Vehicle Trajectory Prediction Method Based on “Current” Statistical Model and Cubature Kalman Filter

Abstract

Vehicle motion trajectory prediction is the basis of vehicle collision early warning or vehicle conflict resolution. In order to improve the accuracy of trajectory prediction, a vehicle trajectory prediction method based on “current” statistical (CS) model and cubature Kalman filter (CKF) is proposed. This method considers the acceleration variation rules in the actual motion process of the vehicle in the state equation, so that the estimated value of the acceleration can be consistent with the real range. This condition overcomes the limitation of the general trajectory prediction model, which ignores the acceleration change, so it improved prediction accuracy. In addition, this method also avoids the large amount of computational resources required, being that some new methods describe the real acceleration fluctuations. The vehicle trajectory at the intersection that crossed by Yingbin Avenue and Qiche Avenue in Nanchang is selected to verify the tracking performance of Constant Acceleration-Unscented Kalman Filter (CA-UKF), Current Statistical-Unscented Kalman Filter (CS-UKF), and CS-CKF models. The results show that the CS-CKF model has superior prediction effectiveness than the CA-UKF and CS-UKF models, and it improves the accuracy of vehicle motion trajectory prediction.