Herd, Benjamin, Burton, Simon orcid.org/0000-0001-9040-8752 and Zacchi, João Vitor (2024) A Deductive Approach to Safety Assurance:Formalising Safety Contracts with Subjective Logic. In: Ceccarelli, Andrea, Bondavalli, Andrea, Trapp, Mario, Schoitsch, Erwin, Gallina, Barbara and Bitsch, Friedemann, (eds.) Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops - DECSoS, SASSUR, TOASTS, and WAISE, Proceedings. 19th Workshop on Dependable Smart Embedded and Cyber-Physical Systems and Systems-of-Systems, DECSoS 2024, 11th International Workshop on Next Generation of System Assurance Approaches for Critical Systems, SASSUR 2024, Towards A Safer Systems architectur, 17 Sep 2024 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer Nature Switzerland , ITA , pp. 213-226.
Abstract
The increasing adoption of autonomous systems in safetycritical applications raises severe concerns regarding safety and reliability. Due to the distinctive characteristics of these systems, conventional approaches to safety assurance are not directly transferable and novel approaches are required. One of the main challenges is the ability to deal with significant uncertainty resulting from (1) the inherent complexity of autonomous system models, (2) potential insufficiencies of data and/or rules, and (3) the open nature of the operational environment. The validity of assumptions made about these three layers greatly impact the confidence in the guarantees provided by a safety argument. In this paper we view the problem of safety assurance as the satisfaction of a safety contract, more specifically as a conditional deduction operation from assumptions to guarantees. We formalise this idea using Subjective Logic and derive from this formalisation an argument structure in GSN that allows for automated reasoning about the uncertainty in the guarantees given the assumptions and any further available evidence. We illustrate the idea using a simple ML-based traffic sign classification example.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. This is an author-produced version of the published paper. Uploaded in accordance with the University’s Research Publications and Open Access policy. |
Keywords: | autonomous systems,safety assurance,uncertainty |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 06 Aug 2025 09:30 |
Last Modified: | 06 Aug 2025 09:30 |
Published Version: | https://doi.org/10.1007/978-3-031-68738-9_16 |
Status: | Published |
Publisher: | Springer Nature Switzerland |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-031-68738-9_16 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:230098 |
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