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    <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>
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    <title language="eng">Synthetic Extreme Weather for AI Training: Concept and Validation</title>
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    <title language="eng">An Open-Source High-Level Fusion Algorithm in ROS for Automated Driving Applications</title>
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      <first_name>Hector G.</first_name>
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      <first_name>Alan</first_name>
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      <first_name>Christian</first_name>
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      <first_name>Samantha</first_name>
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    <title language="eng">Dynamic Vehicle-in-the-Loop: A Novel Method for Testing Automated Driving Functions</title>
    <abstract language="eng">In automated driving functions (ADF) testing, novel methods have been developed to allow the combination of hardware and simulation to ensure safety in usage even at an early stage of development. This article proposes an architecture to integrate an entire test vehicle—denominated Dynamic Vehicle-in-the-Loop (DynViL)—in a virtual environment. This approach enables the interaction of a real vehicle with virtual traffic participants. The vehicle is physically tested on an empty track, but connected to the CARLA simulator, in which virtual driving scenarios are created. The simulated environment is transmitted to the vehicle driving function which controls the real vehicle in reaction to the virtual objects perceived in simulation. Furthermore, the performance of the DynViL in different test scenarios is evaluated. The results show that the reproducibility of the tests with the DynViL is satisfactory. Furthermore, the results indicate that the deviation between simulation and DynViL variates according to the vehicle speed and the complexity of the scenario. Based on the performance of the DynViL in comparison to simulation, the DynViL can be implemented as a complementary test method to be added to the transition between hardware in the loop (HiL) and proving ground. In this test method, erratic or unexpected behavior generated by the driving function and controllers can be detected in the real vehicle dynamics in a risk-free manner.</abstract>
    <parentTitle language="eng">SAE International Journal of Connected and Automated Vehicles</parentTitle>
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    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
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    <author>
      <first_name>Varun</first_name>
      <last_name>Sharma</last_name>
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      <first_name>Fabio</first_name>
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      <first_name>Christoph</first_name>
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      <first_name>Werner</first_name>
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      <value>automated emergency braking</value>
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      <first_name>Natanael</first_name>
      <last_name>Vitorino</last_name>
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      <first_name>Igor Monteiro</first_name>
      <last_name>Moraes</last_name>
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      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
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    <editor>
      <first_name>Thi Mai Trang</first_name>
      <last_name>Nguyen</last_name>
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    <author>
      <first_name>Bruno</first_name>
      <last_name>Malena</last_name>
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    <editor>
      <first_name>Rodrigo</first_name>
      <last_name>de Souza Couto</last_name>
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    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
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    <editor>
      <first_name>Marcelo Goncalves</first_name>
      <last_name>Rubinstein</last_name>
    </editor>
    <author>
      <first_name>Gustavo G.</first_name>
      <last_name>Albuquerque</last_name>
    </author>
    <author>
      <first_name>Tales</first_name>
      <last_name>Furlan</last_name>
    </author>
    <author>
      <first_name>Roberto S.</first_name>
      <last_name>Netto</last_name>
    </author>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
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    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
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    <completedDate>2025-02-13</completedDate>
    <publishedDate>--</publishedDate>
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    <title language="eng">Vehicle Dynamics Parameter Estimation Methodology for Virtual Automated Driving Testing</title>
    <parentTitle language="eng">2024 IEEE International Automated Vehicle Validation Conference (IAVVC), Proceedings</parentTitle>
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    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
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      <first_name>Yuri</first_name>
      <last_name>Poledna</last_name>
    </author>
    <author>
      <first_name>Mattias</first_name>
      <last_name>Hjort</last_name>
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    <author>
      <first_name>Sogol</first_name>
      <last_name>Kharrazi</last_name>
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      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
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    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
  </doc>
  <doc>
    <id>1585</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>229</pageFirst>
    <pageLast>232</pageLast>
    <pageNumber/>
    <edition/>
