[Submitted on 10 Sep 2026]
Title:Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove
View a PDF of the paper titled Testing Between the Test Cases: Proving End-to-End Steering in Conditions You Never Drove, by Menuka Ghalan and 2 other authors
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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.
Comments: 10 pages, 7 figures, 2 tables
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2609.10951 [cs.RO]
(or arXiv:2609.10951v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.10951
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Zachary Asher [view email] [v1] Thu, 10 Sep 2026 01:19:13 UTC (1,063 KB)
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