With the rapid rise of intelligent and connected vehicles (CVs), the traffic flow becomes more complex, and the accurate description of the microscopic behavior of the vehicle is crucial for studying the mixed traffic flow.
This study firstly analyzes and explores the vehicle’s front-wheel dynamics, and a front-wheel steering model (FWSM) is proposed to describe the vehicle’s lateral motion. In addition, a two-dimensional kinematic intelligent driver model (2D-KIDM) is developed to predict the vehicle’s two-dimensional movement considering the intelligent driver model (IDM) characteristics, vehicular dynamics, and the FWSM. The effectiveness of the proposed 2D-KIDM is evaluated with various simulations in realistic scenarios from the highD dataset. Dynamic time warping (DTW) and some common indexes are also used to analyze the error. The results show that the proposed 2D-KIDM can accurately describe the vehicle’s two-dimensional movement and predict future values of vehicle parameters, such as velocity, position, front-wheel steering angle, and heading angle.