Real-time driver characterization during car following using stochastic evolving models

摘要

This paper studies characterizing the driving behavior during steady-state and transient car-following. An approach utilizing the online learning of an evolving Takagi-Sugeno fuzzy model that is combined with a probabilistic model is applied to capture the multi-model and evolving nature of the driving behavior. The approach is validated by testing on a vehicle during different driving conditions.