A Reliable Fusion Methodology for Simultaneous Estimation of Vehicle Sideslip and Yaw Angles

Abstract

Vehicle sideslip and yaw angles are critical for many vehicle safety systems. Although much research has been presented to obtain them individually, simultaneous accurate estimation of them, based on affordable sensors for land vehicle applications, is seldom reported. This paper proposes a fusion methodology for integrating a single-frequency double-antenna Global Positioning System (DA-GPS) with other low-cost in-vehicle sensors to achieve reliable estimation of both vehicle sideslip and yaw angles. The proposed methodology adopts a hybrid decentralized filtering architecture. First, a vehicle state estimator (VSE) is developed as a virtual sensor to estimate vehicle state information, mainly according to vehicle dynamics. It is composed of two parallel extended Kalman filters (EKFs). Through the interaction of two EKFs, the VSE can adapt to the variations in tire-road friction and accurately estimate the vehicle roll angle. Then, according to vehicle kinematics, a global federated estimator (GFE) is designed, based on the federated filtering algorithm, to achieve the global fusion of inertial sensors, DA-GPS, and VSE. In particular, an adaptive inference mechanism is proposed and introduced into the GFE to adapt to complex driving situations, such as GPS failure, various driving maneuvers, etc. Finally, the proposed method is evaluated via intensive simulations and experiments. The overall results show that the proposed methodology can provide reliable estimation of two angles under a wide range of driving situations.