Dynamic Estimation of Velocity and Headway Distance of Longitudinal Platooning Vehicles Using Neural Kalman Filter

Abstract

A dynamic feedback system is developed to estimate velocity and headway distance in a longitudinal three-vehicle platoon. The estimation system is modeled by extended Kalman filter (EKF) as well as neural Kalman filter (NKF) that estimate the velocity and headway distance by measuring acceleration rate of some selected vehicles in the platoon. State equations of the EKF are analytically defined by discrete conservation equation of vehicle speed and spacing, whereas the measurement equation is based on a conventional car-following model. The NKF, however, defined both equations by artificial neural network models (ANNs) which enables both equations to be defined without using any analytical equations. Numerical analysis showed that the NKF reduces the estimation errors in most cases compared to EKF because of the high capability of ANN models for describing non linear phenomena. However, less statistical difference was observed between NKF and EKF due to the lack of data sets or measurement variables.