Abstract

This paper presents a novel methodology for mod- elling human lane keeping control by characterizing a unique concept of elementary steering pulses, which are motor primi- tives 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 modelling), and are incorporated into an alternative steering control scheme. This newly proposed hybrid- open-closed- loop (HOCL) control scheme, where an elementary steering pulse starts with an open loop, representing real human’s reflex responses triggered by human lane keeping errors, and is adjusted back with the traditional close-loop control, has shown a significant improvement on both the stability and 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 semi-autonomous driving systems, which greatly benefits the current vehicle industry.