Distributed vehicle state estimation system using information fusion of GPS and in-vehicle sensors for vehicle localization

Abstract

This paper proposes a distributed vehicle state estimation system to improve the performance of vehicle positioning using Global Positioning System (GPS) and in-vehicle sensor components. The distributed architecture of the estimation system can reduce the computational complexity of high-order estimation by dividing it into several small-order estimation modules, and simplifies fault detection and isolation problems. The distributed vehicle state estimation algorithm consists of three estimation modules. The first is a longitudinal vehicle state estimation module which estimates the longitudinal vehicle speed and road slope. The road slope estimate is used to compensate for the vertical speed on the sloped road. The second module is a lateral vehicle state estimator which estimates yaw rate, yaw, and side slip angle using an Interacting Multiple Model (IMM) filter. The last is a position estimation module which integrates the vehicle states from the previous two modules with GPS data to obtain more accurate position information. The proposed estimation algorithm was verified through simulation with the aid of a commercial vehicle model. The results demonstrate the efficiency and accuracy of the proposed algorithm.