Abstract: In the field of automatic driving target tracking, there is a problem that the target occlusion will cause the loss of feature points, resulting in the loss of tracking targets. In this paper, a multi-target tracking algorithm combining spatial mask prediction and point cloud projection is proposed to reduce the adverse effects of the occlusion. Firstly, the temporal image data is processed by an example segmentation mask extraction model, and the basic mask data is obtained. Secondly, the obtained mask data is input into the tracker, the mask output of subsequent sequence images is obtained through the prediction model, and the verifier is used for a comparative analysis to obtain an accurate target tracking output. Finally, the obtained 2D target tracking data is projected into the corresponding point cloud image to obtain the final 3D target tracking point cloud image. In this paper, simulation experiments are carried out on multiple data sets. The experimental results show that the tracking effect of this algorithm is better than other similar algorithms. In addition, this paper is also tested on the actual road, and the vehicle detection accuracy reaches 81.63%. The results verify that the algorithm can also meet the real-time requirements of target tracking under the actual road conditions.