AuthSim: Towards Authentic and Effective Safety-critical Scenario Generation for Autonomous Driving Tests

Overview

AuthSim combines reinforcement learning with a three-layer relative-safety-region model to constrain adversarial traffic behavior. Experiments examine whether boundary-focused interactions yield plausible, effective safety-critical scenarios rather than indiscriminate collision attacks.