Aiming at the problems of inaccurate recognition results and large deviation of target orientation detection when 3D target detection is carried out by laser radar during auto driving, a 3D target detection method of laser radar based on improved PointPillars is proposed. First of all, based on Swin Transformer's improved two-dimensional convolution downsampling module of PointPillars, the self attention mechanism can be used in the network feature extraction phase to enrich context semantics and obtain global features, and enhance the feature extraction ability of the algorithm. Second, the ground part of the point cloud is removed by using the characteristics of the point cloud column to reduce the impact of redundant point clouds, so as to improve the recognition accuracy of 3D object detection. The experimental results on the public dataset KITTI show that the proposed method has higher detection accuracy. Compared with the original PointPillars, its average detection accuracy is increased by 1.3 percentage points, which verifies the effectiveness of the proposed method.