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    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
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    <completedDate>2022-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mixed Reality Environment for Testing Automated Vehicle and Pedestrian Interaction</title>
    <parentTitle language="eng">Adjunct Proceedings: 13th International ACM Conference on Automotive User Interfaces and Interactive Vehicular Applications</parentTitle>
    <identifier type="isbn">978-1-4503-8641-8</identifier>
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    <enrichment key="THI_conferenceName">AutomotiveUI ’21 Adjunct, Leeds (UK), 09.-14.09. 2021</enrichment>
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    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Jakob</first_name>
      <last_name>Peintner</last_name>
    </author>
    <author>
      <first_name>Georg</first_name>
      <last_name>Seifert</last_name>
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    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
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    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automated Driving Systems</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Test Procedures</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Vehicle-in-the-Loop</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>External Human-Machine Interfaces</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sensor stimulation</value>
    </subject>
    <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="persons" number="26627">Riener, Andreas</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
  </doc>
  <doc>
    <id>1586</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>2021</volume>
    <articleNumber>4641450</articleNumber>
    <type>article</type>
    <publisherName>Hindawi</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Actor-Critic Traction Control Based on Reinforcement Learning with Open-Loop Training</title>
    <abstract language="eng">The use of actor-critic algorithms can improve the controllers currently implemented in automotive applications. This method combines reinforcement learning (RL) and neural networks to achieve the possibility of controlling nonlinear systems with real-time capabilities. Actor-critic algorithms were already applied with success in different controllers including autonomous driving, antilock braking system (ABS), and electronic stability control (ESC). However, in the current researches, virtual environments are implemented for the training process instead of using real plants to obtain the datasets. This limitation is given by trial and error methods implemented for the training process, which generates considerable risks in case the controller directly acts on the real plant. In this way, the present research proposes and evaluates an open-loop training process, which permits the data acquisition without the control interaction and an open-loop training of the neural networks. The performance of the trained controllers is evaluated by a design of experiments (DOE) to understand how it is affected by the generated dataset. The results present a successful application of open-loop training architecture. The controller can maintain the slip ratio under adequate levels during maneuvers on different floors, including grounds that are not applied during the training process. The actor neural network is also able to identify the different floors and change the acceleration profile according to the characteristics of each ground.</abstract>
    <parentTitle language="eng">Modelling and Simulation in Engineering</parentTitle>
    <identifier type="issn">1687-5605</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-15862</identifier>
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    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1155/2021/4641450</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Thiago Antonio</first_name>
      <last_name>Fiorentin</last_name>
    </author>
    <author>
      <first_name>Harald</first_name>
      <last_name>Göllinger</last_name>
    </author>
    <collection role="institutes" number="15985">Fakultät Maschinenbau</collection>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19317">CARISSMA Institute of Automated Driving  (C-IAD)</collection>
    <collection role="persons" number="26844">Göllinger, Harald</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1586/ActorCriticTractionControlBasedonReinforcement.pdf</file>
  </doc>
  <doc>
    <id>1588</id>
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    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>605</pageFirst>
    <pageLast>608</pageLast>
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    <publisherName>ACM</publisherName>
    <publisherPlace>New York</publisherPlace>
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    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-02-28</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Mixed Reality Environment for Complex Scenario Testing</title>
    <parentTitle language="eng">Tagungsband Mensch &amp; Computer 2021</parentTitle>
    <identifier type="isbn">978-1-4503-8645-6</identifier>
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    <enrichment key="THI_conferenceName">MuC '21: Mensch und Computer 2021, Ingolstadt (Germany), 05.-08.09.2021</enrichment>
    <author>
      <first_name>Jakob</first_name>
      <last_name>Peintner</last_name>
    </author>
    <editor>
      <first_name>Stefan</first_name>
      <last_name>Schneegass</last_name>
    </editor>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <editor>
      <first_name>Bastian</first_name>
      <last_name>Pfleging</last_name>
    </editor>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <editor>
      <first_name>Dagmar</first_name>
      <last_name>Kern</last_name>
    </editor>
    <author>
      <first_name>Georg</first_name>
      <last_name>Seifert</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</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="persons" number="26627">Riener, Andreas</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
