A Driver Model Using Optic Information for Longitudinal and Lateral Control of a Long Vehicle Combination

Paper in proceeding, 2014

High driver acceptance is believed to be an important aspect when introducing automated driving functionalities for prospective long vehicle combinations. The main hypothesis of this paper is that high driver acceptance can be realized by utilizing driver models inspired by human cognition as an integrated part of such functions. It is envisioned that the human driver will more easily understand, and trust, a system that behaves in a human-like manner. In the study of a combined retardation and lane-change scenario, a driver model based on optic information was used, together with a single track vehicle model, to control the steering and retardation of a simulated vehicle. The parameters of the driver model’s lateral behavior were estimated using driving data measured from an A-double combination during actual lane-changes. Numerical simulations showed that the driver model was able to generate safe and conservative deceleration and steering for the studied scenario. In future work for automated functionalities, the combined driver and vehicle model could be used when evaluating different tentative plans for lane changes, in real time.