Research on Obstacle Detection and Tracking of Autonomous Vehicles Based on the Fusion of Vision Camera and LiDAR

doi:10.3901/JME.2025.02.296

Abstract

Abstract: To improve the accuracy and stability of obstacle detection and tracking, depthwise separable atrous spatial pyramid pooling(DASPP) layer and weighted boxes fusion(WBF) algorithm are firstly introduced into you only look once version 5(YOLO v5) to tackle the problems of loss of semantic information and candidate box information, respectively. Then, a two-stage point cloud clustering method considering the point cloud distance and the continuity of the outer contour is proposed and a bounding box is established to improve the clustering accuracy of each target while ensuring the recall rate of obstacle targets. Finally, the convolutional block attention module(CBAM) is added into MobileNet to effectively extract the visual features of the obstacle target, visual features and 3D information are combined to establish correlation metrics and thus to improve tracking precision. Tests based on KITTI dataset and real environments show the effectiveness and transferability of the proposed algorithm.

Key words: vision camera, LiDAR, object detection, multi-object tracking, autonomous vehicle

CLC Number:

U463

WEI Chao, WU Xitao, ZHU Gengting, SHU Yongjie, LI Luxing, SUI Shuxin. Research on Obstacle Detection and Tracking of Autonomous Vehicles Based on the Fusion of Vision Camera and LiDAR[J]. Journal of Mechanical Engineering, 2025, 61(2): 296-309,320.

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