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Autonomous Vehicles as Local Traffic Optimizers

This paper explores the interaction between autonomous and human-driven cars on a microscopic level using an agent-based traffic simulator. More specifically, it deals with the design of driving logic models of “socially-aware” autonomous vehicles that can improve the performance of surrounding vehi...

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Detalles Bibliográficos
Autores principales: Bhatia, Ashna, Ivanchev, Jordan, Eckhoff, David, Knoll, Alois
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302265/
http://dx.doi.org/10.1007/978-3-030-50371-0_37
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author Bhatia, Ashna
Ivanchev, Jordan
Eckhoff, David
Knoll, Alois
author_facet Bhatia, Ashna
Ivanchev, Jordan
Eckhoff, David
Knoll, Alois
author_sort Bhatia, Ashna
collection PubMed
description This paper explores the interaction between autonomous and human-driven cars on a microscopic level using an agent-based traffic simulator. More specifically, it deals with the design of driving logic models of “socially-aware” autonomous vehicles that can improve the performance of surrounding vehicles on the road. Congestion waves, which are created as a result of an abrupt stopping or a car joining a highway, are a known phenomenon in current traffic systems. Experiments performed, demonstrate how the presence of intelligent social vehicles on the road can reduce such effects by acting as a flexible medium between human-driven cars. Metrics to evaluate benefits ot our AV behaviour models under various states of traffic conditions/congestion are also proposed. Finally, results showing the effectiveness of these models are presented.
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spelling pubmed-73022652020-06-18 Autonomous Vehicles as Local Traffic Optimizers Bhatia, Ashna Ivanchev, Jordan Eckhoff, David Knoll, Alois Computational Science – ICCS 2020 Article This paper explores the interaction between autonomous and human-driven cars on a microscopic level using an agent-based traffic simulator. More specifically, it deals with the design of driving logic models of “socially-aware” autonomous vehicles that can improve the performance of surrounding vehicles on the road. Congestion waves, which are created as a result of an abrupt stopping or a car joining a highway, are a known phenomenon in current traffic systems. Experiments performed, demonstrate how the presence of intelligent social vehicles on the road can reduce such effects by acting as a flexible medium between human-driven cars. Metrics to evaluate benefits ot our AV behaviour models under various states of traffic conditions/congestion are also proposed. Finally, results showing the effectiveness of these models are presented. 2020-05-26 /pmc/articles/PMC7302265/ http://dx.doi.org/10.1007/978-3-030-50371-0_37 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
Bhatia, Ashna
Ivanchev, Jordan
Eckhoff, David
Knoll, Alois
Autonomous Vehicles as Local Traffic Optimizers
title Autonomous Vehicles as Local Traffic Optimizers
title_full Autonomous Vehicles as Local Traffic Optimizers
title_fullStr Autonomous Vehicles as Local Traffic Optimizers
title_full_unstemmed Autonomous Vehicles as Local Traffic Optimizers
title_short Autonomous Vehicles as Local Traffic Optimizers
title_sort autonomous vehicles as local traffic optimizers
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302265/
http://dx.doi.org/10.1007/978-3-030-50371-0_37
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