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Mixed Reality Environment for Complex Scenario Testing

Published: 13 September 2021 Publication History

Abstract

Driver assistance systems are currently evaluated using standardized test procedures as defined, for example, by Euro NCAP, but which only represent a narrow, idealized spectrum of real-life situations. Test conditions hardly take into account the variability that occur in everyday traffic – adverse weather conditions such as fog, rain, snow or darkness are typically not considered, nor are the resulting changes in, e.g., the behavior of pedestrians. In an emergency braking situation, the behavior of the VRU can also be directly affected by the interaction with the vehicle. For example, a pedestrian might be alarmed by an approaching car. This could elicit a very different response in the human that cannot be simulated in the dummy motion as defined in the standard test procedure. In order to create a more versatile and interactive simulation and test environment, we have developed the Mixed Reality Test Environment MiRE. The goal of Mixed Reality Test Environment MiRE is to enable testing of a broader range of scenarios, including interaction between VRUs and vehicles. During the demo, we will demonstrate MiRE live to interested visitors and show its full potential for future driving safety tests.

References

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Polona Caserman, Augusto Garcia-Agundez, Robert Konrad, Stefan Göbel, and Ralf Steinmetz. 2019. Real-time body tracking in virtual reality using a Vive tracker. Virtual Reality 23, 2 (2019), 155–168. https://doi.org/10.1007/s10055-018-0374-z
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Yu Chen, Shitao Chen, Tong Xiao, Songyi Zhang, Qian Hou, and Nanning Zheng. 2020. Mixed Test Environment-based Vehicle-in-the-loop Validation - A New Testing Approach for Autonomous Vehicles. In 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, Las Vegas, NV, USA, 1283–1289. https://doi.org/10.1109/IV47402.2020.9304658
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Selim Solmaz, Martin Rudigier, and Marlies Mischinger. 2020. A Vehicle-in-the-Loop Methodology for Evaluating Automated Driving Functions in Virtual Traffic. In 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, Las Vegas, NV, USA, 1465–1471. https://doi.org/10.1109/IV47402.2020.9304811
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Carlo van Driesten, Timo Hanke, Nils Hirsenkorn, Pilar Garcia-Ramos, Mark Schiementz, Sebastian Schneider, and Erwin Biebl. 2021. A generic interface for the environment perception of automated driving functions in virtual scenarios. https://www.hot.ei.tum.de/forschung/automotive-veroeffentlichungen/
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Cited By

View all
  • (2024)Exploring augmentation strategies in mixed reality for autonomous driving with depth camerasTransactions of the Institute of Measurement and Control10.1177/01423312241296919Online publication date: 11-Dec-2024
  • (2024)Stepping into safety: a systematic review of extended reality technology applications in enhancing vulnerable road user safetySmart and Sustainable Built Environment10.1108/SASBE-10-2023-0321Online publication date: 4-Jul-2024
  • (2023)Critical Scenario Identification Concept: The Role of the Scenario-in-the-Loop Approach in Future Automotive TestingIEEE Access10.1109/ACCESS.2023.329887511(82464-82476)Online publication date: 2023
  • Show More Cited By
  1. Mixed Reality Environment for Complex Scenario Testing

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    Information & Contributors

    Information

    Published In

    cover image ACM Other conferences
    MuC '21: Proceedings of Mensch und Computer 2021
    September 2021
    613 pages
    ISBN:9781450386456
    DOI:10.1145/3473856
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 13 September 2021

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    Author Tags

    1. Automated Driving Systems
    2. Mixed Reality
    3. Test Procedures
    4. Vehicle in the Loop

    Qualifiers

    • Demonstration
    • Research
    • Refereed limited

    Funding Sources

    • Bundesministerium für Bildung und Forschung

    Conference

    MuC '21
    MuC '21: Mensch und Computer 2021
    September 5 - 8, 2021
    Ingolstadt, Germany

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    Cited By

    View all
    • (2024)Exploring augmentation strategies in mixed reality for autonomous driving with depth camerasTransactions of the Institute of Measurement and Control10.1177/01423312241296919Online publication date: 11-Dec-2024
    • (2024)Stepping into safety: a systematic review of extended reality technology applications in enhancing vulnerable road user safetySmart and Sustainable Built Environment10.1108/SASBE-10-2023-0321Online publication date: 4-Jul-2024
    • (2023)Critical Scenario Identification Concept: The Role of the Scenario-in-the-Loop Approach in Future Automotive TestingIEEE Access10.1109/ACCESS.2023.329887511(82464-82476)Online publication date: 2023
    • (2022)MiRE, A Mixed Reality Environment for Testing of Automated Driving FunctionsIEEE Transactions on Vehicular Technology10.1109/TVT.2022.316035371:4(3443-3456)Online publication date: Apr-2022
    • (2022)How Simulation based Test Methods will substitute the Proving Ground Testing?2022 IEEE Intelligent Vehicles Symposium (IV)10.1109/IV51971.2022.9827394(903-908)Online publication date: 4-Jun-2022
    • (2022)Comparing Different Pedestrian Representations for Testing Automated Driving Functions in Mixed Reality Environments2022 IEEE International Conference on Vehicular Electronics and Safety (ICVES)10.1109/ICVES56941.2022.9986669(1-7)Online publication date: 14-Nov-2022

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