An improved driver-behavior model with combined individual and general driving characteristics

Abstract

In this paper, we propose a stochastic driver-behavior modeling framework which takes into account both individual and general driving characteristics as one aggregate model. Patterns of individual driving styles are modeled using Dirichlet process mixture model, a nonparametric Bayesian approach which automatically selects the optimal number of model components to fit sparse observations of each particular driver's behavior. In addition, general or background driving patterns are also captured with a Gaussian mixture model using a reasonably large amount of development observed data from several drivers. By combining both probability distributions, the aggregate driver-dependent model can better emphasize driving characteristics of each particular driver, while also backing off to exploit general driving behavior in cases of unmatched parameter spaces from individual training observations. The proposed driver-behavior model was employed to anticipate pedal-operation behavior during car-following maneuvers involving several drivers on the road. The experimental results showed advantages of the combined model over the adapted model previously proposed.