Study On The Estimation Of Vehicle State Parameters Under Human-Vehicle-Road Closed Loop System

The human-vehicle-road is an inseparable whole of road traffic safety research.With the increasing demand of people for the active safety of vehicles,it is particularly important to obtain the state parameter information of vehicles in real time.There are few researches on the estimation of vehicle state parameters under the human-vehicle-road closed-loop system,and the vehicle motion is simplified to a large extent.In view of this,this paper designs a vehicle state parameter estimation model based on kalman filter algorithm,and intends to explore the problem of vehicle state parameter estimation under the influence of driver?human?,vehicle?vehicle?and road environment?road?.Firstly,analyzing the theoretical basis,establishment method and overall structure of the human-vehicle-road closed-loop system,establishing a vehicle simulation model in the MATLAB/Simulink environment,and verifying the reliability of the built model through the common test conditions such as shifting and serpentine condition.Secondly,based on the human-vehicle-road closed-loop simulation system,the principle and characteristics of the extended kalman filter algorithm are studied in depth,and the state estimator is designed,the estimator uses the wheel angle signal as the control input,the longitudinal acceleration,the lateral acceleration,and the transverse pendulum angular velocity signal as the measurement output,and achieves accurate estimation of vehicle longitudinal velocity,lateral velocity and yaw rate.Finally,aiming at the problem that the driver model based on the acceleration feedback can no longer simulate the driver's operation behavior under the complex conditions such as high speed,large steering wheel angle and large side inclination,the fuzzy PID control model of the driver is designed,by using the method of fuzzy inference to realize the online automatic adjustment of PID parameters,the driver output angle information is input into the Carsim closed-loop vehicle system to design a longitudinal speed estimation model that considers the impact of slip rate.Combined with the characteristics of the closed loop system and the driver's operation behavior,the validity of the model was verified by the three A/B/C models selected from Carsim,with initial speed,pavement adhesion coefficient and kalman filter parameters(v_x0,?,Q_k-1/R_k/X₀/P_k0)as the basis for evaluating the effect of state parameter estimation,under conditions determined by v_x0 and?,the influence of filter parameter configuration on state parameter estimation is discussed.The results of closed-loop simulation under typical working conditions show that the two models designed in this paper have high estimation precision and can meet the requirements of the vehicle active safety system under specific working conditions and reasonable filter parameter configuration.On the other hand,the reliability of the simulation model can be described more comprehensively by the v_x0??and Q_k-1/R_k/X₀/P_k0,and the evaluation results of the three types of evaluation indicators are related to each other to a certain extent,which further verifies the accuracy of the simulation estimation model and makes up for the deficiencies in the single evaluation.From the influence degree,kalman filter's measurement noise variance matrix R_k has the greatest influence on the estimation effect,the second is the initial state X₀,the third is the process noise variance matrix Q_k-1,and the final is the initial variance matrix P_k0.