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.

翻译：基于模仿学习的端到端自动驾驶规划器易受统计捷径影响：它们会将仅与专家操作共现的场景元素（如路边物体、建筑立面）与驾驶决策关联起来，而非真正决定驾驶行为的因果变量。这种因果混淆会在长尾场景中悄然削弱系统可靠性，且难以被检测，因为主流开环评估指标（L2位移误差和碰撞率）主要受车辆自状态主导，无法反映规划器是否依赖虚假关联。现有基于因果干预训练的解决方案需要重新训练大型模型，且无法审计已部署的规划器。我们提出CADET——一种无需训练即可审计、基准测试并修复预训练端到端规划器中虚假依赖的框架，全程无需更新任何参数。