A Simplified Vehicle Dynamics Model for Motion Planner Designed by Nonlinear Model Predictive Control

Abstract

Motion planning by considering it as an optimal problem is an effective and widely applicable method. Its comprehensive performance greatly depends on the vehicle dynamics model, which is highly coupled and nonlinear, especially under the dynamical scenarios and causes much more consumption of computation resources for the numerical optimization. To increase the real time performance of the motion planner designed by nonlinear model predictive control (NMPC), a unified and simplified vehicle dynamics model (SDM) is presented to make a balance between the accuracy and complexity for dynamical driving scenarios. Based on the statistical analysis results of naturalistic driving conditions, a unified nonlinear vehicle dynamics model is set up, which considers the tyre cornering characteristic and is also applicable to conditions with large turning angle. After the validation of this coupled dynamics model (CDM) by comparisons with other widely used models under a variety of conditions, the coupling effect is analyzed according to the transfer functions, which are obtained by linearizing CDM at equilibrium points. Furthermore, SDM is derived by ignoring the weak part of the coupling effect. The accuracy of SDM is validated by several comparative studies with other models and it is further applied to design a motion planner by NMPC to validate its contribution on the performance improvement under dynamical driving conditions.

Keywords

• automatic driving
• motion planning
• vehicle dynamics
• model predictive control