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

fYear

2007

fDate

2-5 July 2007

Firstpage

3444

Lastpage

3449

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ECC), 2007 European

Conference_Location

Kos

Print_ISBN

978-3-9524173-8-6

Type

conf

Filename

7068741