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Using online verification to prevent autonomous vehicles from causing accidents

Abstract

Ensuring that autonomous vehicles do not cause accidents remains a challenge. We present a formal verification technique for guaranteeing legal safety in arbitrary urban traffic situations. Legal safety means that autonomous vehicles never cause accidents although other traffic participants are allowed to perform any behaviour in accordance with traffic rules. Our technique serves as a safety layer for existing motion planning frameworks that provide intended trajectories for autonomous vehicles. We verify whether intended trajectories comply with legal safety and provide fallback solutions in safety-critical situations. The benefits of our verification technique are demonstrated in critical urban scenarios, which have been recorded in real traffic. The autonomous vehicle executed only safe trajectories, even when using an intended trajectory planner that was not aware of other traffic participants. Our results indicate that our online verification technique can drastically reduce the number of traffic accidents.

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Fig. 1: Verification of legal safety.
Fig. 2: Verification during replanning.
Fig. 3: Results of Scenario I (urban intersection).
Fig. 4: Results of Scenario II (jaywalking pedestrian).
Fig. 5: Results of the verification technique with different intended planners.
Fig. 6: Computation steps of the verification technique.

Data availability

All data gathered and reported in this study are available in the Supplementary data file. This includes the environment model, the intended trajectory and the verification result of each verification cycle for all scenarios.

Code availability

The code to visualize and analyse the gathered data and obtained results of this study are included in the Supplementary data file.

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Acknowledgements

We thank S. Kaster for his support in implementing the prediction and S. Steyer for providing the object detection and tracking algorithms. We also thank C. Schürmann for the voice-overs in the Supplementary Videos. This work was partially supported by the BMW Group within the CAR@TUM project, the German Federal Ministry of Economics and Technology through the research initiative Ko-HAF, and the German Research Foundation (DFG) under grants AL 1185/4-2 and AL 1185/3-2.

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Contributions

C.P., S.M. and M.K. developed the verification technique during replanning. M.K. developed the concept and algorithms for the set-based prediction. C.P. and S.M. developed the concept and algorithms for the drivable area computation, driving corridor identification and fail-safe trajectory planning. M.A. developed the main concept of online verification by integrating set-based prediction and fail-safe trajectory generation. He also developed the underlying algorithms for reachability analysis and led the research project. C.P., S.M. and M.K. designed and conducted the experiments and collected the data. The Article and the Supplementary Information were written by C.P., S.M. and M.K.

Corresponding authors

Correspondence to Christian Pek, Stefanie Manzinger or Markus Koschi.

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Supplementary information

Supplementary Information

Supplementary information with Figs. 1–6, results with Figs. 7–10, methods with Fig. 11 and Tables 1–6, description of the data file, description of Videos 1–3.

Supplementary Video 1

Verification results of presented scenarios.

Supplementary Video 2

Illustration of computation steps during a single verification cycle.

Supplementary Video 3

Comparing the results of different intended planners.

Supplementary Data File

Recorded scenarios, obtained solutions and visualization software.

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Pek, C., Manzinger, S., Koschi, M. et al. Using online verification to prevent autonomous vehicles from causing accidents. Nat Mach Intell 2, 518–528 (2020). https://doi.org/10.1038/s42256-020-0225-y

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