Understanding V2V Driving Scenarios Through Traffic Primitives

Abstract

Understanding driver interaction behavioral semantics has potential benefits to autonomous car’s decision-making design. This article presents a framework of analyzing various encountering behaviors through decomposing driving encounter sequential data into small building blocks, called traffic primitives, using a Bayesian nonparametric learning (BNPL) approach. This framework offers a flexible way to gain semantic insights into complex driving encounters without any prerequisite knowledge of interaction behavior categories. Its effectiveness is then validated using 976 naturalistic driving encounters from which more than 4000 traffic primitives were learned with the BNPL approach. After that, a dynamic time warping method integrated with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -means clustering is then developed to cluster all these extracted traffic primitives into groups. Experimental results identify 20 kinds of traffic primitives capable of representing the essential components of driving encounters in our database. Based on the results, we conclude that the proposed primitive-based analysis could prove useful for autonomous vehicle applications.