  </doc>
  <doc>
    <id>2717</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>903</pageFirst>
    <pageLast>908</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Piscataway</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-08-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">How Simulation based Test Methods will substitute the Proving Ground Testing?</title>
    <parentTitle language="eng">2022 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="isbn">978-1-6654-8821-1</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-27177</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/IV51971.2022.9827394</enrichment>
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    <enrichment key="THI_DownloadUrl">https://www.youtube.com/watch?v=QBfryCB6Dyk&amp;ab_channel=HCIG%40THI</enrichment>
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    <enrichment key="THI_conferenceName">2022 IEEE Intelligent Vehicles Symposium (IV), Aachen (Germany), 04.-09.06.2022</enrichment>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Georg</first_name>
      <last_name>Seifert</last_name>
    </author>
    <author>
      <first_name>Jakob</first_name>
      <last_name>Peintner</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
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    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wireless LAN</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Actuators</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Virtual environments</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Systems architecture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cameras</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Software</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Delays</value>
    </subject>
    <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="persons" number="26627">Riener, Andreas</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
  </doc>
  <doc>
    <id>2325</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>3443</pageFirst>
    <pageLast>3456</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>71</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>New York</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">MiRE, A Mixed Reality Environment for Testing of Automated Driving Functions</title>
    <parentTitle language="eng">IEEE Transactions on Vehicular Technology</parentTitle>
    <identifier type="issn">0018-9545</identifier>
    <identifier type="issn">1939-9359</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/TVT.2022.3160353</enrichment>
    <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>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Jakob</first_name>
      <last_name>Peintner</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Georg</first_name>
      <last_name>Seifert</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>human factors</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>road vehicle testing</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>vehicle safety</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>virtual reality</value>
    </subject>
    <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="persons" number="26627">Riener, Andreas</collection>
    <collection role="persons" number="24800">Huber, Werner</collection>
    <collection role="institutes" number="19588">Human-Computer Interaction Group (HCIG)</collection>
  </doc>
  <doc>
    <id>3151</id>
    <completedYear/>
    <publishedYear>2022</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>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Comparing Different Pedestrian Representations for Testing Automated&#13;
Driving Functions in Mixed Reality Environments</title>
    <parentTitle language="eng">Proceedings of the International Conference on Vehicle Electronics and Safety (ICVES 2022)</parentTitle>
    <identifier type="isbn">978-1-6654-7698-0</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1109/ICVES56941.2022.9986669</enrichment>
    <enrichment key="THI_conferenceName">2022 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Bogotá (Colombia), 14.-16.11.2022</enrichment>
    <enrichment key="THI_openaccess">nein</enrichment>
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    <author>
      <first_name>Jakob</first_name>
      <last_name>Peintner</last_name>
    </author>
    <author>
      <first_name>Maikol</first_name>
      <last_name>Funk Drechsler</last_name>
    </author>
    <author>
      <first_name>Carina</first_name>
      <last_name>Manger</last_name>
    </author>
    <author>
      <first_name>Georg</first_name>
      <last_name>Seifert</last_name>
    </author>
    <author>
      <first_name>Fabio</first_name>
      <last_name>Reway</last_name>
    </author>
    <author>
      <first_name>Werner</first_name>
      <last_name>Huber</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Riener</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Target tracking</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Roads</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mixed reality</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Virtual environments</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Motion capture</value>
    </subject>
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    <title language="eng">REHEARSE-3D: A Multi-Modal Emulated Rain Dataset for 3D Point Cloud De-Raining</title>
    <abstract language="eng">Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset (9.2 billion annotated points), and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D RADAR point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at the point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D RADAR point clouds. Our comprehensive study further evaluates the performance of various statistical and deep learning models, where SalsaNext and 3D-OutDet achieve above 94% IoU for raindrop detection.</abstract>
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