Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego

Moving block railway systems are the next generation signalling systems currently under development as part of the Shift2Rail European initiative, including autonomous driving technologies. In this paper, we model a suitable abstraction of a moving block signalling system with autonomous driving as...

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Detalles Bibliográficos
Autores principales: Basile, Davide, ter Beek, Maurice H., Legay, Axel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281857/
http://dx.doi.org/10.1007/978-3-030-50086-3_1
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author Basile, Davide
ter Beek, Maurice H.
Legay, Axel
author_facet Basile, Davide
ter Beek, Maurice H.
Legay, Axel
author_sort Basile, Davide
collection PubMed
description Moving block railway systems are the next generation signalling systems currently under development as part of the Shift2Rail European initiative, including autonomous driving technologies. In this paper, we model a suitable abstraction of a moving block signalling system with autonomous driving as a stochastic priced timed game. We then synthesise safe and optimal driving strategies for the model by applying advanced techniques that combine statistical model checking with reinforcement learning as provided by Uppaal Stratego. Hence, we show the applicability of Uppaal Stratego in this concrete case study.
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spelling pubmed-72818572020-06-09 Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego Basile, Davide ter Beek, Maurice H. Legay, Axel Formal Techniques for Distributed Objects, Components, and Systems Article Moving block railway systems are the next generation signalling systems currently under development as part of the Shift2Rail European initiative, including autonomous driving technologies. In this paper, we model a suitable abstraction of a moving block signalling system with autonomous driving as a stochastic priced timed game. We then synthesise safe and optimal driving strategies for the model by applying advanced techniques that combine statistical model checking with reinforcement learning as provided by Uppaal Stratego. Hence, we show the applicability of Uppaal Stratego in this concrete case study. 2020-05-13 /pmc/articles/PMC7281857/ http://dx.doi.org/10.1007/978-3-030-50086-3_1 Text en © IFIP International Federation for Information Processing 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
Basile, Davide
ter Beek, Maurice H.
Legay, Axel
Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego
title Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego
title_full Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego
title_fullStr Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego
title_full_unstemmed Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego
title_short Strategy Synthesis for Autonomous Driving in a Moving Block Railway System with Uppaal Stratego
title_sort strategy synthesis for autonomous driving in a moving block railway system with uppaal stratego
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7281857/
http://dx.doi.org/10.1007/978-3-030-50086-3_1
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AT legayaxel strategysynthesisforautonomousdrivinginamovingblockrailwaysystemwithuppaalstratego

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