AI 中文总结
本研究针对AI防撞系统的ODD代表性评估问题，提出采用Kullback-Leibler散度和Cramér's V的覆盖率驱动验证方法，契合EASA安全标准，为安全关键AI应用的设计安全提供支撑。
AI 中文摘要
人工智能（AI）为未来航空系统提供了巨大潜力，但其在安全关键应用中的集成需符合航空领域严格的安全标准。对于基于AI和机器学习（ML）的系统，欧洲航空安全局（EASA）强调需证明运行设计域（ODD）及其开发和验证阶段所用相关数据分布的代表性与完整性。尽管有此要求，但定义目标分布并评估ODD内代表性的结构化工程流程仍未得到充分探索。本研究提出一种用于航空安全保障背景下AI/ML组成部分ODD代表性评估的方法：从系统识别合适的目标分布入手，提出一套流程，指导开发者完成ODD定义、参数分布建模，直至针对EASA的学习保障目标对覆盖率结果进行定量评估与解读。作为定量度量，本研究检验了卡方拟合优度检验，发现其不适用于本场景中产生的大数据集，因此采用Kullback-Leibler散度和Cramér's V进行代表性评估。该方法以基于AI的机载防撞为例进行验证，采用了此前水平防撞系统（HCAS）和垂直防撞系统（VCAS）模拟的实验数据。结果表明，统计分布比较方法可支持安全关键AI应用的代表性评估，并有助于构建符合EASA最新指南的系统化设计安全型AI工程流程。
英文摘要
Artificial Intelligence (AI) offers significant potential for future aviation systems; however, its integration into safety-critical applications requires compliance with the aviation sector's stringent safety standards. For AI and Machine Learning (ML)-based systems, the European Union Aviation Safety Agency (EASA) emphasizes the need to demonstrate the representativeness and completeness of the Operational Design Domain (ODD) and the associated data distributions used during development and verification. Despite this requirement, a structured engineering process for defining target distributions and evaluating representativeness within ODDs remains largely unexplored. This work presents a method for representativeness assessment of AI/ML constituent ODDs in the context of aviation safety assurance. Starting from the methodical identification of suitable target distributions, a process flow is proposed that guides developers from ODD definition and parameter distribution modeling to the quantitative assessment and interpretation of coverage results with respect to EASA's learning assurance objectives. As quantitative measures, the chi-squared goodness-of-fit test is examined and found unsuitable for the large data sets arising in this setting, leading to the adoption of the Kullback--Leibler divergence and Cramér's $V$ for the representativeness assessment. The method is demonstrated using the example of AI-based airborne collision avoidance, employing experimental data from previous Horizontal Collision Avoidance System (HCAS) and Vertical Collision Avoidance System (VCAS) simulations. The results illustrate how statistical distribution comparison methods can support the assessment of representativeness for safety-critical AI applications and contribute toward a systematic Safety-by-Design AI engineering process aligned with emerging EASA guidance.
Journal ref35th Congress of the International Councilof the Aeronautical Sciences (ICAS) 2026
