A Systematic Review of Adversarial Attacks and Defenses for Deep Reinforcement Learning in Autonomous Vehicle Applications

Deep reinforcement learning (DRL) is a significant component of autonomous vehicle (AV) systems, as it is involved in crucial aspects such as decision-making, motion planning, and control systems. Nevertheless, DRL exhibits inherent weaknesses that render it vulnerable to adversarial attacks. On the other hand, AV applications are classified as safety-critical applications. This work presents a systematic literature review of several forms of adversarial attacks and the corresponding response mechanisms developed in the literature on AVs. This study additionally examines several datasets utilized for benchmarking purposes and different evaluation factors employed to assess the resilience and efficacy of adversarial attacks. The survey adheres to the PRMA standards. Excluded from consideration are articles that failed to take into account adversarial assaults on DRL and Deep Learning (DL). A comprehensive search yielded a total of 2250 distinct peer-reviewed articles sourced from the ACM Digital Library, IEEE Xplore, Springer, and Elsevier databases. Upon fulfilling the predefined criteria for inclusion and exclusion, a sum of 151 articles is encompassed in the ultimate review. In the end, the paper examines the existing research gaps and proposes potential options for further investigations.