The Road Ahead: A Comprehensive Review of Recent Advances in Traffic Sign and Lane Line Recognition for Autonomous Systems

The perception systems for Traffic Sign Recognition (TSR) and Lane Line Recognition (LLR) are foundational pillars for the safe and effective operation of Advanced Driver-Assistance Systems (ADAS) and fully autonomous vehicles. This review provides a comprehensive analysis of the latest academic research in these domains, strictly focusing on literature published from October 2024 to the present. The analysis reveals several key trends shaping the field. In TSR, architectural evolution is characterized by the refinement of Convolutional Neural Networks (CNNs), the specialization of light-weight YOLO-based models for real-time embedded applications, and the emergence of hybrid CNN-Transformer architectures. Concurrently, a significant research thrust is dedicated to enhancing robustness against environmental adversities and a growing spectrum of sophisticated, physically plausible adversarial attacks. In LLR, the paradigm is rapidly shifting from 2D image-plane detection to full 3D spatial localization and topology reasoning, driven by Transformer-based models that excel at capturing global context and long-range dependencies. Cross-cutting themes common to both domains include a relentless drive for computational efficiency, a data-centric approach marked by the creation of new, challenging benchmarks for adverse conditions and 3D perception, and the nascent but transformative integration of multi-task learning and Vision-Language Models (VLMs) to build systems capable of holistic scene reasoning. Despite significant progress, several key challenges persist in the field of domain generalization, particularly in handling long-tail corner cases and developing safety-aware evaluation metrics. Future research is expected to focus on self-supervised learning, stronger integration between perception and control systems, and the advancement of trustworthy AI through improved explainability and robust-ness. These efforts will lay the groundwork for the next generation of intelligent vehicle systems.