Data generation for connected and automated vehicle tests using deep learning models

首页 > 成果 > 详情

认领

导出

Link by DOI

反馈

作者信息关键词期刊信息基础信息归属信息摘要

成果类型：

期刊论文

作者：

Li, Ye;Liu, Fei;Xing, Lu;He, Yi;Dong, Changyin;...

通讯作者：

He, Y

作者机构：

[Li, Ye; Liu, Fei; Yuan, Chen] Cent South Univ, Sch Traff & Transportat Engn, Changsha 410075, Hunan, Peoples R China.

[Li, Ye] Changsha Univ Sci & Technol, Hunan Key Lab Smart Roadway & Cooperat Vehicle Inf, Changsha 410114, Hunan, Peoples R China.

[Xing, Lu] Changsha Univ Sci & Technol, Sch Traff & Transportat Engn, Changsha 410114, Hunan, Peoples R China.

[He, Yi; Yuan, Chen] Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China.

[He, Yi] Wuhan Univ Technol, Engn Res Ctr Transportat Safety, Minist Educ, Wuhan 430063, Peoples R China.

通讯机构：

[He, Y ] W

Wuhan Univ Technol, Intelligent Transportat Syst Res Ctr, Wuhan 430063, Peoples R China.

语种：

英文

关键词：

Connected and automated vehicles;Cooperative adaptive cruise control;Generative adversarial network;Safety evaluation;Variational autoencoder

期刊：

Accident analysis and prevention

ISSN：

0001-4575

年：

2023

卷：

190

页码：

107192

DOI：

10.1016/j.aap.2023.107192

基金类别：

Young Scientists Fund of the National Natural Science Foundation of China [72001021]; Natural Science Foundation of Hunan Province [2021JJ40746, 2021JJ40603]; Open Fund of Hunan Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems (Changsha University of Science amp; Technology) [kfj220701]

机构署名：

本校为其他机构

院系归属：

交通运输工程学院

摘要：

For the simulation-based test and evaluation of connected and automated vehicles (CAVs), the trajectory of the background vehicle has a direct effect on the performance of CAVs and experiment outcomes. The collected real trajectory data are limited by the sample size and diversity, and may exclude critical attribute combinations that are of vital importance for CAVs' tests. Consequently, it is indispensable to increase the richness of accessible trajectory data. In this study, we developed the Wasserstein generative adversarial network with gradient penalty (WGAN-GP) and a hybrid model of vari...

反馈

产权有误：本人成果被他人认领

数据有误：数据基本信息有误

归属有误：成果的院系归属、机构署名归属有误

其他原因：

验证码：

看不清楚，换一个

确定

取消

成果认领

标题：

用户	作者	通讯作者	--
	请选择	请选择	--

确定

取消

Data generation for connected and automated vehicle tests using deep learning models

反馈

成果认领

提示

该栏目需要登录且有访问权限才可以访问