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This paper introduces a novel method for verifying Operational Design Domain (ODD) coverage in safety-critical AI systems, addressing the challenge of high-dimensional parameter spaces. The approach combines parameter discretization, constraint-based filtering, and criticality-based dimension reduction to systematically define and achieve coverage metrics. The method is demonstrated using simulation data from AI-based mid-air collision avoidance research, providing a verifiable path to meet EASA certification standards.
Guaranteeing complete coverage of an AI system's operational domain in high-dimensional spaces is now possible, paving the way for safer AI deployment in aviation and other safety-critical applications.
While Artificial Intelligence (AI) offers transformative potential for operational performance, its deployment in safety-critical domains such as aviation requires strict adherence to rigorous certification standards. Current EASA guidelines mandate demonstrating complete coverage of the AI/ML constituent's Operational Design Domain (ODD) -- a requirement that demands proof that no critical gaps exist within defined operational boundaries. However, as systems operate within high-dimensional parameter spaces, existing methods struggle to provide the scalability and formal grounding necessary to satisfy the completeness criterion. Currently, no standardized engineering method exists to bridge the gap between abstract ODD definitions and verifiable evidence. This paper addresses this void by proposing a method that integrates parameter discretization, constraint-based filtering, and criticality-based dimension reduction into a structured, multi-step ODD coverage verification process. Grounded in gathered simulation data from prior research on AI-based mid-air collision avoidance research, this work demonstrates a systematic engineering approach to defining and achieving coverage metrics that satisfy EASA's demand for completeness. Ultimately, this method enables the validation of ODD coverage in higher dimensions, advancing a Safety-by-Design approach while complying with EASA's standards.
