Event-Triggered State Estimation for Connected Vehicles with Packet Losses

Abstract

The accurate estimation of the motion state of the preceding vehicle (PV) can provide effective assistance in the real-time driving decision of the host vehicle. The lateral motion state is directly related to the vehicle stability and the timing of lane change, therefore the prediction of lateral state has been a hot topic of research. However, existing studies estimating the lateral state of PV do not fully consider the advantages of vehicle-to-vehicle communication and ignore the impact of data loss. To address these issues, we propose an event-triggered cubature Kalman filter (ETCKF) that considers data loss to predict the PV state in a connected vehicle environment. First, a bicycle model is developed. Then, an ETCKF is proposed. Where the event-triggered mechanism is designed to decide whether sensor data of the preceding vehicle is transferred to the host vehicle. The desired compromise between transmission rate and state prediction accuracy can be achieved by properly adapting the threshold and the impact of packet loss is diminished significantly. Finally, the test results show that the ETCKF can accurately predict vehicle motion and effectively reduce the communication burden.