Contextual on-board learning and prediction of vehicle destinations

This paper deals with the problem of on-board learning of typical stop locations and the prediction of the vehicle destination. Such a learning and prediction procedure is used to summarize the stop locations, estimate the frequent destinations, and learn the driver´s decision model of selecting the next destinations under different conditions. The prediction of the driver´s usage pattern is useful in generating optimal control policies for energy management control in electrified vehicles. The proposed approach is based on the real-time clustering and learning of a decision model that combines fuzzy and Markov models. The former is applied to represent possibilistically the context of the destination selection while the latter covers the probabilistic process of choosing a destination for given conditions.