A Motivational Driver Steering Model: Task Difficulty Homeostasis From Control Theory Perspective

Abstract

A general and psychologically plausible collision avoidance driver model can improve transportation safety significantly. Most computational driver models found in the literature have used control theory methods only, and they are not established based on psychological theories. In this paper, a unified approach is presented based on concepts taken from psychology and control theory. The "task difficulty homeostasis theory", a prominent motivational theory, is combined with the "Lyapunov stability method" in control theory to present a general and psychologically plausible model. This approach is used to model driver steering behavior for collision avoidance. The performance of this model is measured by simulation of two collision avoidance scenarios at a wide range of speeds from 20 km/h to 170 km/h. The model is validated by experiments on a driving simulator. The results demonstrate that the model follows human behavior accurately with a mean error of 7 percent.