Traffic Signs Recognition for Connected and Autonomous Vehicles: A Survey of Threats and Defenses

Traffic sign recognition is an important topic in the safety of connected and autonomous vehicles (CAVs). However, adversarial attacks pose significant threats by exploiting vulnerabilities in machine learning models to manipulate or misclassify traffic signs. This paper provides a comprehensive survey of adversarial attacks targeting traffic sign recognition systems. Specifically, it categorizes existing attack methodologies into distinct strategies, such as perturbation-based, patch-based, and targeted effects, offering a structured understanding of their approaches and implications. In addition, the paper reviews and organizes the current defense strategies used to mitigate these threats, highlighting their effectiveness and limitations. By presenting these insights, the survey aims to guide future research in developing robust and resilient traffic sign recognition systems to improve the safety of intelligent transportation systems.