Obstacle detection algorithm of urban rail transit based on lidar

Obstacles intruding into the boundary pose great hazards to urban rail transit, and existing communication-based train control (CBTC) technologies are unable toprovide automated protection against them. To prevent safety accidents caused by the intrusion of obstacles into the driving area, a non-contact obstacle detection algorithm was proposed byusing lidar as the main sensor. With the advantages of high measurement accuracy, long-distance detection capability, and immunity to environmental light interference, the radar-based algorithm can effectively detect obstacles within 100 meters with high reliability, and is not affected by poor lighting conditions in tunnels or other adverse conditions. To overcome the influence of slope variation in the tunnel, a point cloud calibration algorithm was first used to align the data plane with the ground plane. Then, based on the modeling analysis of the tunnel environment and platform environment, a rule-based track plane segmentation algorithm and a region-growing background point cloud segmentation algorithm were proposed to effectively separate the ground plane and filter out the background point cloud. Finally, an adaptive Euclidean clustering obstacle detection algorithm was proposed to addressthe uneven distribution of point cloud density at different distances. To verify the effectiveness of the overall algorithm, a large amount ofmainline data was collected fromNingbo Metro Line 5 for obstacle injection simulation experiments. The experimental results show that under complex operating scenarios, the obstacle detection algorithm can achieve a detection rate of 85.89% for obstacles with a size smaller than 70 m within the visual range. In the case of exceeding the visual range, the detection rate decreases to 63.08% for obstacles smaller than 120 m. The average processing time of the algorithm is 37.86 ms. © 2023, Central South University Press. All rights reserved.