A semi-automated certification framework for DO-178C Level D ML systems is demonstrated on a YOLOv8 vehicle detector, producing a Moderate Assurance score of 74.7.
I came, I saw, I certified: some perspectives on the safety assurance of cyber-physical systems
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The execution failure of cyber-physical systems (e.g., autonomous driving systems, unmanned aerial systems, and robotic systems) could result in the loss of life, severe injuries, large-scale environmental damage, property destruction, and major economic loss. Hence, such systems usually require a strong justification that they will effectively support critical requirements (e.g., safety, security, and reliability) for which they were designed. Thus, it is often mandatory to develop compelling assurance cases to support that justification and allow regulatory bodies to certify such systems. In such contexts, detecting assurance deficits, relying on patterns to improve the structure of assurance cases, improving existing assurance case notations, and (semi-)automating the generation of assurance cases are key to develop compelling assurance cases and foster consumer acceptance. We therefore explore challenges related to such assurance enablers and outline some potential directions that could be explored to tackle them.
fields
cs.SE 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
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Approach Towards Semi-Automated Certification for Low Criticality ML-Enabled Airborne Applications
A semi-automated certification framework for DO-178C Level D ML systems is demonstrated on a YOLOv8 vehicle detector, producing a Moderate Assurance score of 74.7.