Research On Path Planning And Trajectory Tracking Method Of Intelligent Vehicle

As one of the important means of transportation,the number of vehicles have increased year after year,and at the same time,it has also exacerbated the problems of traffic congestion and frequent traffic accidents.With the intention of minimizing the occurrence of these problems,vehicles have become highly intelligentized.With the rapid development of computers,sensors,and cloud big data technology,intelligent vehicles have gradually become the frontier research topics in the automotive field.Among them,the path planning and trajectory tracking methods are the two most important technologies of intelligent vehicles.Therefore,research on path planning and trajectory tracking methods is particularly critical to the development of intelligent vehicles.To this end,this thesis introduces the development of intelligent vehicles,path planning and trajectory tracking algorithms firstly,and understands the path planning algorithms and trajectory tracking algorithms which are currently used,artificial potential field is selected as the path planning algorithm of this thesis,model predictive control algorithm is used as the trajectory tracking algorithm in this thesis.Then,the principle of the Artificial Potential Field is described,and its shortcomings in the terms of path planning of vehicle are analyzed,and the corresponding improvement methods are proposed.The results show that the improved Artificial Potential Field can make the intelligent vehicle run safely in the environment which is full of obstacles.Then,the vehicle model is established by using the relevant theoretical knowledge of automobile theory,and the tire model is established by using magic formula.Finally,the model of vehicle system is deduced based on those two models,and it is written as the form of state space expression.After establishing the model of the vehicle system,the linear time-varying model predictive control theory is used to convert the vehicle model into a predictive model,then the optimization objective function with related constraints is given.Finally,the objective function is transformed into a quadratic programming problem which is easy to solve.A trajectory tracking controller based on the linear time-varying Model Predictive Control is established.Finally,the vehicle model and trajectory tracking controller form a joint simulation platform,and the trajectory points planned by the improved artificial potential field method are used as the reference trajectory to carry out the trajectory tracking test.The speed of the vehicle and the ground adhesion coefficient are used as variables to set a variety of test conditions,and the control effectiveness of the trajectory tracking controller is verified.