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Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles

In the last few years autonomous vehicles have been on the rise. This increase in popularity lead by new technology advancements and availability to the regular consumer has put them in a position where safety must now be a top priority. With the objective of increasing the reliability and safety of...

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Detalles Bibliográficos
Autores principales: Garrido, Daniel, Ferreira, Leonardo, Jacob, João, Silva, Daniel Castro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302236/
http://dx.doi.org/10.1007/978-3-030-50371-0_35
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author Garrido, Daniel
Ferreira, Leonardo
Jacob, João
Silva, Daniel Castro
author_facet Garrido, Daniel
Ferreira, Leonardo
Jacob, João
Silva, Daniel Castro
author_sort Garrido, Daniel
collection PubMed
description In the last few years autonomous vehicles have been on the rise. This increase in popularity lead by new technology advancements and availability to the regular consumer has put them in a position where safety must now be a top priority. With the objective of increasing the reliability and safety of these vehicles, fault detection and treatment modules for autonomous vehicles were developed for an existing multi-agent platform that coordinates them to perform high-level missions. Additionally, a fault injection tool was also developed to facilitate the study of said modules alongside a fault categorization system to help the treatment module select the best course of action. The results obtained show the potential of the developed work, with it being able to detect all the injected faults during the tests in a small enough time frame to be able to adequately treat these faults.
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spelling pubmed-73022362020-06-18 Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles Garrido, Daniel Ferreira, Leonardo Jacob, João Silva, Daniel Castro Computational Science – ICCS 2020 Article In the last few years autonomous vehicles have been on the rise. This increase in popularity lead by new technology advancements and availability to the regular consumer has put them in a position where safety must now be a top priority. With the objective of increasing the reliability and safety of these vehicles, fault detection and treatment modules for autonomous vehicles were developed for an existing multi-agent platform that coordinates them to perform high-level missions. Additionally, a fault injection tool was also developed to facilitate the study of said modules alongside a fault categorization system to help the treatment module select the best course of action. The results obtained show the potential of the developed work, with it being able to detect all the injected faults during the tests in a small enough time frame to be able to adequately treat these faults. 2020-05-26 /pmc/articles/PMC7302236/ http://dx.doi.org/10.1007/978-3-030-50371-0_35 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Garrido, Daniel
Ferreira, Leonardo
Jacob, João
Silva, Daniel Castro
Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles
title Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles
title_full Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles
title_fullStr Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles
title_full_unstemmed Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles
title_short Fault Injection, Detection and Treatment in Simulated Autonomous Vehicles
title_sort fault injection, detection and treatment in simulated autonomous vehicles
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302236/
http://dx.doi.org/10.1007/978-3-030-50371-0_35
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