Filtering distributed information to build a plausible scene for autonomous and connected vehicles

Autor: Hanna Klaudel, Guillaume Hutzler, Abderrahmane Sali
Přispěvatelé: Informatique, BioInformatique, Systèmes Complexes (IBISC), Université d'Évry-Val-d'Essonne (UEVE)-Université Paris-Saclay
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: 17th International Conference on Distributed Computing and Artificial Intelligence (DCAI 2020)
17th International Conference on Distributed Computing and Artificial Intelligence (DCAI 2020), Oct 2020, L'Aquila, Italy. pp.89--101, ⟨10.1007/978-3-030-53036-5_10⟩
Advances in Intelligent Systems and Computing ISBN: 9783030530358
DCAI
Popis: International audience; To make their decisions, autonomous vehicles need to build a reliable representationof their environment. In the presence of sensors that are redundant, but not necessarilyequivalent, that may get unreliable, unavailable or faulty, or that may get attacked, it is offundamental importance to assess the plausibility of each information at hand. To this end,we propose a model that combines four criteria (relevance, trust, freshness and consistency)in order to assess the confidence in the value of a feature, and to select the values that aremost plausible.We show that it enables to handle various difficult situations (attacks, failures,etc.), by maintaining a coherent scene at any time despite possibly major defects.
Databáze: OpenAIRE