Autonomous vehicles (AVs) rely heavily on LiDAR (Light Detection and Ranging) systems for accurate perception and navigation, providing high-resolution 3D environmental data that is crucial for object detection and classification. However, LiDAR systems are vulnerable to adversarial attacks, which pose significant challenges to the safety and robustness of AVs. This survey presents a thorough review of the current research landscape on physical adversarial attacks targeting LiDAR-based perception systems, covering both single-modality and multi-modality contexts. We categorize and analyze various attack types, including spoofing and physical adversarial object attacks, detailing their methodologies, impacts, and potential real-world implications. Through detailed case studies and analyses, we identify critical challenges and highlight gaps in existing attacks for LiDAR-based systems. Additionally, we propose future research directions to enhance the security and resilience of these systems, ultimately contributing to the safer deployment of autonomous vehicles.

本研究针对自主车辆在LiDAR感知系统中存在的物理对抗攻击问题进行了全面调研，揭示了这一领域的研究现状及其面临的挑战。文章分析了多种攻击类型及其方法，对现有研究的不足进行了讨论，并提出了未来研究方向，旨在提升LiDAR系统的安全性和鲁棒性，确保自主车辆的安全部署。

应对威胁：对自主车辆LiDAR感知系统的物理对抗攻击的调研