SIA OpenIR  > 广州中国科学院沈阳自动化研究所分所
Numerical Calibration Method for Vehicle Velocity Data from Electronic Registration Identification of Motor Vehicles Based on Mobile Edge Computing and Particle Swarm Optimization Neural Network
Yang, Jingfeng1,2; Luo, Zhiyong3; Zhang, Nanfeng4; Wang, Honggang5; Li, Ming6,7; Xiao JC(肖金超)1,2
Department广州中国科学院沈阳自动化研究所分所
Source PublicationCOMPLEXITY
ISSN1076-2787
2020
Volume2020Pages:1-13
Indexed BySCI ; EI
EI Accession number20204109320299
WOS IDWOS:000571903000001
Contribution Rank1
Funding OrganizationNational Key Research and Development Program [2017YFD0700602, 2018YFB2003500, 2018YFB1700200] ; Key Research and Development Plan of Shaanxi Province [2018ZDXM-GY-041]
Abstract

In the development of technology for smart cities, the installation and deployment of electronic motor vehicle registration identification have attracted great attention in terms of smart transportation in recent years. Vehicle velocity measurement is one of the fundamental data collection efforts for motor vehicles. The velocity detection using electronic registration identification of motor vehicles is constrained by the detection algorithm, the material of the automobile windshield, the placement of the decals, the installation method of the signal reader, and the angle of the antenna. The software and hardware for electronic motor vehicle registration identification produced in the standard manner cannot meet the accuracy of velocity detection for all scenarios. Based on the actual application requirements, we propose a calibration method for the numerical output of the automobile velocity detector based on edge computing of the optimized multiple reader/writer velocity values and based on a particle swarm-optimized radial basis function (RBF) neural network. The proposed method was tested on a two-way eight-lane road, and the test results showed that it can effectively improve the accuracy of velocity detection using electronic registration identification of motor vehicles. Compared with the actual velocity, 87.12% of all the data samples had an error less than 5%, and 91.76% of the data samples for vehicles in the center lane had an error less than 5%. By calibrating the electronic vehicle velocity based on the registration identification, the accuracy of velocity detection in different application environments can be improved. Moreover, the method can establish an accurate foundation for application in traffic flow management, environmental protection, traffic congestion fee collection, and special vehicle traffic management.

Language英语
WOS SubjectMathematics, Interdisciplinary Applications ; Multidisciplinary Sciences
WOS KeywordFRAMEWORK ; ALGORITHM
WOS Research AreaMathematics ; Science & Technology - Other Topics
Funding ProjectNational Key Research and Development Program[2017YFD0700602] ; National Key Research and Development Program[2018YFB2003500] ; National Key Research and Development Program[2018YFB1700200] ; Key Research and Development Plan of Shaanxi Province[2018ZDXM-GY-041]
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27686
Collection广州中国科学院沈阳自动化研究所分所
Corresponding AuthorLi, Ming; Xiao JC(肖金超)
Affiliation1.Shenyang Institute of Automation (Guangzhou) Chinese Academy of Sciences, Guangzhou 511458, China
2.Shenyang Institute of Automation Chinese Academy of Sciences, Shenyang 110016, China
3.School of Electronics and Communication Engineering, Sun Yat-Sen University, Guangzhou 510006, China
4.Technical Center of Huangpu Customs District China, Guangzhou 510730, China
5.School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710061, China
6.South China Agricultural University, Guangzhou 510642, China
7.Yaz Technology Co., Ltd., Guangzhou 510630, China
Recommended Citation
GB/T 7714
Yang, Jingfeng,Luo, Zhiyong,Zhang, Nanfeng,et al. Numerical Calibration Method for Vehicle Velocity Data from Electronic Registration Identification of Motor Vehicles Based on Mobile Edge Computing and Particle Swarm Optimization Neural Network[J]. COMPLEXITY,2020,2020:1-13.
APA Yang, Jingfeng,Luo, Zhiyong,Zhang, Nanfeng,Wang, Honggang,Li, Ming,&Xiao JC.(2020).Numerical Calibration Method for Vehicle Velocity Data from Electronic Registration Identification of Motor Vehicles Based on Mobile Edge Computing and Particle Swarm Optimization Neural Network.COMPLEXITY,2020,1-13.
MLA Yang, Jingfeng,et al."Numerical Calibration Method for Vehicle Velocity Data from Electronic Registration Identification of Motor Vehicles Based on Mobile Edge Computing and Particle Swarm Optimization Neural Network".COMPLEXITY 2020(2020):1-13.
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