Research On Path Planning And Tracking Control Of The Intelligent Vehicle On Curve

Intelligent transportation system is the research trend of solving traffic accidents in various countries.Among them,the research of intelligent vehicles is the most important part.Because machine driving can reduce the driver's operating errors,the traffic accident rate is reduced,and the traffic efficiency is also improved.Domestic and foreign research on intelligent vehicles driving on curved roads is significantly lower than their research on straight roads.However,the number of traffic accidents per kilometer of curved roads is significantly greater than that on straight roads.In the face of the above problems,the thesis studies the lane changing technology of intelligent vehicles on curves.The vehicle needs to establish a dynamic model when changing lanes.The vehicle mass is an important parameter for the model and is usually treated as a constant.However,the overall quality of the vehicle will be affected by changes in the weight of the vehicle.In order to make the quality parameters of the model more accurate,this thesis first estimates the overall vehicle quality,uses two methods to estimate,and compares them.The first method performs quality estimation based on extended Kalman filtering.Based on the vehicle longitudinal dynamics model,assuming the road slope is 0,a state space model of the system is obtained,and a quality estimation algorithm based on extended Kalman filter is implemented.The second method improves a traditional vehicle quality estimation algorithm.The algorithm estimates the quality based on the vehicle longitudinal dynamics model and the data on the CAN bus,and uses the ISODATA algorithm to perform clustering to obtain the final vehicle quality estimate.It can be known from the simulation results that the estimation accuracy of the latter is relatively low,but the difference is not large,and because the latter does not need to add an additional engine torque sensor,it is more in line with practical applications.Aiming at the problem of curved lane changing path planning,a path planning algorithm based on Extenics is proposed in the thesis.The path planner is divided into: an upper path generator and a lower path selector.The upper-level path generator generates path sets using quintic polynomials at different longitudinal distances.The lower-level path selector establishes an optimality evaluation controller based on the lane change distance,lateral acceleration,yaw angular velocity,and side deviation angle of the centroid to select the optimal path.Finally,based on Matlab simulation verification,the results show that the lane change path planner can accurately plan the lane change path according to the current state of the vehicle and road information.Aiming at the problem of path change during curve change,two path planning algorithms are used and compared in the thesis.The first is the path tracking control method using traditional preview theory,which uses the feedforward and feedback optimal preview controller.Tracking control of the optimal lane changing path at different speeds has better tracking effect,but the controller does not consider the vehicle's dynamic constraints.The second method is to design a path tracking control method based on MPC.First,a three-degree-of-freedom dynamic model of the vehicle is established,and then the model is linearized and discretized.Then,the objective function and constraints are determined.The step size and control step size are optimized.Finally,the established MPC path tracking controller is simulated and verified on Matlab and Carsim platforms.The simulation results show that the controller not only considers the vehicle dynamic constraints and stability,but also accurately tracks the lane change path.