Identification of human driver models in car following

Abstract

This paper presents the development of two new mathematical models of driver behavior in single-lane car following situations. Optimum parameter values in each of these models were obtained using standard parameter identification algorithms. The basic approach to model development was to derive the model structure using optimal control theory. The problem was formulated as a model-tracking problem and a quadratic cost function was minimized. Model parameters were optimized by comparing model behavior with freeway data obtained on Interstate 71 in Ohio by Clear and Treiterer. The results indicate that the models fit the data very well during acceleration and deceleration phases, but not during constant velocity regions. To obtain a better fit during transitions between acceleration and deceleration phases, a second model was postulated based on the hypothesis that the driver tracks not only the car directly in front of him, but also cars directly ahead of the lead car. An adaptive controller structure with a mode switching algorithm has been derived and its parameters optimized.