A Deductive Approach to Safety Assurance: Formalising Safety Contracts with Subjective Logic

TY - GEN

T1 - A Deductive Approach to Safety Assurance

T2 - 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 architecture Through Security, TOASTS 2024 and 7th International Workshop on Artificial Intelligence Safety Engineering, WAISE 2024 held in conjunction with the 43rd International Conference on Computer Safety, Reliability, and Security, SAFECOMP 2024

AU - Herd, Benjamin

AU - Burton, Simon

AU - Zacchi, João Vitor

N1 - © 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.

PY - 2024/9/9

Y1 - 2024/9/9

N2 - 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.

AB - 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.

KW - autonomous systems

KW - safety assurance

KW - uncertainty

UR - https://www.scopus.com/pages/publications/85204639985

U2 - 10.1007/978-3-031-68738-9_16

DO - 10.1007/978-3-031-68738-9_16

M3 - Conference contribution

AN - SCOPUS:85204639985

SN - 9783031687372

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 213

EP - 226

BT - Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops - DECSoS, SASSUR, TOASTS, and WAISE, Proceedings

A2 - Ceccarelli, Andrea

A2 - Bondavalli, Andrea

A2 - Trapp, Mario

A2 - Schoitsch, Erwin

A2 - Gallina, Barbara

A2 - Bitsch, Friedemann

PB - Springer Nature Switzerland

Y2 - 17 September 2024 through 17 September 2024

ER -