Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR

Object occlusion is a common challenge in roadside LiDAR-based vehicle tracking. This issue can cause variances in vehicle location and speed calculations. This paper proposes a vehicle tracking post-processing method designed to handle full occlusion and limited datapoint conditions. The first part...

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Main Authors: Qiyang Luo, Zhenyu Xu, Yibin Zhang, Morris Igene, Tamer Bataineh, Mohammad Soltanirad, Keshav Jimee, Hongchao Liu
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/25/4/1114
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author Qiyang Luo
Zhenyu Xu
Yibin Zhang
Morris Igene
Tamer Bataineh
Mohammad Soltanirad
Keshav Jimee
Hongchao Liu
author_facet Qiyang Luo
Zhenyu Xu
Yibin Zhang
Morris Igene
Tamer Bataineh
Mohammad Soltanirad
Keshav Jimee
Hongchao Liu
author_sort Qiyang Luo
collection DOAJ
description Object occlusion is a common challenge in roadside LiDAR-based vehicle tracking. This issue can cause variances in vehicle location and speed calculations. This paper proposes a vehicle tracking post-processing method designed to handle full occlusion and limited datapoint conditions. The first part of the method focuses on linking the disconnected trajectories of the same vehicle caused by full occlusion. The second part refines the vehicle representative point to enhance tracking accuracy. Performance evaluation demonstrates that the proposed method can detect and reconnect the trajectories of the same vehicle, even under prolonged full occlusion. Moreover, the refined vehicle representative point provides more stable speed estimates, even with sparse datapoints. This significantly increases the effective detection range of roadside LiDAR. This approach lays a strong foundation for the application of roadside LiDAR in emission analysis and near-crash studies.
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spelling doaj-art-1ee7af78f455434c8ffa8a2a6724f70d2025-08-20T02:44:33ZengMDPI AGSensors1424-82202025-02-01254111410.3390/s25041114Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDARQiyang Luo0Zhenyu Xu1Yibin Zhang2Morris Igene3Tamer Bataineh4Mohammad Soltanirad5Keshav Jimee6Hongchao Liu7Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Construction and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, ChinaDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USAObject occlusion is a common challenge in roadside LiDAR-based vehicle tracking. This issue can cause variances in vehicle location and speed calculations. This paper proposes a vehicle tracking post-processing method designed to handle full occlusion and limited datapoint conditions. The first part of the method focuses on linking the disconnected trajectories of the same vehicle caused by full occlusion. The second part refines the vehicle representative point to enhance tracking accuracy. Performance evaluation demonstrates that the proposed method can detect and reconnect the trajectories of the same vehicle, even under prolonged full occlusion. Moreover, the refined vehicle representative point provides more stable speed estimates, even with sparse datapoints. This significantly increases the effective detection range of roadside LiDAR. This approach lays a strong foundation for the application of roadside LiDAR in emission analysis and near-crash studies.https://www.mdpi.com/1424-8220/25/4/1114roadside LiDARfull occlusionlimited datapointstrajectory repair
spellingShingle Qiyang Luo
Zhenyu Xu
Yibin Zhang
Morris Igene
Tamer Bataineh
Mohammad Soltanirad
Keshav Jimee
Hongchao Liu
Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR
Sensors
roadside LiDAR
full occlusion
limited datapoints
trajectory repair
title Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR
title_full Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR
title_fullStr Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR
title_full_unstemmed Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR
title_short Vehicle Trajectory Repair Under Full Occlusion and Limited Datapoints with Roadside LiDAR
title_sort vehicle trajectory repair under full occlusion and limited datapoints with roadside lidar
topic roadside LiDAR
full occlusion
limited datapoints
trajectory repair
url https://www.mdpi.com/1424-8220/25/4/1114
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