Naturalistic lane-keeping based on human driver data

Autonomous lane keeping is a well studied problem with several good solutions that can be found in the literature. However, naturalistic lane keeping mechanisms, in the sense of imitating human car steering, are not so common. Based on existing knowledge of human driving, this paper analyses several controllers prone to generate human-like lane keeping behavior. Using systems identification, we compare how well the control models fit with real human steering data gathered in a simulator. Experimental results points towards a parsimonious control mechanism where angles relative to the direction of the road are directly used by the driver to steer the car and keep it on the lane. This result can be used to design human like autonomous lane keeping mechanisms or to improve the design of ADAS systems adapting them to individual drivers.