Roadside lidar-based scene understanding toward intelligent traffic perception: A comprehensive review

Urban transportation systems are undergoing a paradigm shift with the integration of high-precision sensing technologies and intelligent perception frameworks. Roadside lidar, as a key enabler of infrastructure-based sensing technology, offers robust and precise 3D spatial understanding of dynamic urban scenes. This paper presents a comprehensive review of roadside lidar-based traffic perception, structured around five key modules: sensor placement strategies; multi-lidar point cloud fusion; dynamic traffic information extraction;subsequent applications including trajectory prediction, collision risk assessment, and behavioral analysis; representative roadside perception benchmark datasets. Despite notable progress, challenges remain in deployment optimization, robust registration under occlusion and dynamic conditions, generalizable object detection and tracking, and effective utilization of heterogeneous multi-modal data. Emerging trends point toward perception-driven infrastructure design, edge-cloud-terminal collaboration, and generalizable models enabled by domain adaptation, self-supervised learning, and foundation-scale datasets. This review aims to serve as a technical reference for researchers and practitioners, providing insights into current advances, open problems, and future directions in roadside lidar-based traffic perception and digital twin applications.