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A quantitative model for the risk evaluation of driver-ADAS systems under uncertainty

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).
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https://hal.archives-ouvertes.fr/hal-01702349
Contributor : mohamed sallak Connect in order to contact the contributor
Submitted on : Tuesday, February 6, 2018 - 4:26:48 PM
Last modification on : Wednesday, March 9, 2022 - 9:16:03 AM

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Siqi Qiu, Nedjemi Rachedi, Mohamed Sallak, Frédéric Vanderhaegen. A quantitative model for the risk evaluation of driver-ADAS systems under uncertainty. Reliability Engineering and System Safety, Elsevier, 2017, 167, pp.184-191. ⟨10.1016/j.ress.2017.05.028⟩. ⟨hal-01702349⟩

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