In the field of autonomous driving,the point cloud data obtained by lidar has problems such as sparsity and edge noise false detection.This paper proposes a point cloud vehicle target detection method based on improved PointPillars.Firstly,the voxelized feature input is improved based on the SimAM attention mechanism,so that the network feature extraction stage can pay more attention to the key information and improve the globality of feature learning.Secondly,based on the improved backbone network structure of CBAM,a new lightweight channel attention module Tiny-CAM and a deformable spatial attention module Deformable-SAM are proposed to construct the Multi- CBAM backbone network and improve the network feature extraction and feature fusion ability.The KITTI data set and the non-public garage point cloud data set are verified.The experimental results show that the method adopted in this paper has higher detection accuracy.Compared with the original network,the average detection accuracy is improved by 2.98%,and the detection accuracy of point cloud vehicle targets with occlusion less than 30%is improved by 6.51%, which proves the effectiveness of the method.