Trustworthy autonomous driving via defense-aware robust reinforcement learning against worst-case observational perturbations

Abstract

Despite the substantial advancements in reinforcement learning (RL) in recent years, ensuring trustworthiness remains a formidable challenge when applying this technology to safety-critical autonomous driving domains. One pivotal bottleneck is that well-trained driving policy models may be particularly vulnerable to observational perturbations or perceptual uncertainties, potentially leading to severe failures. In view of this, we present a novel defense-aware robust RL approach tailored for ensuring the robustness and safety of autonomous vehicles in the face of worst-case attacks on observations. The proposed paradigm primarily comprises two crucial modules: an adversarial attacker and a robust defender. Specifically, the adversarial attacker is devised to approximate the worst-case observational perturbations that attempt to induce safety violations (e.g., collisions) in the RL-driven autonomous vehicle. Additionally, the robust defender is developed to facilitate the safe RL agent to learn robust optimal policies that maximize the return while constraining the policy and cost perturbed by the adversarial attacker within specified bounds. Finally, the proposed technique is assessed across three distinct traffic scenarios: highway, on-ramp, and intersection. The simulation and experimental results indicate that our scheme enables the agent to execute trustworthy driving policies, even in the presence of the worst-case observational perturbations.