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.

翻译：尽管人工智能（AI）在运行性能方面具有变革性潜力，但其在航空等安全关键领域的部署需要严格遵守严格的认证标准。当前的EASA指南要求展示AI/ML组件的运行设计域（ODD）的完全覆盖——这一要求需要证明在定义的操作边界内不存在关键缺口。然而，当系统在高维参数空间中运行时，现有方法难以提供满足完整性标准所需的可扩展性和形式化基础。目前，尚无标准化的工程方法能够弥合抽象ODD定义与可验证证据之间的差距。本文通过提出一种将参数离散化、基于约束的过滤以及基于关键性的降维整合为结构化、多步骤ODD覆盖验证过程的方法，填补了这一空白。本研究基于先前关于基于AI的空中防撞研究的仿真数据，展示了一种系统性的工程方法，用于定义和实现满足EASA完整性要求的覆盖度量。最终，该方法能够验证高维空间中的ODD覆盖，在符合EASA标准的同时推进了“安全设计”方法。