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：端到端（E2E）自主驾驶规划器通过模仿学习训练，容易受到统计捷径的影响：它们将仅与专家动作共现的场景元素（如路边物体、建筑立面）与驾驶决策相关联，而非因果决定驾驶决策的变量。这种因果混淆在长尾场景中无声地损害可靠性，且难以检测，因为当前主流的开环指标（L2位移和碰撞率）受自车状态主导，无法指示规划器是否依赖虚假线索。基于因果干预训练的现有修复方法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，一种无需训练框架，可在不更新任何参数的情况下，对预训练E2E规划器中的虚假依赖性进行审计、基准测试和修复。