Three-dimensional LiDAR is widely used in unmanned driving systems, mainly to detect the road environment and for collision avoidance detection. A real-time method to segment the point cloud based on depth projection was proposed to increase the segmentation accuracy of a point cloud scanned by LiDAR. Voxel filtering was first used to remove noise points, after which progressive morphological filtering was used to remove ground points, and finally the point cloud was subjected to point depth projection. The adaptive angle threshold method for the depth projection image was used to segment the point cloud, and after segmentation of the point cloud target, a hybrid hierarchical bounding box was constructed for collision detection. The experimental results show that this method constitutes a significant improvement in time efficiency compared with traditional clustering algorithms, and can effectively reduce the problem of over-segmentation. The proposed method increased the segmentation accuracy rate in the experiment to 78.82%. The combined hierarchical bounding box algorithm is applied to the segmented points.