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Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self-Organizing Feature Mapping
Yang, Jingfeng1,2,3; Luo, Zhiyong3; Zhang, Nanfeng4; Xiao JC(肖金超)1,2; Wang, Honggang5; Zhou, Shengpei1,2; Liu, Xiaosong6; Li, Ming7,8
Department广州中国科学院沈阳自动化研究所分所
Source PublicationComplexity
ISSN1076-2787
2021
Volume2021Pages:1-8
Indexed BySCI ; EI
EI Accession number20210409813876
WOS IDWOS:000611823000007
Contribution Rank1
Funding OrganizationNational Key Research and Development Program [2017YFD0700602] ; Key Research and Development Plan of Shaanxi Province [2018ZDXM-GY-041] ; Foshan Entrepreneurship and Innovation Team Project [2017IT100032]
Abstract

With the rapid development of sensor technology for automated driving applications, the fusion, analysis, and application of multimodal data have become the main focus of different scenarios, especially in the development of mobile edge computing technology that provides more efficient algorithms for realizing the various application scenarios. In the present paper, the vehicle status and operation data were acquired by vehicle-borne and roadside units of electronic registration identification of motor vehicles. In addition, a motion model and an identification system for the single-vehicle lane-change process were established by mobile edge computing and self-organizing feature mapping. Two scenarios were modeled and tested: lane change with no vehicles in the target lane and lane change with vehicles in the target lane. It was found that the proposed method successfully identified the stochastic parameters in the process of driving trajectory simulation, and the standard deviation between simulation and the measured results obeyed a normal distribution. The proposed methods can provide significant practical information for improving the data processing efficiency in automated driving applications, for solving single-vehicle lane-change applications, and for promoting the formation of a closed loop from sensing to service.

Language英语
Citation statistics
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/28312
Collection广州中国科学院沈阳自动化研究所分所
Corresponding AuthorLuo, Zhiyong; Li, Ming
Affiliation1.Shenyang Institute of Automation Chinese (Guangzhou) 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.Huangpu Customs District Technical Center, Guangzhou 510730, China
5.School of Communications and Information Engineering, Xi'An University of Posts and Telecommunications, Xi'an 710061, China
6.Guangdong Zhongke Zhenheng Information Technology Co., Ltd, Foshan 528225, China
7.South China Agricultural University, Guangzhou 510642, China
8.Yaz Technology Co., Ltd., Guangzhou 510630, China
Recommended Citation
GB/T 7714
Yang, Jingfeng,Luo, Zhiyong,Zhang, Nanfeng,et al. Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self-Organizing Feature Mapping[J]. Complexity,2021,2021:1-8.
APA Yang, Jingfeng.,Luo, Zhiyong.,Zhang, Nanfeng.,Xiao JC.,Wang, Honggang.,...&Li, Ming.(2021).Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self-Organizing Feature Mapping.Complexity,2021,1-8.
MLA Yang, Jingfeng,et al."Stochastic Parameter Identification Method for Driving Trajectory Simulation Processes Based on Mobile Edge Computing and Self-Organizing Feature Mapping".Complexity 2021(2021):1-8.
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