Adversarial Attacks on Autonomous Driving Systems in the Physical World: A Survey
Lijun Chi, Mounira Msahli, Qingjie Zhang, Han Qiu, Tianwei Zhang, Gérard Memmi, Meikang Qiu
Abstract
Autonomous Driving Systems (ADS) represent a revolutionary advancement in transportation and offer unprecedented safety and convenience. Real-world physical attacks are emphasized because Autonomous Driving Systems (ADS) depend heavily on sensors and perception modules to detect and interpret their surroundings, making security a critical concern. Defenders usually have the upper hand in the digital sphere while they are challenged in the physical world because attackers have greater flexibility for covert operations. A comprehensive analysis is essential for understanding attack trends, evolution, and defense directions. This paper provides a survey of state-of-the-art physical attacks that threaten ADS perception. A novel multi-label classification method is introduced to categorize these attacks along four main dimensions. Visualization and analysis of the classification enhance the understanding of these multidimensional threats. Five research directions for future exploration are also proposed.