This paper is verified on the Apollo data set. The continuous road live screenshots are extracted from the data set to obtain the required set of time-series pictures, and the targets in the images are detected and tracked. Finally, the experiments show that the algorithm in this paper has an obvious effect on solving the occlusion problem. This paper has also been tested on the actual road, and the effect of medium and long-distance vehicle detection is good. The experiment shows that the algorithm can meet the real-time detection requirements under the actual road conditions.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.