Vehicle sideslip angle estimation using deep ensemble-based adaptive Kalman filter

Abstract

This paper presents a novel sideslip angle estimation scheme combining deep neural network and nonlinear Kalman filters. The deep neural network contains a recurrent neural network with long short-term memory which is effective for analyzing sequential sensor data and deep ensemble which is used for robustness of the estimation and acquisition of the uncertainty of the estimate. The deep neural network is trained using input sets which consist of on-board sensor measurements (yawrate, velocity, steering wheel angle and lateral acceleration) and provides sideslip angle estimate and its uncertainty. The estimate of deep neural network is used as a new measure in the nonlinear Kalman filters and its uncertainty is used to make an adaptive measurement covariance matrix. The algorithm is verified through both simulation and experiment. The performance with the proposed method is analyzed in terms of the root mean squared error (RMSE) and maximum error (ME) as compared to the case where nonlinear Kalman filter or deep neural network is utilized individually. The results demonstrate the effectiveness of the proposed solution.