  Automated Vehicles require exhaustive testing in simulation to detect as many
safety-critical failures as possible before deployment on public roads. In this
work, we focus on the core decision-making component of autonomous robots:
their planning algorithm. We introduce a planner testing framework that
leverages recent progress in simulating behaviorally diverse traffic
participants. Using large scale search, we generate, detect, and characterize
dynamic scenarios leading to collisions. In particular, we propose methods to
distinguish between unavoidable and avoidable accidents, focusing especially on
automatically finding planner-specific defects that must be corrected before
deployment. Through experiments in complex multi-agent intersection scenarios,
we show that our method can indeed find a wide range of critical planner
failures.
