CRASH: Challenging Reinforcement-Learning Based Adversarial Scenarios For Safety Hardening
This addresses safety testing for autonomous vehicles by providing a scalable simulation method, though it is incremental as it builds on existing adversarial and reinforcement learning techniques.
The paper tackles the problem of identifying rare failure cases for autonomous vehicle safety by introducing CRASH, an adversarial reinforcement learning framework that generates realistic traffic scenarios to stress test motion planners, achieving over 90% collision rates in falsification and reducing collision rates by 26% through safety hardening.
Ensuring the safety of autonomous vehicles (AVs) requires identifying rare but critical failure cases that on-road testing alone cannot discover. High-fidelity simulations provide a scalable alternative, but automatically generating realistic and diverse traffic scenarios that can effectively stress test AV motion planners remains a key challenge. This paper introduces CRASH - Challenging Reinforcement-learning based Adversarial scenarios for Safety Hardening - an adversarial deep reinforcement learning framework to address this issue. First CRASH can control adversarial Non Player Character (NPC) agents in an AV simulator to automatically induce collisions with the Ego vehicle, falsifying its motion planner. We also propose a novel approach, that we term safety hardening, which iteratively refines the motion planner by simulating improvement scenarios against adversarial agents, leveraging the failure cases to strengthen the AV stack. CRASH is evaluated on a simplified two-lane highway scenario, demonstrating its ability to falsify both rule-based and learning-based planners with collision rates exceeding 90%. Additionally, safety hardening reduces the Ego vehicle's collision rate by 26%. While preliminary, these results highlight RL-based safety hardening as a promising approach for scenario-driven simulation testing for autonomous vehicles.