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

Qiyang Luo, Zhenyu Xu, Yibin Zhang, Morris Igene, Tamer Bataineh, Mohammad Soltanirad, Keshav Jimee, Hongchao Liu

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Occlusion-Aware Trajectory Discontinuity Correction for Roadside LiDAR Using Time-Space Analysis.Sensors (Basel, Switzerland) · 2026
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Qiyang LuoDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.ORCID 0000-0003-3318-0004

Zhenyu XuDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.ORCID 0009-0002-6724-9807

Yibin ZhangDepartment of Construction and Transportation Engineering, Ningbo University of Technology, Ningbo 315211, China.ORCID 0000-0002-1165-7783

Morris IgeneDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.ORCID 0000-0003-4055-2750

Tamer BatainehDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.ORCID 0009-0003-3338-1890

Mohammad SoltaniradDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.ORCID 0009-0003-6148-3165

Keshav JimeeDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.ORCID 0000-0001-7140-5927

Hongchao LiuDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

full occlusionlimited datapointsroadside LiDARtrajectory repair

Identifiers

PMID40006344

DOI10.3390/s25041114

PMCPMC11859876

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.