为了精确地识别出交通场景下的目标障碍物，考虑到真实道路场景的复杂性和道路安全的重要性，以稀疏嵌入卷积检测（sparsely embedded convolutional detection, SECOND）模型作为基础模型，通过采取自注意力机制获得全局语义信息来增强点云表征能力，采用感兴趣区域（region of interest, RoI）检测头对候选区域生成的三维建议框进行优化，提升其检测精度方法，提出了一种基于自注意力机制的两阶段三维目标检测方法SAR-SECOND检测模型。结果表明：与现有的先进三维目标检测方法相比，SAR-SECOND在KITTI数据集上的检测精度与之不相上下，汽车整体检测精度为82.28%；行人整体精度为51.45%，骑行者整体精度为72.41%。检测结果验证了该方法的有效性。

In order to accurately identify the target obstacle in the traffic scene, considering the complexity of the real road scene and the importance of road safety, taking the sparsely embedded convolutional detection(SECOND) model as the basic model, the point cloud representation ability was enhanced by using the self-attention mechanism to obtain global semantic information, and the region of interest (RoI) detection head was used to optimize the 3D suggestion box generated by the candidate region to improve its detection accuracy. Then, two-stage 3D object detection method based on self-attention mechanism named SAR-SECOND was proposed. The results show that compared with the existing advanced 3D object detection methods, SAR-SECOND overall detection accuracy of car is 82.28%, the overall accuracy of pedestrians is 51.45%, and the overall accuracy of the rider is 72.41%. It can be seen that the effectiveness of this method is effective.