Evidence map›Paper›PMID 40595044›Full record

ArticleScientific reports2025

A vehicle coordinate system reconstruction method for end of line calibration applications.

Zhigang Ding, Yangyang Gong, Jincheng Lin, Jingjing Jiang, Jiadi Zhang

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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

5 authors.

Zhigang DingSchool of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou, 350118, China.
Yangyang GongSchool of Mechanical and Automotive Engineering, Fujian University of Technology, Fuzhou, 350118, China. gyy17682459343@outlook.com.
Jincheng LinXiamen Richen Technology Co., Ltd., Xiamen, 361000, China.
Jingjing JiangSchool of Transportation, Fujian University of Technology, Fuzhou, 350118, China.
Jiadi ZhangXiamen Richen Technology Co., Ltd., Xiamen, 361000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sensor calibration is a crucial step to ensure the proper functioning of advanced driver assistance systems (ADAS), and vehicle coordinate system reconstruction is an indispensable part of this process. However, the currently mainstream vehicle alignment platforms are characterized by high costs, complex structures, and immobility, making them unsuitable for the rapid upgrade and transformation pace of modern automotive manufacturers. To address these limitations, this paper proposes a low-cost, flexible solution based on extracting key feature points from the car body-in-white. First, the paper introduces the reconstruction principles based on rigid body transformation. Then, the method for selecting the necessary feature points and its general applicability are discussed. Third, the paper describes how to mitigate the influence of vehicle body color by choosing appropriate light sources, and it employs suitable algorithms to complete the feature point extraction process. Finally, validation experiments were designed to verify the accuracy of the proposed method, demonstrating that it achieves better reconstruction precision compared to the vehicle alignment platforms.

Indexed as

Machine visionMulti-sensor calibrationVehicle coordinate system reconstruction

Identifiers

PMID40595044
PMCPMC12216551

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.