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A Formal Model of Train Control with AI-Based Obstacle Detection

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Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification (RSSRail 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14198))

Abstract

The research project KI-LOK aims to develop a certification methodology for incorporating AI components into rail vehicles. In this work, we study how to safely incorporate an AI for obstacle detection into an ATO (automatic train operation) system for shunting movements. To analyse the safety of our system we present a formal B model comprising the steering and AI perceptions subsystems as well as the shunting yard environment. Classical model checking is applied to ensure that the complete system is safe under certain assumptions. We use SimB to simulate various scenarios and estimate the likelihood of certain errors when the AI makes mistakes.

This research is part of the KI-LOK project funded by the “Bundesministerium für Wirtschaft und Energie”; grant # 19/21007E.

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Notes

  1. 1.

    https://ki-lok.itpower.de.

  2. 2.

    But we are also investigating systems with two cameras or with LiDAR sensors.

  3. 3.

    https://github.com/ultralytics/ultralytics.

  4. 4.

    https://universe.roboflow.com/kilok/sign-detection-4oqe4/dataset/2.

  5. 5.

    https://automaticaddison.com/how-to-determine-the-orientation-of-an-object-using-opencv/.

  6. 6.

    We hope to obtain such precise figures from industrial partners in our project.

  7. 7.

    For now, we define that the probability of a safe drive from 347a to 855b must be \(\ge \) 99.9%.

  8. 8.

    For now, we define that the probability of achieving the mission order must be \(\ge \) 99.9%.

  9. 9.

    The models can be found at https://github.com/hhu-stups/kilok_shunting_model/tree/14c2ecdb6e32ba593cac64e5868c94773139b391.

  10. 10.

    Version: 1.12.0-final (fef4b935b59d76e353ab67230f6206b15f903f4b, 05.04.2023).

  11. 11.

    Some of the traces can be accessed as an interactive HTML document at

    https://stups.hhu-hosting.de/models/kilok/HTML_Traces.

  12. 12.

    https://data.fid-move.de/dataset/osdar23.

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Acknowledgements

Infrastructure for model checking benchmarks was provided by the “Centre for Information and Media Technology” (ZIM) at the University of Düsseldorf (Germany). We thank anonymous reviewers for their very helpful comments and links to related work.

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Authors and Affiliations

  1. Institut für Informatik, Universität Düsseldorf, Universitätsstr. 1, 40225, Düsseldorf, Germany

    Jan Gruteser, David Geleßus, Michael Leuschel, Jan Roßbach & Fabian Vu

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  1. Jan Gruteser
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  2. David Geleßus
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  3. Michael Leuschel
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  4. Jan Roßbach
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  5. Fabian Vu
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Corresponding author

Correspondence to Michael Leuschel .

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Editors and Affiliations

  1. Technische Universität Berlin, Berlin, Germany

    Birgit Milius

  2. Université Gustave Eiffel, Villeneuve d’Ascq, France

    Simon Collart-Dutilleul

  3. CLEARSY Systems Engineering, Aix en Provence, France

    Thierry Lecomte

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Gruteser, J., Geleßus, D., Leuschel, M., Roßbach, J., Vu, F. (2023). A Formal Model of Train Control with AI-Based Obstacle Detection. In: Milius, B., Collart-Dutilleul, S., Lecomte, T. (eds) Reliability, Safety, and Security of Railway Systems. Modelling, Analysis, Verification, and Certification. RSSRail 2023. Lecture Notes in Computer Science, vol 14198. Springer, Cham. https://doi.org/10.1007/978-3-031-43366-5_8

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  • DOI: https://doi.org/10.1007/978-3-031-43366-5_8

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