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

Abstract : To make their decisions, autonomous vehicles need to build a reliable representation of their environment. In the presence of sensors that are redundant, but not necessarily equivalent, that may get unreliable, unavailable or faulty, or that may get attacked, it is of fundamental 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 are most 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.
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https://hal.archives-ouvertes.fr/hal-02886993
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Submitted on : Wednesday, July 1, 2020 - 9:33:32 PM
Last modification on : Sunday, August 30, 2020 - 3:59:55 PM
Long-term archiving on: : Friday, September 25, 2020 - 10:14:16 AM

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Guillaume Hutzler, Hanna Klaudel, Abderrahmane Sali. Filtering distributed information to build a plausible scene for autonomous and connected vehicles. 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⟩. ⟨hal-02886993⟩

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