Evidence map›Paper›PMID 39066122›Full record

ArticleSensors (Basel, Switzerland)2024

Research on Vehicle Pose Detection Method Based on a Roadside Unit.

Juan Ni, Xiangcun Kong, Bingchen Yan, Shuzhe Si, Shuyue Shi, Dong Guo, Pengwei Wang, Lei Wang, Yi Xu

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2024. 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.

  1. Article
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

9 authors.

Juan NiSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Xiangcun KongSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Bingchen YanZibo Environmental Pollution Control and Prevention Center, Zibo 255000, China.
Shuzhe SiCestera Motor Co., Ltd., Zibo 255000, China.
Shuyue ShiSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Dong GuoSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Pengwei WangSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.
Lei WangCestera Motor Co., Ltd., Zibo 255000, China.
Yi XuSchool of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China.

Funding

Experiment technology upgrading project 2022003Intelligent Automotive Sensor Technology Graduate Project 4053223015National Natural Science Foundation of China 52102465Natural Science Foundation of Shandong Province ZR2022MF230Shandong Province Major Science and Technology Innovation Project 2023CXGC010111Shandong Province Youth Innovation Team Plan for Universities 2023KJ153Shandong Provincial Program of Introducing and Cultivating Talents of Discipline to Universities 4012322006Small and Medium-sized Enterprise Innovation Capability Improvement Project 2022TSGC2277
6 · The paper itself

Abstract

Vehicle pose detection plays a vital role in modern automotive technology, which can improve driving safety, enhance vehicle stability and provide important support for the development of autonomous driving technology. The current pose estimation methods have the problems of accumulation errors, large algorithm computing power, and expensive cost, so they cannot be widely used in intelligent connected vehicles. This paper proposes a vehicle pose detection method based on an RSU (Roadside Unit). First, the on-board GPS performs the positioning of the target vehicle and transmits the positioning information to the RSU through the UDP (User Data Protocol). Next, the RSU transmits a forward command to the OBU (On-board Unit) through the UDP. The OBU sends the command to the ECU (Electronic Control Unit) to control the vehicle forward. Then, the RSU detects and tracks the vehicle. The RSU takes pictures of two images before and after the movement and obtains the coordinates of the four angle points and the center point by image processing. The vehicle heading direction is determined by the moving direction of the center point of the front and rear two images. Finally, the RSU captures the vehicle images in real time, performs the process of tracking, rectangular fitting and pose calculation to obtain the pose information and transmits the information to the OBU to complete the whole process of vehicle pose detection and information transmission. Experiments show that the method can realize accurate and efficient detection of vehicle pose, meet the real-time requirements of vehicle pose detection, and can be widely used in intelligent vehicles.

Indexed as

pose detectionRSUUDPvehicle detection

Identifiers

PMID39066122
PMCPMC11280798

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.