系统梳理激光雷达在自动驾驶中的物理攻击威胁
Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles
- 按欺骗与实体对抗物分类，分析攻击原理与实现方式
- 揭示真实场景下攻击对感知系统的破坏性影响
- 适合关注自动驾驶安全与防御的研究者参考
自动驾驶汽车严重依赖激光雷达（LiDAR）系统获取高精度三维环境数据，用于物体检测与分类。然而，激光雷达易受对抗攻击，威胁车辆安全性与系统鲁棒性。本文全面综述针对基于激光雷达感知系统的物理对抗攻击研究现状，涵盖单模态与多模态场景。我们对欺骗攻击和实体对抗物体攻击进行分类与分析，详述其方法、影响及现实应用后果。通过案例研究，识别现有攻击的局限与关键挑战，并提出未来研究方向，以提升系统的安全性和抗干扰能力，推动自动驾驶更安全落地。
原文摘要 · Abstract (English)
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.
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