Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove

Abstract:AI-based automated vehicle testing is challenging because a model that passes every test condition can still fail in the real world. Formal verification offers a way to directly address this gap. On a simulated highway and an arterial road we trained two small end-to-end steering networks each in CARLA, one on clear conditions alone and one on clear, fog, night and low sun. All four models were driven against a 2.19 ft lane-departure budget. Without driving again, we used bound propagation, a formal method that reads the trained weights, to compute how far steering can drift at every disturbance strength between two captured images. One calculation covers more than a campaign could drive: on the arterial it spans 133 poses, where ten intensities each would be 10^133 combinations, in minutes on one GPU. Not only did formal verification find conditions that broke the clear-trained policy without simulation testing, it provided some preliminary evidence for potential failures between the test cases. Our overall conclusion is that formal verification is a viable complement to simulation, and could be adopted as a part of verification and validation for automated driving.

Robotics (cs.RO); Machine Learning (cs.LG)

arXiv:2609.10951 [cs.RO]

(or arXiv:2609.10951v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.10951 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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