Open AccessCooperative driving modelling in the vicinity of traffic signals based on intelligent driver model
Author(s) -
Sun Dihua,
He Yuchu,
Zhao Min,
Cheng Senlin
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5245
Subject(s) - intelligent transportation system , fuel efficiency , computer science , simulation , traffic simulation , real time computing , floating car data , advanced driver assistance systems , automotive engineering , vehicle information and communication system , road traffic , engineering , transport engineering , traffic congestion , artificial intelligence , microsimulation
The vicinity of traffic signals is one of the most special and critical areas in the whole road system. Considering that the driver can obtain traffic signals information through two approaches: vision and the vehicle‐to‐vehicle (V2X) communication equipment in the vehicle, this study proposes two vehicle cooperative driving models that apply to the vicinity of traffic signals: the intelligent driver model (IDM) in the vicinity of traffic signals (IDM‐VT) and IDM in the vicinity of traffic signals under V2X environment (IDM‐VT‐V2X). These two models are both based on the intelligent driver model. In accordance with different situations of vehicles in the vicinity of traffic signals, such as distance from the traffic lights, whether there was another vehicle in front and different states of traffic lights, the pertinent analysis was conducted, and the cooperative driving strategy that satisfied the complicated situations of vehicle was proposed. These two models were verified and compared in the simulation experiment. The results of simulation show that when comparing with the IDM‐VT, the IDM‐VT‐V2X can reduce the average travel time and the average stop delay time by 12.98 and 98.32%, respectively, and it can also reduce 22.53% fuel consumption.