Citation

Sellami Y, Imine H, Boubezoul A, Cadiou JC. Veh. Syst. Dyn. 2018; 56(3): 385-405.

Copyright

(Copyright © 2018, Informa - Taylor and Francis Group)

DOI

10.1080/00423114.2017.1381980

PMID

unavailable

Abstract

This paper focuses on a combination of a reliability-based approach and an empirical modelling approach for rollover risk assessment of heavy vehicles. A reliability-based warning system is developed to alert the driver to a potential rollover before entering into a bend. The idea behind the proposed methodology is to estimate the rollover risk by the probability that the vehicle load transfer ratio (LTR) exceeds a critical threshold. Accordingly, a so-called reliability index may be used as a measure to assess the vehicle safe functioning. In the reliability method, computing the maximum of LTR requires to predict the vehicle dynamics over the bend which can be in some cases an intractable problem or time-consuming. With the aim of improving the reliability computation time, an empirical model is developed to substitute the vehicle dynamics and rollover models. This is done by using the SVM (Support Vector Machines) algorithm. The preliminary obtained results demonstrate the effectiveness of the proposed approach.

Language: en

Keywords

empirical modelling; Heavy vehicle accident prediction; reliability index; rollover risk assessment; SVM classification