Safe Driving Model Based on V2V Vehicle Communication

被引:15
作者
Xie, Hua [1 ]
Wang, Yunjia [1 ]
Su, Xieyang [2 ]
Wang, Shengchun [3 ]
Wang, Liang [4 ]
机构
[1] Beijing Jiaotong Univ, Sch Elect Engn, Beijing 100044, Peoples R China
[2] NYU, Sch Engn, Brooklyn, NY 11201 USA
[3] China Acad Railway Sci Corp Ltd, Infrastruct Inspect Res Inst, Beijing 100081, Peoples R China
[4] Sun Yat Sen Univ, Sch Elect & Commun Engn, Guangzhou 510275, Guangdong, Peoples R China
来源
IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS | 2022年 / 3卷
关键词
Mathematical models; Roads; Safety; Vehicular ad hoc networks; Connected vehicles; Automobiles; Data models; Connected vehicle communication; driver behavior; FVD model; road factors; vehicle type; variable following distance;
D O I
10.1109/OJITS.2021.3135664
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Along with the rapid development of connected vehicle communication technology, describing the vehicle following driving status becomes gradually complicated. Driver behavior, vehicle type, and road factors affect vehicle speed, and the following distance reflects variability. In this paper, a nonlinear following distance model is constructed to characterize this variability. The model is based on the full speed difference model (FVD), and introduces the headway time distance coefficient, the following vehicle type coefficient, the communication advance response parameter reflecting the driver's personal characteristics, and the slope coefficient and curve curvature coefficient reflecting the road conditions, etc., and analyzes to obtain the stability conditions of the model. MATLAB is applied to numerical simulation experiments of the model, and the results show that the model can better describe the variability of following headway due to driver attributes, vehicle type, slope and curve in the connected vehicle scenario, thus providing a reference for traffic flow control and management.
引用
收藏
页码:449 / 457
页数:9
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