Title :
Heavy vehicle state estimation and rollover risk evaluation using Kalman Filter and Sliding Mode Observer
Author :
Dakhlallah, J. ; Imine, H. ; Sellami, Y. ; Bellot, D.
Author_Institution :
Lab. Central des Ponts et Chaussees, Paris, France
Abstract :
Safety driving is due to the prevention of risks situation, one of the important risk is the rollover of a heavy vehicle. Preventing this accident requires the knowledge of the rollover coefficient which depends on the vehicle dynamic state and other vehicle parameters. Thus, we estimate the vehicle dynamic state using the Extended and Unscented Kalman Filter and the Sliding Mode Observer. Thereafter, we calculate the probability to have a rollover risk using the previous result and Monte-Carlo simulations.
Keywords :
Kalman filters; Monte Carlo methods; accident prevention; nonlinear filters; probability; risk analysis; road accidents; road safety; state estimation; variable structure systems; vehicle dynamics; Monte-Carlo simulations; accident prevention; extended Kalman filter; heavy vehicle state estimation; probability; rollover coefficient; rollover risk evaluation; safety driving; sliding mode observer; unscented Kalman filter; vehicle dynamic state estimation; vehicle parameters; Kalman filters; Mathematical model; Observers; Vehicle dynamics; Vehicles; Heavy Vehicle Modeling; Kalman Filtering; Rollover Risk; Sliding Mode Observer;
Conference_Titel :
Control Conference (ECC), 2007 European
Conference_Location :
Kos
Print_ISBN :
978-3-9524173-8-6