Research On Automatic Driving System For Pure Electric Vehicles

In recent years,with the rapid development of China's economy,the rapid increase in the number of car ownership has led to an increase in traffic safety accidents,and the problem of vehicle exhaust pollution has become more serious.According to the analysis of statistical data on traffic safety accidents by relevant departments in recent years,it has been found that complicated driving operations and complicated driving environments are the key reasons leading to traffic safety accidents.Autopilot technology separates the “human” from the driving task,thereby effectively avoiding the traffic accident caused by the driver's misjudgment or misoperation.Therefore,how to successfully plan a passable path in a complex driving environment and how to control the vehicle's smooth and accurate tracking of the planned path have very important theoretical research significance and practical application value.The main research work of this article is:The path planning option is artificial potential field method.A visual interface was written using MATLAB/GUIDE software,and the position information of multiple obstacles was input through the visual interface for the traditional artificial potential field method.Simulation results show that the path planned by the traditional artificial potential field method can successfully avoid obstacles and be smooth.In order to make the traditional artificial potential field method used in low-speed robots suitable for vehicle path planning,the vehicle model with two degrees of freedom is taken as the research object,and a method based on the limitation of vehicle turning radius is used to improve the traditional artificial potential field method.The simulation results show that the improved artificial potential field method can be well adapted to linear,polygonal,curvilinear and square obstacles.The path-tracking controller is designed based on the disturbance observer.The sliding mode control theory which is not sensitive to parameter perturbations and disturbances is selected.The reaching law is the exponential reaching law,and the saturation function method is used to eliminate chattering.The control amount of the path tracking controller is the steering wheel angle,and the tracking amount is the lateral displacement and speed.The cruise controller uses an incremental PID control algorithm.The difference between the expected cruise speed and the actual vehicle speed is used as a control input to control the vehicle speed.Based on the test road related standards,a three-step curve fitting method was adopted to establish a dual-line and serpentine road test model.Then the path tracking and speed tracking performances of the path tracking controller are simulated and analyzed by using the double-moving path,serpentine path and obstacle avoidance path respectively as the desired path.The simulation results show that the tracking performance of the sliding mode controller based on the disturbance observer is better than that of the incremental PID control,and the tracking effect of the double shift line and snake line path is better than the tracking effect of the obstacle avoidance path.The path tracking deviations were kept within 0.03 m,0.02 m and 0.04 m respectively,the deviations of speed deviations were kept within 0.14m/s,0.04m/s and 0.99m/s,respectively;the steering wheel rotation angles were kept at 0.89 rad,0.31 rad and 2.85 rad or less;lateral speed is kept within 1.71m/s,0.85m/s and 3.56m/s;yaw rate is kept within 0.39rad/s,0.16rad/s and 1.06rad/s.The cruise control experiment uses a smart car with a code speed function and an LCD display function as a controlled object and a development board with a master control chip as the STM32F103ZET6 as the main controller,and carries out cruise control on a real slope road surface.Simulation experiments,experimental results show that the controller can meet the design requirements.