Research On Intelligent Vehicle Path Planning And Tracking Control

With the rapid development of science and technology,the national economy has been significantly enhanced,and the per capita economic ownership has been greatly improved.At the same time,the car ownership is bound to increase,resulting in increasingly serious environmental and traffic problems.Among them,traffic accidents are the most prominent and the most harmful to society,mainly due to the irregular operation of drivers.In order to reduce the occurrence of traffic accidents and respond to social needs,domestic and foreign automobile enterprises and scientific research institutions began to research on intelligent vehicles,which can help people drive to achieve the purpose of reducing traffic accidents.Path planning and path tracking are two key technologies in the research of intelligent vehicle,which can make the intelligent vehicle track the planned path accurately.Therefore,based on intelligent vehicles,this thesis studies the path planning and path tracking technology of intelligent vehicles.In this thesis,the kinematic model and monorail model of vehicle are established firstly,which lays the theoretical foundation for the research of intelligent vehicle control system and the design of model predictive control(MPC)tracking controller;the tire model is established to determine the linear working area of tire,which lays the foundation for the design of controller constraints.Secondly,the local path planning of intelligent vehicles in structured road environment is studied.In order to solve the problem of inaccessibility and local optimization of the target revealed by the artificial potential field method in path planning,a method of adding distance factor to the repulsion function is proposed for improvement.In order to restrict the vehicle driving in the structured road with multiple obstacles,the potential field of road boundary and the potential field of speed are added,so that the improved algorithm can achieve the effect of avoiding obstacles and make the vehicle reach the target point at the same time.In order to adapt to the actual road environment and avoid collision with the vehicle in front,the dynamic overtaking simulation is completed to verify that the improved algorithm can effectively complete the local path planning.Then,the basic theory of model prediction is described,and the nonlinear dynamic model is selected as the prediction model of MPC control algorithm.After linearization and discretization,the linear time-varying MPC control algorithm is obtained according to the constraint conditions and objective functions,thus the MPC path tracking controller is established.The simulation results show that the intelligent vehicle controlled by MPC has higher tracking accuracy and better stability under different speed and road adhesion coefficient.Finally,in order to verify the practicability of the path planning and tracking algorithm proposed in this thesis,the controller model built in Simulink,the whole vehicle model and test conditions are built in Car Sim,so that intelligent vehicles can track and simulate the planned overtaking path under the conditions of different speed and different road adhesion coefficient.The results show that the path planning and tracking algorithm proposed in this paper can well plan the overtaking path and realize the stable tracking of the overtaking path.