Planner-Breaker: Finding Informative Failures of Driving Planners with Reinforcement Learning

Closed-loop driving benchmarks assess nominal performance among logged or rule-based traffic, but reveal little about a planner's weaknesses. Adversarial methods address this by generating safety-critical situations, but an identified collision is only informative if the planner could have avoided it. We introduce Planner-Breaker, which uses self-play reinforcement learning at scale to train traffic that produces collisions of a chosen form against a given black-box planner. Since this reward does not need to be differentiable, we instantiate it with a counterfactual criterion that tests whether braking in time would have avoided a collision, and whether the danger was foreseeable early enough to react. Evaluated against five rule-based and learned planners, Planner-Breaker produces about three times more avoidable collisions than gradient-based adversaries, which collide about as often or less. A cross-evaluation shows that each adversary exploits weaknesses specific to the planner it was trained on. Our adversaries can be used without retraining as a traffic model in real-world scenes, where they produce more informative collisions than log replay and rule-based traffic. The same method optimizes other failure criteria, such as nuPlan's at-fault rule, allowing practitioners to search their own planner for the failures they care about.