>
    <author>
      <first_name>Robert</first_name>
      <last_name>Lugner</last_name>
    </author>
    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</last_name>
    </author>
    <author>
      <first_name>Gerald Joy</first_name>
      <last_name>Sequeira</last_name>
    </author>
    <author>
      <first_name>Kilian</first_name>
      <last_name>Schneider</last_name>
    </author>
    <author>
      <first_name>Thomas</first_name>
      <last_name>Brandmeier</last_name>
    </author>
    <author>
      <first_name>Valentin</first_name>
      <last_name>Soloiu</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>contour estimation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>curve similarity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>integrated safety</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>intelligent vehicles</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>inverse analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>light detection and ranging (LiDAR)</value>
    </subject>
    <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>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>
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    <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>
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    <id>6086</id>
    <completedYear/>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>27</pageNumber>
    <edition/>
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    <volume>12</volume>
    <articleNumber>17</articleNumber>
    <type>article</type>
    <publisherName>SpringerOpen</publisherName>
    <publisherPlace>Berlin</publisherPlace>
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    <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>
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    <author>
      <first_name>Shahabaz</first_name>
      <last_name>Afraj</last_name>
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