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arXiv · 2609.10951

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

Menuka Ghalan·Charles Rodgers·Zachary D. Asher
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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.

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Menuka Ghalan, Charles Rodgers, Zachary D. Asher. 2026-09-10. Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove. https://arxiv.org/abs/2609.10951

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