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Semantically understanding complex drivers' encountering behavior, whereintwo or multiple vehicles are spatially close to each other, does potentiallybenefit autonomous car's decision-making design. This paper presents aframework of analyzing various encountering behaviors through decomposingdriving encounter data into small building blocks, called driving primitives,using nonparametric Bayesian learning (NPBL) approaches, which offers aflexible way to gain an insight into the complex driving encounters without anyprerequisite knowledge. The effectiveness of our proposed primitive-basedframework is validated based on 976 naturalistic driving encounters, from whichmore 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-meansclustering is then developed to cluster all these extracted driving primitivesinto groups. Experimental results find that there exist 20 kinds of drivingprimitives capable of representing the basic components of driving encountersin our database. This primitive-based analysis methodology potentially revealsunderlying information of vehicle-vehicle encounters for self-drivingapplications.

Understanding V2V Driving Scenarios through Traffic Primitives