A survey of adversarial threats and defenses for traffic sign recognition in autonomous vehicles

Autonomous and intelligent vehicles increasingly rely on perception pipelines that must operate reliably under uncertainty and adversarial manipulation. Traffic Sign Recognition (TSR) is a safety critical perception system because it translates structured road semantics that directly affect planning and control. Despite high accuracy in favorable settings, modern TSR systems that are driven by deep neural networks remain vulnerable to adversarial perturbations, including digital manipulations, physical patches, shadows, lighting effects, and sensor-level attacks that can cause unsafe misclassification. This survey combines adversarial threats and defenses for TSR through a cognitive computing lens. It treats TSR not only as an image classification problem but also as an intelligent perception task that requires understanding traffic sign meaning, handling uncertainty, using domain rules, and supporting safety-critical driving decisions. We provide a TSR specific taxonomy of adversarial attacks spanning attack medium (digital/physical), attacker objective, model access, and attack phase. We also consolidated a view of TSR datasets, recognition pipelines, and evaluation protocols and analysis of robustness metrics beyond attack success rate, including detection and system oriented measures used in recent literature. The survey further examines defense strategies across preprocessing, model training, inference-time detection, verification, and structure-aware reasoning, including approaches that exploit the geometric, color, and semantic regularities of traffic signs. Overall, the survey aims to support the development of trustworthy TSR systems for intelligent vehicles operating in complex, real-world traffic environments.