@article{ref1,
title="A car-following driver model capable of retaining naturalistic driving styles",
journal="Journal of advanced transportation",
year="2020",
author="Hu, Jie and Luo, Sheng",
volume="2020",
number="",
pages="e6520861-e6520861",
abstract="The modeling of car-following behavior is an attractive research topic in traffic simulation and intelligent transportation. The driver plays an important role in car following but is ignored by most car-following models. This paper presents a novel car-following driver model, which can retain aspects of human driving styles. First, simulated car-following data are generated by using the speed control driver model and the real-world driving behavior data if the real-world car-following data are not available. Then, the car-following driver model is established by imitating human driving maneuver during real-world car following. This is accomplished by using a neural network-based learning control paradigm and car-following data. Finally, the FTP-72 driving cycle is borrowed as the speed profile of the leading vehicle for the model test. The driving style is quantitatively analyzed by A<sub>esd</sub>. The results show that the proposed car-following driver model is capable of retaining the naturalistic driving styles while well accomplishing the car-following task with the error of relative distance mostly less than 5 meters for every driving styles.<p /> <p>Language: en</p>",
language="en",
issn="0197-6729",
doi="10.1155/2020/6520861",
url="http://dx.doi.org/10.1155/2020/6520861"
}