期刊
IEEE SENSORS JOURNAL
卷 21, 期 19, 页码 21921-21930出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSEN.2021.3079257
关键词
Laser radar; Trajectory; Tracking; Sensors; Switches; Radar tracking; Kalman filters; Roadside LiDAR; connected-vehicles; vehicle trajectory; tracking point
资金
- National Natural Science Foundation of China [52002224]
- Natural Science Foundation of Jiangsu Province [BK20200226]
- Program of Science and Technology of Suzhou [SYG202033]
- Research Program of Department of Transportation of Shandong Province [2020BZ01-03]
This paper presents an augmented vehicle tracking method utilizing roadside LiDAR data to address occlusion-induced issues in linking vehicle trajectories. Through a two-part approach, over 89% of disconnected trajectories were successfully fixed, demonstrating superior performance compared to the state-of-the-art methods.
Object occlusion is a common issue in Light Detection and Ranging (LiDAR)-based vehicle tracking technology. The occlusions can cause variance in vehicle location and speed calculation. How to link the vehicle trajectories caused by occlusion issues is a challenge for traffic engineers and researchers. This paper developed an augmented vehicle tracking method under occlusions with the roadside LiDAR data. The proposed method can be divided into two parts. The first part based on the corner point is used to choose a representative vehicle tracking point. And the second part based on the GNN algorithm is employed to link the vehicles' trajectories under two occlusion situations. The performance of the proposed method has been evaluated using roadside lidar data collected from four different scenarios. The test results showed that more than 89% of disconnected trajectories can be fixed with the proposed method, which is superior compared to the state-of-the-art method. The proposed method can benefit a lot of transportation areas, such as traffic volume count, vehicle speed tracking, and traffic safety analysis.
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