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Communication Dans Un Congrès Année : 2021

Adapted Risk Indicator For Autonomous Driving System With Uncertainties and Multi-Dimensional Configurations Modeling

Résumé

This paper addresses the proposal of a new multidimensional risk estimator using spatial uncertainty modeling for automated vehicles risk assessment. This risk estimator, on which relies AV decision-making, is based on an extended version of the distance of Gruyer (DG). This estimator provides an answer and a solution to the risk assessment needed as a part of a generic and extended architecture dedicated to the building of a generic driving meta-model usable for multimodal driving behavior simulation (personal vehicle, connected vehicle, connected and automated vehicles and autonomous vehicle). The proposed estimators have been tested, evaluated, and analyzed on a set of representative highway scenarios with three key performance indicators. Results show that the proposed risk estimator (RIMUM) is more realistic, extended DG more reversible. Future works will concentrate on improving their prediction capacity which is lower than reference estimators.
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Dates et versions

hal-04482648 , version 1 (28-02-2024)

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Jeremy Leroy, Dominique Gruyer, Olivier Orfila, Nour-Eddin El Faouzi. Adapted Risk Indicator For Autonomous Driving System With Uncertainties and Multi-Dimensional Configurations Modeling. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Sep 2021, Indianapolis, United States. pp.2034-2041, ⟨10.1109/ITSC48978.2021.9564857⟩. ⟨hal-04482648⟩
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