Evolving Markov chain models of driving conditions using onboard learning

This paper describes simple and suitable for real-time implementation algorithms for on-board learning of Markov Chain models of driving conditions (e.g., driver wheel torque request, vehicle speed, surrounding traffic speed, road grade, road curvature etc.). The use of Kullback-Liebler (KL) divergence is proposed as a stopping and re-initialization criterion for learning, permitting an evolving set of Markov Chain models to be generated for different route segments. Examples based on learning models of road grade and vehicle speed are reported. Assuming that a set of learned Markov Chain models and of associated control policies is available onboard of the vehicle, the use of KL divergence is also advocated for selecting the control policy that matches the current driving conditions. Potential applications of this approach include optimal energy management in Hybrid Electric Vehicles (HEV) and fuel efficient Adaptive Cruise Control.