In autonomous driving, the combination of AI and vehicular technology offers great potential. However, this amalgamation comes with vulnerabilities to adversarial attacks. This survey focuses on the intersection of Adversarial Machine Learning (AML) and autonomous systems, with a specific focus on LiDAR-based systems. We comprehensively explore the threat landscape, encompassing cyber-attacks on sensors and adversarial perturbations. Additionally, we investigate defensive strategies employed in countering these threats. This paper endeavors to present a concise overview of the challenges and advances in securing autonomous driving systems against adversarial threats, emphasizing the need for robust defenses to ensure safety and security.

翻译：在自动驾驶领域，人工智能与车辆技术的结合展现出巨大潜力。然而，这种融合也带来了对抗攻击的脆弱性。本综述聚焦于对抗性机器学习与自动驾驶系统的交叉领域，特别关注基于LiDAR的系统。我们全面探讨了威胁态势，涵盖针对传感器的网络攻击和对抗性扰动。此外，我们研究了应对这些威胁所采用的防御策略。本文旨在简要概述自动驾驶系统抵御对抗性威胁所面临的挑战与进展，强调构建鲁棒防御机制对保障系统安全性的必要性。