Feedback Estimators for Identifying Headway Distance and Velocity of Longitudinal Platooned Vehicles

概要

論文の詳細を見る
A dynamic feedback system is developed for estimating the headway distance and velocity in a longitudinal three-vehicle platoon. The estimation system is modeled using a particle filter (PF) and an unscented Kalman filter (UKF) that estimate them by measuring the acceleration rate and/or velocity of probe vehicle(s) in the platoon. State equations are defined as a discrete conservation equation of headway distance and velocity, whereas the measurement equation is based on a conventional car-following model. The UKF and PF have the advantage of avoiding first-order approximation when implementing a filtering process to increase the estimation accuracy. Numerical analyses using artificial simulated data as well as real car-following data showed that the PF and UKF reduce the estimation errors in most cases compared to conventional approaches such as an extended Kalman filter (EKF) or neural Kalman filter (NKF). This was significant especially in the headway estimation, where the accuracy of the EKF estimates was low.