@inproceedings{9e1c7ab9af0a4c828f294b32f86f5bfd,
title = "Development and evaluation of two learning-based personalized driver models for car-following behaviors",
abstract = "Personalized driver models play a key role in the development of advanced driver assistance systems and automated driving systems. Traditionally, physical-based driver models with fixed structures usually lack the flexibility to describe the uncertainties and high non-linearity of driver behaviors. In this paper, two kinds of learning-based car-following personalized driver models were developed using naturalistic driving data collected from the University of Michigan Safety Pilot Model Deployment program. One model is developed by combining the Gaussian Mixture Model (GMM) and the Hidden Markov Model (HMM), and the other one is developed by combining the Gaussian Mixture Model (GMM) and Probability Density Functions (PDF). Fitting results between the two approaches were analyzed with different model inputs and numbers of GMM components. Statistical analyses show that both models provide good performance of fitting while the GMM-PDF approach shows a higher potential to increase the model accuracy given a higher dimension of training data.",
keywords = "Car-following behavior, Gaussian mixture model, Hidden Markov model, Learning-based driver model, Personalized model",
author = "Wenshuo Wang and Ding Zhao and Junqiang Xi and Leblanc, {David J.} and Hedrick, {J. Karl}",
note = "Publisher Copyright: {\textcopyright} 2017 American Automatic Control Council (AACC).; 2017 American Control Conference, ACC 2017 ; Conference date: 24-05-2017 Through 26-05-2017",
year = "2017",
month = jun,
day = "29",
doi = "10.23919/ACC.2017.7963105",
language = "English",
series = "Proceedings of the American Control Conference",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1133--1138",
booktitle = "2017 American Control Conference, ACC 2017",
address = "United States",
}