An effective object segmentation and discrimination method based on DST fusion multi-view fuzzy reasoning assignment is proposed for the point cloud data of pre-lidar. The point cloud data is transformed into a voxel map and road surface segmentation is carried out to obtain the front and top views. In the two views, the basic probability of the voxel's degree of belonging to the object is assigned according to different fuzzy inference rules, and the target is distinguished by DST fusing, and the object is accurately segmented to obtain the parameters of the box model. Converting the 3D recognition problem into a series of 2D detection problems, compared with directly utilizing the 3D point cloud information, can reduce the complexity of data processing and improve the stability of the system. Experiments have been carried out on a self-developed autonomous cars using a 16-line lidar and TX2 embedded development board, with the comparison and verification on KITTI. The results show that the method has good real-time and accuracy in practical application.