Research On Intelligent Vehicle Active Avoidance Path Planning And Tracking Control

With the increase in the number of motor vehicles,the number of personal and property losses caused by traffic accidents has been increasing year by year.In the early days,vehicles were equipped with passive safety devices such as seat belts and airbags to improve driving safety,vehicles are installed with assisted driving systems,more mature ones such as Adaptive Cruise Control(ACC),Autonomous Emergency Braking(AEB)and other active safety technologies that can play an active preventive role,and the application of intelligent control systems on vehicles has significantly improved traffic safety and reduce traffic losses.In the process of vehicle driving,avoidance behaviour is a common movement,and there are more traffic accidents caused by avoidance behaviour.Suitable path planning and accurate tracking control algorithms can bring better safety and efficiency to avoidance behaviour.In this paper,path planning and tracking control during avoidance is studied based on intelligent electric vehicles,The main research contents are as follows:(1)An intelligent electric vehicle system model is established.An empirical tyre model is established through the magic formula to analyse the mechanical characteristics of tyre lateral force,longitudinal force and return moment;a non-linear dynamics model is established based on the small angle assumption.(2)A double quintuple polynomial avoidance algorithm integrating polynomial algorithm and genetic algorithm is designed.By analyzing the vehicle avoidance process and motion environment,the avoidance time algorithm with real-time is proposed;according to the objective function and constraints,the avoidance process is transformed into a nonlinear programming problem,the genetic algorithm is used to solve the nonlinear programming problem,the evaluation function is added to the algorithm,the parameters are filtered,and the obtained parameters are substituted into the double quintuple polynomial algorithm to get the avoidance path.(3)An MPC path tracking method based on intelligent electric vehicle and avoidance path is designed.The smart electric vehicle dynamics model is used as the controller prediction model,the difficulty of solving is reduced by model linearisation and the model is discretised.The objective function and constraints are designed and transformed into a quadratic programming optimisation problem,and the step size and sampling period in the MPC controller are adjusted online in real time to optimise the solution by the interior point method.The MPC algorithm is effective in dealing with intelligent vehicle path tracking problems,and has good adaptability and stability in the face of changes in vehicle speed and road adhesion conditions.(4)A simulation platform based on Carsim,Prescan,and Simulink was built to analyze the vehicle avoidance performance through joint simulation and hardware-in-the-loop simulation.The simple dynamics model in Prescan is replaced by the more accurate dynamics model in Carsim,and the software simulation is carried out based on this model,and the software is debugged and connected to the hardware for hardware-in-the-loop simulation.The simulation results show that the MPC controller designed in this paper has high stability and tracking performance when the road attachment conditions and vehicle speed change,and can control the vehicle to follow the designed route.