This paper presents a method for obtaining estimates of key vehicle states by combining automotive grade inertial sensors with a Global Positioning System (GPS) receiver. A Kalman filter integrates the inertial sensors with GPS to provide high update estimates of the sensor biases, heading, and vehicle velocities, which can be used to calculate the vehicle slip angle. Since roll and pitch effects from the vehicle motion and road influence the measurements obtained from the sensors and GPS, this paper develops a method that incorporates these roll and pitch effects to improve the accuracy of the vehicle state and sensor bias estimates. With accurate measurements of roll angle and roll rate, it is also possible to estimate roll parameters, such as roll stiffness and damping ratio, with a second order dynamic model. Based on these results, this paper also presents a new method for identifying road bank and vehicle roll separately using a disturbance observer. Experimental results verify that the estimation schemes are giving appropriate estimates of vehicle states, vehicle roll angle, and road bank angle.
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