检测并修复自动驾驶模型对无关场景元素的错误依赖。
Physics-Grounded Causal Auditing of End-to-End Driving Planners
- 基于物理因果理论，不重新训练直接分析预训练模型的决策逻辑。
- 在真实驾驶数据集上验证，可识别出30%以上由虚假关联导致的错误决策。
- 适合已部署的自动驾驶系统进行安全审计，无需模型更新。
端到端（E2E）自动驾驶规划器通过模仿学习训练时容易采用统计捷径：将仅与专家动作共现的场景元素（如路边物体、建筑立面）错误关联到驾驶决策，而非真正起因果作用的变量。这种因果混淆在长尾场景中悄然降低可靠性，且难以被现有开环指标（如L2位移和碰撞率）发现，因为这些指标受自车状态主导，无法揭示模型是否依赖虚假线索。现有基于因果干预的修复方法需重新训练大模型，无法用于已部署的规划器。本文提出CADET，一种无需训练的框架，可对预训练的E2E规划器进行审计、基准测试和修复，无需任何参数更新。
原文摘要 · Abstract (English)
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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