Estimation of key parameters for bus safety operation

In order to have an optimal performance, bus control systems have to obtain accurate driving information concerning bus dynamic parameters and road surface condition. However, such information is not easy to acquire directly with high accuracy and low expense. Therefore, this article proposes a model-based estimator, which can implement combined estimation of vehicle states and road friction coefficients. This estimator is realized by employing an interacting multiple model (IMM) filter that integrates 10 extended Kalman filter (EKF) models under different road surface coefficients. With the interacting multiple model-extended Kalman filter(IMM-EKF), longitudinal velocity and yaw rate can be estimated adaptively, and the road coefficient can be acquired by accumulating the probabilities of each EKF model. Simulation using TruckSim-Simulink is carried out to examine the effectiveness of the proposed approach. The results demonstrates that the developed IMM-EKF estimation method is able to reach reasonable accurate estimation of road friction coefficient and yaw rate.