Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

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. INDEX TERMS Driving assistance, autonomous vehicle, model predictive control, cornering characteristics, trajectory tracking control.

A Review of Tracking and Trajectory Prediction Methods for Autonomous Driving

“…Furthermore, results focusing on tracking-while-driving problems may opt for a vehicle behavior model, or a kinematic model, as opposed to one that is based on appearance criteria. Examples of such approaches are [75][76][77], where the authors build models of vehicle behavior from parameters such as steering angles, headings, offset distances, and relative positions. Note that kinematic and motion models are generally more suited to situations where the input consists in data from radar, Light Detection and Ranging (LiDAR) or Global Positioning Systems (GPS), as opposed to image sequences.…”

“…-traditional, classic methods do not model sequence dependencies as effectively as many RNN-based solutions Models that represent and predict actor relationships using flow-networks and graphs [83,85,89,90] Models relying on geometric representations, kinematics and pose estimations [74,75,77,91] Models that ensure detection coherence using adaptive partitioning of the problem space [71,86] Methods relying on Markov models and Markov decision processes [66][67][68] Methods that build appearance models and/or use appearance similarity metrics [72,73,87,88] Methods using a multi-stage tracking pipeline incorporating filtering, segmentation, clustering and/or data association [76,78] Methods relying on lightweight filtering and optimization for high-speed high-performance applications [63,64,80,84]…”

Preview Control with Dynamic Constraints for Autonomous Vehicles

“…The demands of traffic safety as well as the advances in sensing technology arouse researchers’ attention on autonomous vehicles and facilitate the development of this field [ 1 , 2 ]. Vehicle motion control is a key technology in autonomous vehicles, as it has a direct effect on tracking performance and safety [ 3 ]. This paper is concerned with the path tracking control of autonomous vehicles, which aims at ensuring the vehicle to accurately follow a reference trajectory and maintain stability under varying environmental and vehicular conditions.…”

Research for Nonlinear Model Predictive Controls to Laterally Control Unmanned Vehicle Trajectory Tracking

“…In addition, many MPC algorithms for autonomous driving use linear predictive models based on approximate linearization [16]. A linear time-varying predictive model considering cornering characteristics was designed and optimized in [17]. Gallep et al directly used a simplified vehicle linear kinematics model and verified the algorithm by a single-shift simulation [18].…”