Modeling Lane Keeping by a Hybrid Open-Closed-Loop Pulse Control Scheme

This paper presents a novel methodology for modeling human lane keeping control by characterizing a unique concept of elementary steering pulses, which are motor primitives in man-vehicle systems. The novelty of the paper is the introduction of elementary steering pulses that have been evidently extracted from naturalistic driving data through machine learning techniques (data-driven modeling), and are incorporated into an alternative steering control scheme. This newly proposed hybrid-open-closed-loop control scheme starts an elementary steering pulse with an open-loop steering actuation, representing real human's reflex responses triggered by human lane keeping errors, and adjusts it back with the traditional closed-loop control. This shows a significant improvement on both the stability and the matching performance to real driving events. Online measurement of the key metrics in the steering process provides a new tool for monitoring driver states, and the biofidelic steering model may provide human-like qualities for future automated lane keeping systems. Both will add to the array of tools available for achieving autonomous and semiautonomous driving systems, which greatly benefits the current vehicle industry.