Vehicle Sideslip Angle Estimation Based on Interacting Multiple Model Kalman Filter Using Low-Cost Sensor Fusion

Abstract

This study proposes a new method for vehicle sideslip angle estimation utilizing the competitively priced sensor fusion using in-vehicle sensors and low-cost standalone global positioning system (GPS). To estimate unmeasurable vehicle states, vehicle sideslip angle and tire cornering stiffness, an interacting multiple model (IMM) Kalman filter is proposed that combines two extended Kalman filters (EKFs), each including kinematic and dynamic equations of vehicle lateral velocity. To properly combine the outputs of these model-based EKFs, a weighted probability of each model based on the stochastic process is designed, which reflects the characteristics of each of the kinematic and dynamic equations in real-time. Also, the observability of the proposed estimation algorithm is checked by observability functions of nonlinear systems. The estimation performance in various driving scenarios is verified using an experimental vehicle, and its superiority is confirmed through a comparative study. The proposed algorithm makes the following main contributions for estimating the vehicle sideslip angle: 1) the high optimality of estimation results and 2) the accurate estimation of tire cornering stiffness.