Learning Responsibility-Attributed Adversarial Scenarios for Testing Autonomous Vehicles
For developers of autonomous driving systems, CARS provides a method to generate test scenarios that distinguish system failures from unavoidable traffic conflicts, enabling more trustworthy safety validation.
CARS integrates responsibility attribution into adversarial scenario generation for autonomous driving systems, producing physically feasible collision scenarios with high attribution rates across multiple traffic environments and driver models.
Establishing trustworthy safety assurance for autonomous driving systems (ADSs) requires evidence that failures arise from avoidable system deficiencies rather than unavoidable traffic conflicts. Current adversarial simulation methods can efficiently expose collisions, but generally lack mechanisms to distinguish these fundamentally different failure modes. Here we present CARS (Context-Aware, Responsibility-attributed Scenario generation), a framework that integrates responsibility attribution directly into adversarial scenario generation. CARS combines context-aware adversary selection with a generative adversarial policy optimized in closed-loop simulation to construct collision scenarios that are both physically feasible and diagnostically attributable. Across benchmark datasets spanning heterogeneous national traffic environments, CARS consistently discovers feasible collision scenarios with high attribution rates under multiple regulation-prescribed careful and competent driver models. By coupling adversarial generation with normative responsibility assessment, CARS moves simulation testing beyond collision discovery toward the construction of interpretable, regulation-aligned safety evidence for scalable ADS validation.