Research On Intelligent Vehicle Path Tracking Control

With the development of industrial technology,the number of cars is gradually increasing,and road traffic safety is increasingly concerned by people.Traditional vehicle passive safety can reduce casualties when accidents occur,but it cannot fundamentally reduce the occurrence of accidents.At present,active safety and autonomous driving have attracted more and more attention.This paper will study the path following control of intelligent vehicles.Firstly,Pacejka magic formula is used to model and analyze the vehicle tire,and the system identification toolbox is used to identify the parameters in the magic formula.By analyzing the motion state of the vehicle,a two-degree-of-freedom vehicle model is established,which provides a theoretical basis for the research of the back-sequence path tracking algorithm.The accurate model of the controlled vehicle was established by using the vehicle dynamics simulation software Carsim,and the 2-DOF vehicle model was verified.Secondly,by analyzing the driver’s behavior,the formula derivation and simulation analysis of the path tracking control algorithm based on trajectory prediction are carried out.The predicted trajectory of the vehicle is obtained through the vehicle’s current position,control period and preview deviation.The front wheel rotation Angle is solved according to the two-degree-of-freedom vehicle dynamics model.Based on the analysis of the influence of control period on path tracking at different velocities,a path tracking algorithm based on adaptive control period is proposed.The validity of the path tracking algorithm was verified by the simulation platform built by Car Sim and Simulink.The results show that the improved path tracking algorithm is better than the original algorithm.Then,the path following algorithm based on model predictive control was used to further improve the stability of the vehicle.The two-degree-of-freedom vehicle model was linearized and discretized,and the linear time-varying prediction equation was obtained.The objective function based on the lateral deviation,course Angle deviation and vehicle stability was constructed.Combined with the constraints of vehicle centroid sidesaw Angle,tire sidesaw Angle and the increment of front wheel Angle,the front wheel Angle was obtained.The simulation results show that both of the two tracking algorithms can control the vehicle to track the target path,but the tracking effect of the model predictive control algorithm is better than the trajectory predictive control algorithm,and the operation cycle of the former is much higher than the latter.Finally,this paper formulated a path tracking experiment scheme and built an experimental platform to test the real-time performance of the path tracking algorithm.The positioning receiver and the high-precision positioning service were used to realize the high-precision positioning of the vehicle.The Nvidia autonomous driving development board "Xavier" was used to jointly build the intelligent driving system computing platform with the vehicle controller.The software platform of the intelligent driving vehicle was developed through ROS.The experimental results show that the proposed path tracking algorithm has certain engineering application value,which can realize the accurate tracking of the target path at low speed.