Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

Jingwei Cao,Silun Peng,Shixin Song,Xu Zhang,Chuanxue Song,Feng Xiao

doi:10.1109/access.2020.2982963

Abstract

Trajectory tracking control is a key technology in the research and development of autonomous vehicles. With the aim of addressing problems such as low control accuracy and poor real-time performance, which can occur easily when an autonomous vehicle avoids obstacles, this research focuses on the trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics. First, the vehicle dynamics model and tire model are established through appropriate simplification. Then, based on the basic principle of model predictive control, a linear time-varying model predictive controller (LTV MPC) that considers the cornering characteristics is designed and optimized. Finally, using CarSim and MATLAB/Simulink software, a joint simulation model is established and the trajectory tracking performance of the controlled vehicle under different vehicle speeds and road adhesion conditions are tested through simulation experiments in combination with the double-shift line reference trajectory. The simulation results show the LTV MPC controller that considers cornering characteristics has good self-adaptability under complicated and severe working conditions, and no cases, such as car sideslip or track departure, were observed. Compared with other controllers and algorithms, the designed trajectory tracking controller has remarkable comprehensive performance, exhibits superior robustness and anti-interference ability, and significant improvements in the trajectory tracking control accuracy and real-time performance. The proposed control algorithm is of great importance in improving the tracking stability and driving safety of autonomous vehicles under complex extreme conditions and conducive to the further development and improvement of the technological level of intelligent vehicle driving assistance.

Highlights

The yearly increase in car ownership has greatly facilitated people’s daily travel
This work is divided mainly into four steps: (1) Through appropriate simplification, establish vehicle dynamics model and tire model respectively; (2) Based on the basic principle of model predictive control, a linear time-varying model predictive controller (LTV Model predictive control (MPC)) that considers the cornering characteristics is designed and optimized; (3) Using CarSim and MATLAB/Simulink software, a joint simulation model is established, and the trajectory tracking performance of the controlled vehicle under different vehicle speeds and different road adhesion conditions is tested through simulation experiments in combination with the double-shift line reference trajectory; (4) According to the simulation results, the controller performance is comprehensively compared and analyzed
This paper focuses on the trajectory tracking control algorithm for autonomous vehicle considering cornering characteristics, so that the vehicle can still quickly and smoothly track the desired path in the harsh conditions, mainly involving the longitudinal, lateral and yaw movement of the vehicle

Summary

Introduction

The yearly increase in car ownership has greatly facilitated people’s daily travel. it has caused increasingly serious traffic safety issues. This work is divided mainly into four steps: (1) Through appropriate simplification, establish vehicle dynamics model and tire model respectively; (2) Based on the basic principle of model predictive control, a linear time-varying model predictive controller (LTV MPC) that considers the cornering characteristics is designed and optimized; (3) Using CarSim and MATLAB/Simulink software, a joint simulation model is established, and the trajectory tracking performance of the controlled vehicle under different vehicle speeds and different road adhesion conditions is tested through simulation experiments in combination with the double-shift line reference trajectory; (4) According to the simulation results, the controller performance is comprehensively compared and analyzed.

Results

Conclusion