Development and Evaluation of Two Learning-Based Personalized Driver Models for Pure Pursuit Path-Tracking Behaviors

Abstract

Establishing a personalized driver model to predict the driving behavior plays a significant role in the promotion of driver assistance system and automated driving system. In this paper, we propose two different learning-based pathtracking personalized driver models to predict the lookahead distance based on pure pursuit algorithm using the naturalistic driving data collected from BIT intelligent vehicle platform. Based on Gaussian Mixture Model (GMM), one stochastic driver model is the velocity-based Gaussian Mixture Regression (GMR) approach established by combining Gaussian classification process and Gaussian mixture regression, while another driver model is the general GMR approach. The predicting results obtained from the stochastic models are analyzed based on the numbers of GMM components. Statistical analyses show that both personalized driver models perform well, and the velocity-based GMR approach demonstrate higher accuracy than general GMR approach in predicting lookahead distance with the preferred number of the GMM components 10-12 and better performance in tracking the given path.