Characterization of Longitudinal Driving Behavior by Measurable Parameters

Abstract

For the design of a vehicle control algorithm that monitors and corrects longitudinal driving behavior, it is essential to have good insight into the different parameters that determine this behavior. The present research identifies 11 systems and control-related parameters for time headway, the inverse of the time to collision, and the switch time between accelerator release and brake activation. These parameters are used to determine and distinguish between dissimilar types of longitudinal driving behavior according to driving and driver characteristics. The efficient K-means clustering algorithm is used to classify longitudinal driving behavior. Driver behavior experiments were carried out with 45 participants. The results of the study show that four main determinants of longitudinal driving behavior can be distinguished by using measurable parameters with the indicated opposite extreme values: prudence (aggressive versus prudent), stability (unstable versus stable), conflict proneness (risk prone versus risk infrequent), and skillfulness (nonskillful versus skillful).