Abstract
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.
Abstract (translated)
自动驾驶车辆(AVs)对激光雷达(LDAR)系统依赖性很大,用于准确感知和导航,提供高分辨率的三维环境数据,这对目标检测和分类至关重要。然而,LDAR系统很容易受到对抗性攻击,这给AV的安全和鲁棒性带来了重大挑战。这项调查对针对基于LDAR感知系统的当前研究格局进行了全面的回顾,涵盖了单模态和多模态情境。我们分类并分析了各种攻击类型,包括伪造和物理对抗性对象攻击,详细描述了它们的攻击方法、影响和潜在的现实世界影响。通过详细的案例研究和分析,我们找出了关键挑战,突出了现有攻击中LDAR系统存在的空白。此外,我们提出了未来研究的方向,以增强这些系统的安全性和鲁棒性,最终为自动驾驶车辆的更安全部署做出贡献。
URL
https://arxiv.org/abs/2409.20426