Physics-Grounded Causal Auditing of End-to-End Driving Planners

Zikun Guo; Minglan Chen; Jinyou Zhai; Rongjin Zou

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arXiv · 2606.14438

Physics-Grounded Causal Auditing of End-to-End Driving Planners

Zikun Guo·Minglan Chen·Jinyou Zhai·Rongjin Zou

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Abstract

End-to-end (E2E) autonomous-driving planners trained by imitation are prone to statistical shortcuts: they associate scene elements that merely co-occur with expert actions (a roadside object, a building facade) with driving decisions, rather than the variables that causally determine them. Such causal confusion silently compromises reliability in long-tail scenarios, and it is difficult to detect, because prevailing open-loop metrics (L2 displacement and collision rate) are dominated by ego status and do not indicate whether a planner depends on spurious cues. Existing remedies based on causal-intervention training require retraining large models and cannot audit a planner that is already deployed. We present CADET, a training-free framework that audits, benchmarks, and repairs spurious reliance in pretrained E2E planners without any parameter update.

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Zikun Guo, Minglan Chen, Jinyou Zhai, Rongjin Zou. 2026-06-12. Physics-Grounded Causal Auditing of End-to-End Driving Planners. https://arxiv.org/abs/2606.14438

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