Graphical safety assurance case using Goal Structuring Notation (GSN) — challenges, opportunities and a framework for autonomous trains

Mohammed Chelouati, Abderraouf Boussif, Julie Beugin and El-Miloudi El Koursi

Reliability Engineering and System Safety, 2023, vol. 230, issue C

Abstract: The development of fully autonomous vehicles is an ambition that took seed in the automotive industry a few years ago and is now growing in the railways considering their benefits. The main objective of autonomous train is to perform its operations and assure its mission with an acceptable safety level in all possible operational conditions. Such an objective needs to be supported by a safety demonstration. In order to authorize the operations of railway systems, they must be proven safe. This requires a technical and operational safety assessment, and also a safety assurance process during the system’s whole life-cycle. The goal of such activities is to ensure that designed systems comply with railway safety standards and regulations. Both safety arguments and evidences are required to demonstrate that this compliance is achieved. These sets of evidence are documented in a so-called safety case. Recently, graphical safety cases, such as Goal Structuring Notation (GSN)-based safety case, have become an interesting alternative to narrative reports and plain texts. The graphical structure and visual properties improve the presentation and comprehension of the safety arguments. In this paper, we firstly review the use of the GSN for building graphical safety case for different transportation systems, with a focus on the railway domain. Then, we discuss the opportunities and challenges of considering such an approach in railway and we propose a high-level framework for building the GSN-based safety assurance case for the autonomous trains.

Keywords: Railway safety; Autonomous trains; Safety assurance; Safety case; Goal Structuring Notation (GSN) (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005488

DOI: 10.1016/j.ress.2022.108933

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