A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles
It addresses security vulnerabilities in autonomous driving systems, which is critical for public safety, but is incremental as it synthesizes existing research rather than presenting new findings.
This survey examines the problem of adversarial attacks on LiDAR-based machine learning perception in autonomous vehicles, reviewing threats and defensive strategies to enhance safety and security.
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.