A Survey of Adversarial Methods in Autonomous Driving
Huyan Gong, Dongcheng Li, W. Eric Wong, Hui Li · 2025
The convergence of autonomous driving and deep learning technologies has brought unprecedented convenience to future mobility while also introducing new security challenges. Adversarial attacks can exploit minor perturbations to deceive vehicle perception and decision-making processes, thereby posing potential dangers to both passengers and pedestrians. Although considerable progress has been made in developing adversarial detection and defense mechanisms, significant challenges remain, including high computational overhead, limited real-time performance, incomplete multi-modal integration, and insufficient understanding of black-box attacks and cross-scenario transfer. To comprehensively enhance the security and robustness of autonomous driving systems, it is necessary to further expand the data and model scales of adversarial examples, promote multi-modal fusion, improve the generalizability and real-time performance of adversarial defenses, and conduct additional validation under realistic and complex environments. Based on these considerations, this paper systematically reviews recent advances and gaps in adversarial research for autonomous driving. Furthermore, it explore future research directions from the perspectives of multi-modal fusion, dataset scale expansion, black-box defense, and the emergent role of large language models.