A Survey on Adversarial Robustness of LiDAR-based Machine Learning Perception in Autonomous Vehicles

Autor:	Kim, Junae, Kaur, Amardeep
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence Computer Science - Cryptography and Security
Druh dokumentu:	Working Paper
Popis:	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. Comment: 20 pages, 2 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.13778 Zobrazit plný text záznamu View this record from Arxiv