Roadside Lidar

Roadside LiDAR systems aim to enhance perception and safety in intelligent transportation systems by providing comprehensive 3D views of traffic scenes. Current research focuses on improving object detection and tracking accuracy through advanced algorithms like deep learning models (e.g., variations of PointPillars and encoder-decoder networks), efficient data compression techniques, and optimized sensor placement strategies. These advancements are crucial for enabling real-time applications such as autonomous driving assistance, traffic monitoring, and improved infrastructure-based safety systems. The development of robust calibration methods and effective background modeling techniques are also key areas of ongoing investigation.