  Semantically understanding complex drivers' encountering behavior, wherein
two or multiple vehicles are spatially close to each other, does potentially
benefit autonomous car's decision-making design. This paper presents a
framework of analyzing various encountering behaviors through decomposing
driving encounter data into small building blocks, called driving primitives,
using nonparametric Bayesian learning (NPBL) approaches, which offers a
flexible way to gain an insight into the complex driving encounters without any
prerequisite knowledge. The effectiveness of our proposed primitive-based
framework is validated based on 976 naturalistic driving encounters, from which
more than 4000 driving primitives are learned using NPBL - a sticky HDP-HMM,
combined a hidden Markov model (HMM) with a hierarchical Dirichlet process
(HDP). After that, a dynamic time warping method integrated with k-means
clustering is then developed to cluster all these extracted driving primitives
into groups. Experimental results find that there exist 20 kinds of driving
primitives capable of representing the basic components of driving encounters
in our database. This primitive-based analysis methodology potentially reveals
underlying information of vehicle-vehicle encounters for self-driving
applications.
