Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles
This survey addresses the critical problem of securing LiDAR perception systems against adversarial attacks for autonomous vehicle safety, providing a comprehensive overview for researchers and developers in the field.
This survey reviews physical adversarial attacks on LiDAR perception systems in autonomous vehicles, categorizing and analyzing various attack types like spoofing and physical adversarial object attacks. It details their methodologies, impacts, and real-world implications, identifying critical challenges and gaps in current research.
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.