综述激光雷达感知在自动驾驶中的对抗攻击与防御方法
A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
- 梳理激光雷达系统面临的对抗攻击类型与威胁
- 总结现有防御策略及其在真实场景中的有效性
- 适合关注自动驾驶安全的工程师与研究人员阅读
在自动驾驶领域，人工智能与车辆技术的结合带来了巨大潜力，但也存在对抗攻击的漏洞。本综述聚焦于对抗机器学习（AML）与自动驾驶系统的交叉点，特别关注基于激光雷达的系统。全面探讨了网络攻击对传感器的威胁以及对抗扰动的影响，同时研究了现有的防御策略。本文旨在简明呈现自动驾驶系统在对抗威胁下的挑战与进展，强调构建鲁棒防御机制对于保障安全与可靠性的必要性。
原文摘要 · Abstract (English)
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.
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