Discretionary Cut-In Driving Behavior Risk Assessment Based on Naturalistic Driving Data

Abstract

Cut-in driving behavior is one of the basic micro traffic actions for vehicles. A risk assessment helps vehicles execute the behavior well and determine how to react to the same maneuver from other traffic participants when following a leading car or truck. This article presents a discretionary cut-in driving behavior risk assessment method based on field driving data and a united algorithm that is designed to a combine decision tree and a support vector machine to achieve enhanced sensitivity for the riskiest traffic conditions. To build the learning database, a wavelet method is employed to filter naturalistic driving data, incorporating the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">K</i> -means approach. An unsupervised data learning method is used to categorize the impact on vehicles in the target lane, indicated by a target vehicle’s average and maximum deceleration, into three groups. Experiment results based on self-collected and public databases show that tested vehicles are aware of the risk presented by other cars’ and trucks’ cut-in driving as well as their own impact on traffic participants in the target lane.