This paper proposes a 3D vehicle detection algorithm for unmanned driving systems to solve the problem of low accuracy in environmental perception based on lidar. First， according to statistical filtering and a random sampling consensus algorithm （RANSAC）， the ground point cloud segmentation was analyzed in order to eliminate the redundant points and outliers of the lidar data. Second， we improved the 3DSSD deep neural network to extract vehicle semantic and distance information from the point cloud through fusion sampling. According to the feature information， the candidate point position was adjusted twice to generate a center point. The 3D center-ness assignment strategy was adopted to create a 3D vehicle detection box. Finally， we divided the KITTI dataset into different scenes， to be used as experimental data， by comparing various current 3D vehicle detection algorithms. The experimental results showed that the proposed method could detect vehicles quickly and accurately. The average detection time was 0.12 s， and the highest detection accuracy was 89.72%.