  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.
