Point Cloud Automatic Annotation Framework for Autonomous Driving

In autonomous driving systems, infrastructure LiDAR technology provides advanced point cloud information of the road, allowing for preemptive analysis, which increases decision-making time. 3D object detection affords autonomous vehicles the ability to recognize and understand surrounding environmental objects accurately. To further investigate the optimal deployment locations and impacts of infrastructure LiDAR in autonomous driving systems, we have developed an automated annotation framework integrated into an autonomous driving simulator. This framework enables the automated labeling of point cloud data and the rapid construction of datasets, significantly reducing the time required for users to create such datasets. Additionally, we enhanced the usability of the autonomous driving simulator, allowing for real-time adjustments of LiDAR settings during operation, and the generation of vehicle NPCs in accordance with the OpenSCENARIO 2.0 standard. Finally, utilizing this automatic annotation framework, we conducted an evaluation of the impact of various types of LiDAR (dense point clouds and sparse point clouds) and their quantities on the accuracy of 3D object detection models. The experimental evaluation shows that the number of points in infrastructure point clouds and the detection range have a significant impact on 3D detection models. Upon replacing VLP-16 with MID70, the performance of various models improved significantly, with a maximum increase of 50% mAP.