Tracking Multiple Vehicles with a Flexible Life Cycle Strategy Based on Roadside LiDAR Sensors
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 150, Issue 4
Abstract
Tracking trajectories of the unconnected vehicles contributes to the improvement of traffic efficiency and safety. However, the effects of occlusions on the accuracy and reliability of the tracking results are nonnegligible. To address this issue, a modified multiple objects tracking algorithm was proposed to reduce the loss of trajectories caused by occlusions. The proposed algorithm was based on the multiple objects detection results, in which the motion states of the detected objects were determined. Further, the Kalman filter was employed to predict the trajectories, and each trajectory was uniquely matched to the detected object labeled with a tracking identity (ID) using the Hungarian algorithm. Afterward, a flexible life cycle strategy was proposed in terms of the speeds and accelerations of the detected objects, which controls the life cycle of the labeled trajectories when the occlusion occurred and guarantees the continuity of trajectories. The proposed tracking algorithm was tested on the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) data set, and the classification of events, activities, and relationships workshops, multiple object tracking (CLEAR-MOT) metrics was introduced to evaluate the performance of the proposed algorithm. The results indicated that the proposed algorithm with appropriate life cycle apparently increased the precision and accuracy and reduced the influence of occlusions on multiple objects tracking. Future work will concentrate on field implementations of the algorithm, and various scenarios and weather conditions will be taken into account.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The research reported in this paper is supported by the Open Project of Shandong Key Laboratory of Smart Transportation (preparation), the Key R&D Program of Shandong Province in China (2020CXG010118), the Natural Science Foundation of Jiangsu Province (Grant No. SBK2023041475), and the China Postdoctoral Science Foundation (Grant No. 2023M732064). The authors gratefully acknowledge their financial support.
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Information & Authors
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 4 • April 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Jun 26, 2023
Accepted: Nov 14, 2023
Published online: Feb 2, 2024
Published in print: Apr 1, 2024
Discussion open until: Jul 2, 2024
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