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Guidance on the Safety Assurance of Autonomous Systems in Complex Environments (SACE)

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arxiv 2208.00853 v2 pith:6PYRDRNO submitted 2022-08-01 cs.SE cs.SYeess.SY

Richard Hawkins , Rob Alexander , Matt Osborne , Mike Parsons , Mark Nicholson , John McDermid , Ibrahim Habli This is my paper · ORCID
classification cs.SEcs.SYeess.SY
keywords safetyautonomousenvironmentssystemsapplicationsassurancecomplexsace
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Autonomous systems (AS) are systems that have the capability to take decisions free from direct human control. AS are increasingly being considered for adoption for applications where their behaviour may cause harm, such as when used for autonomous driving, medical applications or in domestic environments. For such applications, being able to ensure and demonstrate (assure) the safety of the operation of the AS is crucial for their adoption. This can be particularly challenging where AS operate in complex and changing real-world environments. Establishing justified confidence in the safety of AS requires the creation of a compelling safety case. This document introduces a methodology for the Safety Assurance of Autonomous Systems in Complex Environments (SACE). SACE comprises a set of safety case patterns and a process for (1) systematically integrating safety assurance into the development of the AS and (2) for generating the evidence base for explicitly justifying the acceptable safety of the AS.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. MATRIX: Multi-Agent simulaTion fRamework for safe Interactions and conteXtual clinical conversational evaluation

    cs.AI 2025-08 conditional novelty 5.0 of 10

    MATRIX combines a structured safety taxonomy, an LLM hazard judge, and a patient simulator to benchmark clinical dialogue agents, claiming expert-level hazard detection and revealing weak emergency handling in current LLMs.

  2. Probabilistic Safety Verification for an Autonomous Ground Vehicle: A Situation Coverage Grid Approach

    cs.RO 2025-07 conditional novelty 5.0 of 10

    A situation coverage grid is augmented with transition probabilities and checked with probabilistic model checking to rank AGV situations by collision risk.

  3. SCALOFT: An Initial Approach for Situation Coverage-Based Safety Analysis of an Autonomous Aerial Drone in a Mine Environment

    cs.RO 2025-05 conditional novelty 4.0 of 10

    SCALOFT, a situation coverage-based testing approach for a simulated mine drone, detected all three seeded faults it was tested against.

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