Research On Local Path Planning And Tracking Control Of Driverless Vehicle

With the improvement of science and technology and the development of economy,the number of cars is also increasing rapidly,which is followed by traffic congestion and frequent traffic accidents,which leads to the aggravation of environmental pollution,people and property injury and other problems.Driverless cars with autonomous driving function have become a hot research topic because they can solve these problems from various aspects.The autonomous driving system of a driverless car can be divided into four parts according to its functions: environment perception,behavior decision,path planning and motion control.In this paper,path planning and path tracking control of automatic driving system are studied,and the specific content is as follows.Firstly,the current development status of path planning algorithms and path tracking control methods at home and abroad is summarized.It is found that many path planning algorithms at home and abroad have been relatively mature at present,but there is still the possibility of improvement or integration with other algorithms to improve the effect of algorithm planning.Based on this,the artificial potential field method is improved based on the comprehensive consideration of various path planning algorithms.In view of the problem that the traditional artificial potential field method may appear local optimal phenomenon when facing complex or multiple obstacles,which leads to the stagnation of path planning,this paper improves the artificial potential field method based on simulated annealing algorithm to solve the problem of local optimal.Based on the traditional artificial potential field method,road boundary repulsive potential field is established to improve driving safety.Build a simulation environment for simulation verification.The simulation results show that the improved algorithm can drive within the road range specified by the road boundary,and plan a reasonable obstacle avoidance path when encountering obstacles.Secondly,in order to study the path tracking control problem,a three-degree-of freedom vehicle model is established through reasonable assumptions.On this basis,the MPC algorithm is used to study the vehicle path tracking control,and the MPC path tracking controller is designed.It includes designing the objective function and dealing with the constraint conditions,and converting the multi-objective optimal solution problem into the standard quadratic programming problem.The co-simulation model of Carsim and Simulink was built to verify the feasibility of the MPC path tracking control strategy designed by tracking the serpentine path.Then,through the analysis of the principle of MPC algorithm and multi-group simulation verification,it is found that under different traffic conditions,using the same predictive time domain parameters,the path tracking effect will be worse and the tracking error will be larger.To solve this problem,the original MPC path tracking controller was improved,and A time-domain MPC path tracking controller based on variable prediction is designed.The co-simulation model was built in Carsim and Simulink,and the co-simulation was carried out when the vehicle speed changed and the results were analyzed,which verified that the improved path tracking controller has good control accuracy and stability.Finally,the improved artificial potential field method path planning module is integrated with the improved MPC path tracking controller module,and the Carsim and Simulink co-simulation platform is built.The performance of the integrated path planning and control system was simulated and verified by setting different speed under the road conditions of single obstacle and multiple obstacle respectively.