Rollover risk prediction of heavy vehicles by reliability index and empirical modelling

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.

KEYWORDS:

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

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. The SVM classification is realized by using simple SVM library [31].