Uncertainty analysis of trajectory prediction based on advanced motion model
In order to study the influence of vehicle model uncertainty and environmental perception uncertainty on the vehicle trajectory prediction accuracy,a model of constant yaw rate and constant turn rate and velocity(CTRV)trajectory prediction was established by using Matlab.Combined with the global positioning system(GPS)and inertial measurement unit(IMU)fused positioning datasets under different working conditions,the extended Kalman filters(EKF)algorithm was used to process the vehicle model process noise and sensor measurement noise.On this basis,simulation experiments were carried out to analyze the vehicle trajectory prediction error under different driving conditions and its influence on the five state variables of the trajectory prediction model.The results show that the EKF algorithm can handle the vehicle model process noise and sensor measurement noise well,and the trajectory prediction error under straight-line driving conditions is controlled within 0.3 m,the trajectory prediction error range under the small curvature curve driving conditions is 1～9 m,and the trajectory prediction error range under large curvature curve driving conditions is 2～38 m.When the trajectory prediction model based on CTRV is combined with the EKF algorithm to deal with the uncertainty,the road curvature will directly affect the filtering of the vehicle yaw angle,and even cause the vehicle yaw angle filtering trajectory to diverge,resulting in a large trajectory prediction error under the condition of large curvature curves.
NETL
NSTL
万方数据
