Herd, BenjaminBenjaminHerdZacchi, Joao-VitorJoao-VitorZacchiBurton, SimonSimonBurton2024-10-012024-10-012025-02-262024-10-012024https://publica.fraunhofer.de/handle/publica/47592610.1007/978-3-031-68738-9_16The increasing adoption of autonomous systems in safety-critical 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.ensafety assuranceuncertaintyautonomous systemsconference paper