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Derivation of harmonised high-level safety requirements for self-driving cars using railway experience

The development and manufacture of self-driving cars (SDCs) have triggered unprecedented challenges among car manufacturers and smart road operators to accelerate awareness and implementation of innovative technologies for cooperative, connected and automated mobility (CCAM), especially those with a...

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Detalles Bibliográficos
Autores principales: Filip, Aleš, Capua, Roberto, Neri, Alessandro, Rispoli, Francesco
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792447/
https://www.ncbi.nlm.nih.gov/pubmed/36572714
http://dx.doi.org/10.1038/s41598-022-26764-0
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author Filip, Aleš
Capua, Roberto
Neri, Alessandro
Rispoli, Francesco
author_facet Filip, Aleš
Capua, Roberto
Neri, Alessandro
Rispoli, Francesco
author_sort Filip, Aleš
collection PubMed
description The development and manufacture of self-driving cars (SDCs) have triggered unprecedented challenges among car manufacturers and smart road operators to accelerate awareness and implementation of innovative technologies for cooperative, connected and automated mobility (CCAM), especially those with a high level of automation and safety. Safety improvement is a pre-requisite to justify and unleashing a mass deployment of connected and driverless cars to reach the goal of zero-accident in 2050 set by the European Commission. Behind these motivations a well-justified and widely acceptable high-level safety target for SDCs is mandatory. The aim of this article is to contribute to the derivation of an harmonised high-level safety target for SDCs, starting from the safety requirements and the state of the art achieved by train and airplane operations. The novelty of our approach is to leverage the Common Safety Method-Design Targets (CSM-DT) already adopted and widely accepted by the railway community. According to this approach, the derived, justified and harmonised high-level design safety target for SDCs, defined as the average probability of a dangerous failure PF(SDC) per 1 h, should be 1 × 10(−7)/h. An example of PF(SDC) allocation to individual SDC safety functions, including position determination based on Global Navigation Satellite System (GNSS), is described using a fault tree. The proposed methodology can speed up the validation and certification process needed to authorise the SDCs, by capitalising the know-how and best practices in use since many years for the train management.
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spelling pubmed-97924472022-12-28 Derivation of harmonised high-level safety requirements for self-driving cars using railway experience Filip, Aleš Capua, Roberto Neri, Alessandro Rispoli, Francesco Sci Rep Article The development and manufacture of self-driving cars (SDCs) have triggered unprecedented challenges among car manufacturers and smart road operators to accelerate awareness and implementation of innovative technologies for cooperative, connected and automated mobility (CCAM), especially those with a high level of automation and safety. Safety improvement is a pre-requisite to justify and unleashing a mass deployment of connected and driverless cars to reach the goal of zero-accident in 2050 set by the European Commission. Behind these motivations a well-justified and widely acceptable high-level safety target for SDCs is mandatory. The aim of this article is to contribute to the derivation of an harmonised high-level safety target for SDCs, starting from the safety requirements and the state of the art achieved by train and airplane operations. The novelty of our approach is to leverage the Common Safety Method-Design Targets (CSM-DT) already adopted and widely accepted by the railway community. According to this approach, the derived, justified and harmonised high-level design safety target for SDCs, defined as the average probability of a dangerous failure PF(SDC) per 1 h, should be 1 × 10(−7)/h. An example of PF(SDC) allocation to individual SDC safety functions, including position determination based on Global Navigation Satellite System (GNSS), is described using a fault tree. The proposed methodology can speed up the validation and certification process needed to authorise the SDCs, by capitalising the know-how and best practices in use since many years for the train management. Nature Publishing Group UK 2022-12-26 /pmc/articles/PMC9792447/ /pubmed/36572714 http://dx.doi.org/10.1038/s41598-022-26764-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Filip, Aleš
Capua, Roberto
Neri, Alessandro
Rispoli, Francesco
Derivation of harmonised high-level safety requirements for self-driving cars using railway experience
title Derivation of harmonised high-level safety requirements for self-driving cars using railway experience
title_full Derivation of harmonised high-level safety requirements for self-driving cars using railway experience
title_fullStr Derivation of harmonised high-level safety requirements for self-driving cars using railway experience
title_full_unstemmed Derivation of harmonised high-level safety requirements for self-driving cars using railway experience
title_short Derivation of harmonised high-level safety requirements for self-driving cars using railway experience
title_sort derivation of harmonised high-level safety requirements for self-driving cars using railway experience
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9792447/
https://www.ncbi.nlm.nih.gov/pubmed/36572714
http://dx.doi.org/10.1038/s41598-022-26764-0
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