Annotated 3D Point Cloud Dataset for Traffic Management in Simulated Urban Intersections

Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle’s viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D intersections. Our approach introduces a point cloud collection framework using static LiDAR sensors to provide a global view of the entire traffic scene. By incorporating randomized traffic patterns observed from multiple angles, this method generates a diverse, comprehensive dataset for object detection and instance segmentation, showcasing its advantages for benchmarking smart mobility applications.