提出可验证的高维ODD覆盖方法，助力航空AI系统通过严格认证
From High-Dimensional Spaces to Verifiable ODD Coverage for Safety-Critical AI-based Systems
- 通过参数离散化与关键维度压缩，构建可扩展的ODD验证流程
- 基于仿真数据实现高维空间下覆盖完整性的形式化证明
- 适合需符合EASA标准的航空、自动驾驶等安全关键AI系统
人工智能在航空等安全关键领域虽具变革潜力，但其部署须满足严格的认证标准。欧洲航空安全局（EASA）要求证明AI/ML系统的运行设计域（ODD）全覆盖，即确保定义边界内无关键遗漏。然而，系统运行于高维参数空间时，现有方法难以提供可扩展且形式化的覆盖证明，缺乏标准化工程手段将抽象的ODD定义转化为可验证证据。本文提出一种集成参数离散化、约束过滤与关键性维度缩减的多阶段验证方法，基于前期关于基于AI的空中避撞研究的仿真数据，构建了系统化的覆盖度量框架，实现高维空间下覆盖完整性的可验证性，推动安全设计原则落地，符合EASA标准。
原文摘要 · Abstract (English)
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.
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