A Probabilistic Approach to Measuring Driving Behavior Similarity With Driving Primitives

Abstract

Evaluating the similarity levels of driving behavior plays a pivotal role in driving style classification and analysis, thus benefiting the design of human-centric driver assistance systems. This article presents a novel framework capable of quantitatively measuring the similarity of driving behaviors for human based on driving primitives, i.e., the building blocks of driving behavior. To this end, we develop a Bayesian nonparametric method by integrating hierarchical Dirichlet process (HDP) with a hidden Markov model (HMM) in order to automatically extract the driving primitives from sequential observations without using any prior knowledge. Then, we propose a grid-based relative entropy approach, which allows quantifying the probabilistic similarity levels among these extracted primitives. Finally, the naturalistic driving data from 10 drivers are collected to evaluate the proposed framework, with comparison to traditional work. Experimental results demonstrate that the proposed probabilistic framework based on driving primitives can provide a quantitative measurement of similar levels of driving behavior associated with the dynamic and stochastic characteristics.