Physics-Grounded Causal Auditing of End-to-End Driving Planners
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 alread...
| Authors | Zikun Guo, Minglan Chen, Jinyou Zhai et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 June 2026 |
| Last updated at source | 18 August 2026 |
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