Vehicle Detection Framework Based on LiDAR for Autonoumous Driving

Stable and efficient environmental perception in autonomous driving is an important prerequisite for path planning and behavior prediction. This paper proposes a vehicle identification framework based on LiDAR. After using the ground segmentation algorithm based on Gaussian process regression to complete high-quality ground segmentation, the adaptively density-based spatial clustering of applications with noise (A-DBSCAN) algorithm is used to cluster the remaining obstacle point clouds, and then the improved L-Shape algorithm is used for bounding box fitting. The experimental results based on the KITTI data set show that the framework has good stability under simple working conditions.