Adversarial Stress Test for Autonomous Vehicle via Series Reinforcement Learning Tasks With Reward Shaping
Xuan Cai, Xuesong Bai, Zhiyong Cui, Peng Hang, Haiyang Yu, Yilong Ren
Abstract
Testing is a pivotal phase for uncovering potential vulnerabilities in autonomous vehicles (AVs) to develop a secure autonomy system. However, existing methods often lack consideration for efficiently exploring multiple vulnerability-revealing cases, particularly under adversarial game scenarios. We introduce an evolving series reinforcement learning (RL) framework for adversarial policy training, integrating Responsibility Sensitive Safety (RSS) and Dynamic Time Warping (DTW) theories to shape the reward function to steer the evolving direction of the subsequent series agents for exploring vulnerability-revealing attack scenarios uncharted in the refined buffered repository. Our method undertakes adversarial stress tests for both black-box and white-box AV systems under test in driving tasks that engage in games with traffic vehicles and pedestrians. The results indicate that our approach expedites the exploration of additional scenarios blamed for the AV, outperforming the baselines in the vulnerability-revealing accident and scenario diversity. Furthermore, the causality of the collisions is qualitatively analyzed to provide insights for AV system vulnerability repair.