Paper Info
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.
Year
Venue
Keywords
2017
2017 AMERICAN CONTROL CONFERENCE (ACC)
Personalized model, Learning-based driver model, Gaussian mixture model, Hidden Markov model, Car-following behavior
DocType
Volume
ISSN
Conference
abs/1703.03534
0743-1619
Citations 
PageRank 
References 
0
0.34
17
Authors
5
Name
Order
Citations
PageRank
Wenshuo Wang110214.41
Ding Zhao211027.07
Junqiang Xi312814.36
David J. LeBlanc4688.83
J. Karl Hedrick569187.42