Citation

Guan W, Chen H, Li X, Li H, You X, Moridpour S. J. Adv. Transp. 2022; 2022: e8436388.

Copyright

(Copyright © 2022, Institute for Transportation, Publisher John Wiley and Sons)

DOI

10.1155/2022/8436388

PMID

unavailable

Abstract

Driving speeds are higher on freeways, and the visibility reduction occurring in freeway fog zones often increases traffic accidents. This study aims at assessing the impact of different levels of connected vehicle fog warning systems on driving behavior and safety. A connected vehicle fog warning system is developed based on driving simulators, and virtual scenes are developed based on fog zones. The connected vehicle technology includes three levels: a normal level, a level including a human-machine interface, and a level with both a human-machine interface and dynamic message signs. Speed and lateral deviation are chosen as assessment indicators and combined with sample entropy to evaluate the impact of the connected vehicle fog warning system on safety. The deceleration ratio of the warning point is used to evaluate the efficiency of the connected vehicle fog warning system.

RESULTS show that the connected vehicle fog warning system can significantly reduce driving speed, and that there are differences in the speed-reduction effectiveness for different technical levels. The connected vehicle fog warning system can reduce the lateral deviation and improve the lateral driving safety. From the perspective of change stability, speed safety entropy and lateral deviation safety entropy are increased, which indicates that the connected vehicle fog warning system will negatively impact safety because of the additional workload. Drivers’ responses are more pronounced in the human-machine interface group compared to the group with dynamic message signs, where the drivers maintained a lower speed. This study provides a reference for the studies on connected vehicle technology based on driving simulators and supports the optimization, design, and evaluation of connected vehicle fog warning systems.

Language: en