Enhancing Roadside 3D Object Detection Using Dynamic Pseudo HD Map

Roadside 3D object detection plays a pivotal role in enhancing autonomous driving systems. Elevated sensors, such as those mounted on utility poles, offer distinct advantages in traffic safety by expanding the visual field and reducing occlusions. Despite these benefits, such configurations still face challenges related to occlusion and limited detection range. To address these limitations, we propose a Multi-LiDAR Stitching technique designed to mitigate occlusion and enhance detection coverage. Furthermore, we introduce a dynamic pseudo HD map, which continuously updates via a memory bank, effectively reducing the high costs and maintenance complexities associated with traditional HD maps while adapting to dynamic environmental changes. Evaluations on our newly constructed Multi-LiDAR Stitching dataset show a 3.3% improvement in mAP over the baseline, underscoring the effectiveness of our approach in improving roadside object detection and advancing traffic safety in autonomous driving systems.