Kinematic and dynamic vehicle models for autonomous driving control design

TLDR

The study investigates using kinematic and dynamic vehicle models for model‑based control design in autonomous driving. The authors analyze forecast error statistics and discretization effects of kinematic and dynamic models, then use these insights to design an MPC controller based on a simple kinematic bicycle model. The proposed MPC approach is computationally cheaper than tire‑model methods, remains effective at low speeds where tire models fail, and performs well experimentally across speeds on windy roads.

Abstract

We study the use of kinematic and dynamic vehicle models for model-based control design used in autonomous driving. In particular, we analyze the statistics of the forecast error of these two models by using experimental data. In addition, we study the effect of discretization on forecast error. We use the results of the first part to motivate the design of a controller for an autonomous vehicle using model predictive control (MPC) and a simple kinematic bicycle model. The proposed approach is less computationally expensive than existing methods which use vehicle tire models. Moreover it can be implemented at low vehicle speeds where tire models become singular. Experimental results show the effectiveness of the proposed approach at various speeds on windy roads.