A Novel Strong Tracking ETUKF for State Estimation of Preceding Vehicles Using V2V Communications

Abstract

Real-time decision-making of the host vehicle relies on the motion information of the preceding vehicle (PV). Using some advanced sensors to estimate the PV state is a hot research topic. However, existing studies rarely use vehicle-to-vehicle (V2V) communication to estimate the PV state. Meanwhile, the influence of model parameter perturbation on estimation accuracy is not fully considered. In this article, a strong tracking event-triggered unscented Kalman filter (STETUKF) is designed to estimate the PV state utilizing V2V communication. First, we develop a nonlinear vehicle model. Then, the strong tracking filtering is combined with the ETUKF to establish STETUKF. Among them, the event-triggered mechanism determines if onboard sensor data from the PV will be delivered to the host vehicle. By appropriately adjusting the event-triggered threshold, an ideal compromise can be achieved between the transmission rate and estimation accuracy. Finally, experimental results demonstrate that STETUKF has better estimation performance than the traditional UKF. The proposed algorithm can save at least 64.93% of communication resources and it is robust to the model parameter perturbation.