In this study, a 3D point-cloud target detection algorithm for vehicles based on attention mechanism is proposed for the recognition and positioning of the targets in autonomous driving scenarios. The algorithm first divides the sparse and disordered point cloud space into equidistant and regular voxel representations. Then, 3D sparse convolution and auxiliary network are used to synchronously extract the internal point cloud features from all voxels. Afterward, a bird’s-eye view is generated. After the internal 3D point cloud features are converted into a 2D bird’s-eye view, the spatial feature information of the target will be lost generally, which makes the final detection result and the direction prediction unsatisfactory. To further extract the feature information of the bird’s-eye view, this study also proposes an attention mechanism module, which contains two attention models and adopts a three-dimensional layout structure (front, middle, and back) to realize amplification and suppression of the feature information of the bird’s-eye view. The convolutional neural network and PS-Warp transformation mechanism are employed to perform 3D target detection on the processed bird’s-eye view. Experiments show that, under the premise of ensuring real-time detection efficiency, this algorithm has better direction prediction and higher detection accuracy than existing algorithms.