Abstract
Ensuring pedestrian-safety is paramount to the acceptance and success of autonomous cars. The scenario-based training and testing of such self-driving vehicles in virtual driving simulation environments has increasingly gained attention in the past years. A key challenge is the automated generation of critical traffic scenarios which usually are rare in real-world traffic, while computing and testing all possible scenarios is infeasible in practice. In this paper, we present a formal method-based approach CriSGen for an automated and complete generation of critical traffic scenarios for virtual training of self-driving cars. These scenarios are determined as close variants of given but uncritical and formally abstracted scenarios via reasoning on their non-linear arithmetic constraint formulas, such that the original maneuver of the self-driving car in them will not be pedestrian-safe anymore, enforcing it to further adapt the behavior during training.
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Notes
- 1.
OpenDS scenarios are described in specific XML files from which such behaviors can be extracted.
- 2.
This disallows maneuvers in which a car drives slowly by, say, 1 m/s, and decelerates by 2 m/s, thus reversing its motion direction. Reversing the sense of direction should be described by decelerating to a stop and a further deceleration for driving backwards.
- 3.
The units do not really matter as long as they are kept consistent throughout the specification.
- 4.
This can trivially be described as a quantifier elimination problem.
- 5.
\(\epsilon \) compensates minor differences between the computed reachable states and the driving simulator’s behavior.
- 6.
Evidently, for any \(0\le c\le a\le 10\) the corresponding instantiation is behavior equivalent to the original scenario. Therefore the safe (green) variants form the top-left triangle instead of a single point.
- 7.
Note that in this illustration the unsafe region and the original region do not intersect, since cutting the area with the plane at \(b=1\) produces exactly Fig. 5.
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Acknowledgement
This research was supported by the German Federal Ministry for Education and Research (BMB+F) in the project REACT.
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Nonnengart, A., Klusch, M., Müller, C. (2020). CriSGen: Constraint-Based Generation of Critical Scenarios for Autonomous Vehicles. In: Sekerinski, E., et al. Formal Methods. FM 2019 International Workshops. FM 2019. Lecture Notes in Computer Science(), vol 12232. Springer, Cham. https://doi.org/10.1007/978-3-030-54994-7_17
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