@article{ref1,
title="A quantitative model for the risk evaluation of driver-ADAS systems under uncertainty",
journal="Reliability engineering and system safety",
year="2017",
author="Qiu, S. and Rachedi, N. and Sallak, M. and Vanderhaegen, F.",
volume="167",
number="",
pages="184-191",
abstract="In this paper, a quantitative model is proposed to assess the probability of accidents occurring in driver-Advanced Driver Assistance Systems (ADAS) under uncertainty using Valuation-Based System (VBS). Two kinds of uncertainties are analyzed: data uncertainty related to the states of components, and model uncertainty related to the system structure. The components and the system structure are modeled using variables, spaces of variables, and a set of valuations represented by basic probability assignments (bpas). Besides, the positive influence of learning and cooperation processes is also quantified. Finally, the proposed method is applied to a real use case: the Car Navigation System (CNS).<p /> <p>Language: en</p>",
language="en",
issn="0951-8320",
doi="10.1016/j.ress.2017.05.028",
url="http://dx.doi.org/10.1016/j.ress.2017.05.028"
}