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Identifying Critical Scenarios in Autonomous Driving During Operation

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Artificial Intelligence. ECAI 2023 International Workshops (ECAI 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1947))

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Abstract

Ensuring autonomous driving systems’ safety, reliability, and trustworthiness is paramount to preventing incorrect or unexpected system behaviors and hazardous scenarios. However, due to the complexity of such systems and the immense search space of possible scenarios, testing could be infeasible, necessitating the need to detect critical situations during operation. This paper proposes a hybrid approach that combines qualitative reasoning and object detection to prevent and discover critical driving scenarios. The proposed approach relies on identifying spatiotemporal patterns of detected objects in the driving environment that are indicative of critical scenarios, such as specific changes in movement or physical impossibilities. We evaluate the approach’s effectiveness on real-world driving data and demonstrate its ability to identify critical driving situations successfully. Moreover, we discuss the challenges associated with the approach and outline future research activities.

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Notes

  1. 1.

    https://www.a2d2.audi/a2d2/en.html.

  2. 2.

    https://potassco.org/.

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Acknowledgments

The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology, and Development, and the Christian Doppler Research Association is gratefully acknowledged.

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Authors and Affiliations

  1. CD Laboratory for Quality Assurance Methodologies for Autonomous Cyber -Physical Systems, Institute of Software Technology, Graz University of Technology, Graz, Austria

    Lorenz Klampfl & Franz Wotawa

Authors
  1. Lorenz Klampfl
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  2. Franz Wotawa
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Corresponding author

Correspondence to Lorenz Klampfl .

Editor information

Editors and Affiliations

  1. Halmstad University, Halmstad, Sweden

    Sławomir Nowaczyk

  2. Warsaw University of Technology, Warsaw, Poland

    Przemysław Biecek

  3. Warsaw University, Warsaw, Poland

    Neo Christopher Chung

  4. University of Huddersfield, Huddersfield, UK

    Mauro Vallati

  5. AGH University of Science and Technology, Kraków, Poland

    Paweł Skruch

  6. AGH University of Science and Technology, Kraków, Poland

    Joanna Jaworek-Korjakowska

  7. University of Huddersfield, Huddersfield, UK

    Simon Parkinson

  8. University of Huddersfield, Huddersfield, UK

    Alexandros Nikitas

  9. Universität Osnabrück, Osnabrück, Germany

    Martin Atzmüller

  10. University of Economics Prague, Prague, Czech Republic

    Tomáš Kliegr

  11. University of Bamberg, Bamberg, Germany

    Ute Schmid

  12. Jagiellonian University, Kraków, Poland

    Szymon Bobek

  13. Jožef Stefan Institute, Ljubljana, Slovenia

    Nada Lavrac

  14. HU University of Applied Sciences Utrecht, Utrecht, The Netherlands

    Marieke Peeters

  15. Rotterdam University of Applied Sciences, Rotterdam, The Netherlands

    Roland van Dierendonck

  16. Amsterdam University of Applied Sciences, Amsterdam, The Netherlands

    Saskia Robben

  17. University of Reims Champagne-Ardenne, Reims, France

    Eunika Mercier-Laurent

  18. Istanbul Technical University, Istanbul, Türkiye

    Gülgün Kayakutlu

  19. Wroclaw University of Economics and Business, Wrocław, Poland

    Mieczyslaw Lech Owoc

  20. University of Galway, Galway, Ireland

    Karl Mason

  21. University of Galway, Galway, Ireland

    Abdul Wahid

  22. University of Calabria, Rende, Italy

    Pierangela Bruno

  23. University of Calabria, Rende, Italy

    Francesco Calimeri

  24. Marche Polytechnic University, Ancona, Italy

    Francesco Cauteruccio

  25. University of Calabria, Rende, Italy

    Giorgio Terracina

  26. University of Bamberg, Bamberg, Germany

    Diedrich Wolter

  27. Coburg University of Applied Sciences, Coburg, Germany

    Jochen L. Leidner

  28. FAU Erlangen-Nürnberg, Erlangen, Germany

    Michael Kohlhase

  29. University of Leeds, Leeds, UK

    Vania Dimitrova

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Klampfl, L., Wotawa, F. (2024). Identifying Critical Scenarios in Autonomous Driving During Operation. In: Nowaczyk, S., et al. Artificial Intelligence. ECAI 2023 International Workshops. ECAI 2023. Communications in Computer and Information Science, vol 1947. Springer, Cham. https://doi.org/10.1007/978-3-031-50396-2_9

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  • DOI: https://doi.org/10.1007/978-3-031-50396-2_9

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  • Online ISBN: 978-3-031-50396-2

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