Lateral Control Method of Intelligent Vehicles Based on Image Segmentation

Liu, Hongxing; Hu, Guangdi; Du, Yiqun; Zhang, Qi

doi:10.4271/2018-01-1596

Cited by 1 publication

(1 citation statement)

References 8 publications

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“…In the above figure, Coordinate OXY is the ground coordinate; Coordinate oxyz is the vehicle coordinate, where the origin o is the mass center of the vehicle, the x-axis is the longitudinal axis of the vehicle, the y-axis is the horizontal axis of the vehicle, and the z-axis satisfies the right-hand rule and is perpendicular to the oxy up [33], [34]. As shown, F lf and F lr are the longitudinal forces on the front and rear wheels respectively; F cf and F cr are the lateral forces on the front and rear wheels respectively; F xf and F xr are the x-direction forces on the front and rear wheels respectively; F yf and F yr are the y-direction forces on the front and rear wheels respectively; α is the tire sideslip angle; δ is the wheel steering angle.…”

Section: Establishing Vehicle Dynamics Model and Tire Model A Vementioning

confidence: 99%

Trajectory Tracking Control Algorithm for Autonomous Vehicle Considering Cornering Characteristics

et al. 2020

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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.

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Section: Establishing Vehicle Dynamics Model and Tire Model A Vementioning

confidence: 99%