This paper aims to introduce deep learning approaches developed to overcome challenges in traffic sign recognition, which include YOLOv8-TS, STD-YOLOv5s, Semantic Scene Understanding & Structured Localization, and Cascade R-CNN with Multi-Scale Attention. The paper also delves into critical system architecture considerations, analyzing the roles of various sensors and computing platforms in enabling real-time and efficient TSR. Performance evaluation is discussed using essential metrics such as Precision, Recall, F1-score, and mean Average Precision (mAP), highlighting their importance in quantifying detection and classification accuracy. Optimization strategies are emphasized as crucial for practical deployment, relying heavily on continuous algorithmic advances (like novel network architectures and attention modules) and sophisticated hardware-software co-design to balance computational efficiency with recognition performance. Looking ahead, TSR research is poised to advance significantly in key directions, including multi-modal data fusion for enhanced environmental perception, extreme lightweighting and real-time optimization for edge deployment, achieving cross-regional and cross-language generality, developing robust adversarial defense and safety mechanisms, improving model interpretability, and fostering deeper integration with driving decision systems. These advancements are vital for enhancing the reliability and safety of autonomous vehicles operating in complex, real-world driving environments.
X. Chen (Tue,) studied this question.
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