A Driver Steering Model With Personalized Desired Path Generation

With the increase in driver assistance systems, driver models are becoming more important to vehicle control, driving safety, and performance. To make these driver assistance systems better cooperate with human drivers, the driver models need to be able to predict human driving behaviors and distinguish among different drivers. In this paper, a combined driver model consisting of a compensatory transfer function and an anticipatory component based on road geometry is integrated with the design of the individual driver's desired path. The proposed driver model parameters are obtained from human subject test data collected in a driving simulator. It has been shown that the proposed combined driver model is able to replicate each driver's steering wheel angle signals for a variety of maneuvers at different vehicle speeds. The driver model is then validated by first using a polynomial to interpolate the driver model and desired path parameters for an intermediate speed. It is also validated by comparing two different drivers' model parameter sets to show that each driver has a unique set of parameters. The final validation is to show that the proposed individual driver's desired path offers more accurate steering wheel fits than the previous geometric centerline.