Research On The Lateral And Longitudinal Coordination Control Of Intelligent Vehicle Based On Extension Theory

With the development of intelligence,the intelligent vehicle has become an important direction of the development.Lateral and longitudinal control is one of the key technologies of intelligent vehicle.Under high-speed and large-curvature conditions,it is difficult for intelligent vehicle to ensure good control effects,which has become an urgent problem to be solved in the research of intelligent vehicle’s lateral and longitudinal control.Therefore,relying on the "National Key R&D Program",this subject has carried out research on the related content of intelligent vehicle lateral and longitudinal coordination control.The lateral and longitudinal coordination control of intelligent vehicle based on extension theory is proposed.Based on the extension theory,the vehicle state correlation function divides the measurement mode to coordinate the lateral and longitudinal control.In the lateral direction,the extension-adjustment pure pursuit control combined with the correlation function of state compensation is adopted,which solves the difficult problem of achieving better lateral control accuracy and lateral stability under high-speed and large-curvature conditions.In the longitudinal direction,a safe vehicle speed extension planning strategy is proposed,which adaptively adjust vehicle speed according to the vehicle state,so that the tracking effect and lateral stability of the intelligent vehicle in the full-speed domain and variable-curvature condition is further improved,and the ride comfort of the intelligent vehicle is effectively improved.The following is the main research content:Firstly,based on the vehicle dynamics,kinematics model and preview tracking model,combined with extension control theory and pure pursuit control theory,the extension adjustment pure pursuit control strategy is designed.The upper layer selects vehicle speed and curvature as the characteristic quantities and calculates the preview characteristic correlation function to realize the measurement pattern recognition.The lower layer executive layer adopts the corresponding preview adjustment method in each measurement mode to achieve preview distance adaptive adjustment of the pure pursuit control.Based on the joint simulation of Matlab/Simulink and Carsim,it is verified that the extension-adjusted pure pursuit control better improves the vehicle tracking effect.Compared with the PID control method and the classic pure pursuit control method,tracking accuracy based on the extension-adjustable pure pursuit control has been improved by about 32.9% and 8.3%.Secondly,in view of the lack of theoretical basis for vehicle speed setting and the lack of judgment based on error and stability indicators in the system,combined with the vehicle state correlation function,a lateral and longitudinal coordination control strategy based on extension theory is optimized.The upper layer selects the lateral position error and yaw rate that characterize the vehicle state as characteristic quantities and calculates the vehicle state correlation function to achieve measurement pattern recognition.The lower layer combines the vehicle state correlation function in the lateral direction to further optimize the extension adjustment pure pursuit control.A state compensation extension adjustment pure pursuit control based on the vehicle state correlation function is designed.A safety vehicle speed extension planning strategy in the longitudinal direction is introduced to adaptively adjust the vehicle speed according to the vehicle state correlation function,which further improves the tracking accuracy and lateral stability in the lateral direction.Based on the joint simulation results of Matlab/Simulink and Carsim,it can be seen that compared with the MPC control method and the extension-adjustable pure pursuit control,the tracking accuracy of the lateral and longitudinal coordination control based on the extension theory was improved respectively by about 34.1% and 13.9%,and the lateral stability was improved respectively by about 23.4% and 10.1%.Finally,in order to verify the effectiveness of the lateral and longitudinal coordination control method proposed in the actual open fast road conditions,this paper is based on the intelligent driving test platform,and transplants the lateral and longitudinal coordination control algorithm based on the extension theory to a real vehicle.Configure the controller to match the signals of the vehicle and sensors and establish communication,integrate the preview error algorithm and the lateral and longitudinal coordination control algorithm based on the extension theory,compile it to generate the controller executable file,and program the entire vehicle through the programming software in the controller,the lateral and longitudinal control test of the intelligent vehicle was carried out on the actual open express road section.The test results show that the lateral and longitudinal coordination control method based on the extension theory proposed in this paper effectively guarantees the tracking accuracy and lateral stability of the intelligent vehicle.Compared with PID control,it is improved by about 24.5% and 22.1% respectively.