AI-driven Traffic Scene Understanding Using Static LiDAR Sensors

Elham Binshaflout, Chaima Zaghouani, Charalampos Antoniadis, Hakim Ghazzai, Nawfal Guefrachi, Ahmad Alsharoa, Sameh Najeh, Gianluca Setti

Abstract: Traffic congestion and road safety remain critical challenges in urban environments, driving the need for more effective traffic monitoring solutions. While recent advancements in computer vision have enhanced traffic perception, the dynamic viewpoint of autonomous vehicles is often insufficient for comprehensive traffic management. To address this gap, we propose an AI-driven framework for enhanced traffic scene understanding using static LiDAR sensors at road intersections. The system collects 3D point clouds from roadside static LiDAR sensors, providing a complete view of vehicles and pedestrians. We integrate state-of-the-art 3D object detection (i.e., PV-RCNN) and instance segmentation models (i.e., PointGroup3heads) to accurately identify road users and their locations. To validate the approach, we developed a 3D simulated traffic environment and generated a labeled dataset of diverse traffic scenarios. Our experimental results demonstrate the effectiveness of combining static LiDAR sensors with deep learning models for accurate scene understanding, offering a scalable solution for AI-driven traffic monitoring in complex urban areas.

External IDs:dblp:conf/aicas/BinshafloutZAGGANS25