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.
Towards certifiable AI in aviation: landscape, challenges, and opportunities
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abstract
Artificial Intelligence (AI) methods are powerful tools for various domains, including critical fields such as avionics, where certification is required to achieve and maintain an acceptable level of safety. General solutions for safety-critical systems must address three main questions: Is it suitable? What drives the system's decisions? Is it robust to errors/attacks? This is more complex in AI than in traditional methods. In this context, this paper presents a comprehensive mind map of formal AI certification in avionics. It highlights the challenges of certifying AI development with an example to emphasize the need for qualification beyond performance metrics.
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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.