Statistical Modeling of the Sensory-Perception Process of Computational Driver Models for Vehicle Simulations

Abstract

This article describes a new approach in developing computational driver models in vehicle simulations for evaluating intelligent driving support systems (IDSSs). As IDSSs become more intelligent and provide active safety controls, a more comprehensive understanding of a driver's cognitive processes is required as well as vehicle dynamics. One hundred thirty drivers participated in a psychophysical experiment of velocity estimation at 71 checkpoints on a road section. The perceived velocity, as a dependent variable, was modeled using analysis of covariance (ANCOVA) with respect to the driver's fundamental human factors (i.e., age, gender, and driving experience) as independent variables. As a result, eight different perceived velocity models were defined as part of the cognitive processes with quantitative covariates in different modalities (i.e., radius of road, level of interior noise, as well as vehicle velocity). Moreover, driver models implemented with different perceptual processes also exhibited considerable changes in the results of vehicle simulations on double lane change maneuvers. The results presented in this study suggest that defining the perceptual processes of different groups of drivers is an essential process in developing vehicle simulation environments for evaluating IDSS. Consequently, the results from this study may provide meaningful data for early stages of new vehicle design.