In recent years, the remarkable progress of Machine Learning (ML)
technologies within the domain of Artificial Intelligence (AI) systems has
presented unprecedented opportunities for the aviation industry, paving the way
for further advancements in automation, including the potential for single
pilot or fully autonomous operation of large commercial airplanes. However, ML
technology faces major incompatibilities with existing airborne certification
standards, such as ML model traceability and explainability issues or the
inadequacy of traditional coverage metrics. Certification of ML-based airborne
systems using current standards is problematic due to these challenges. This
paper presents a case study of an airborne system utilizing a Deep Neural
Network (DNN) for airport sign detection and classification. Building upon our
previous work, which demonstrates compliance with Design Assurance Level (DAL)
D, we upgrade the system to meet the more stringent requirements of Design
Assurance Level C. To achieve DAL C, we employ an established architectural
mitigation technique involving two redundant and dissimilar Deep Neural
Networks. The application of novel ML-specific data management techniques
further enhances this approach. This work is intended to illustrate how the
certification challenges of ML-based systems can be addressed for medium
criticality airborne applications.

近年来，机器学习（ML）技术在人工智能（AI）系统领域取得了显著进展，为航空业带来了前所未有的自动化机会，包括大型商用飞机的单人驾驶或完全自主飞行。然而，ML 技术与现有的空中认证标准存在严重不兼容性，如 ML 模型的可追踪性和可解释性问题，或者传统覆盖度指标的不适用性。当前标准下使用基于 ML 的空中系统的认证存在问题。本文通过一个案例研究，介绍了一个利用深度神经网络（DNN）进行机场标志检测和分类的空中系统。在我们之前的工作基础上，证明了系统符合设计保证等级（DAL）D 的要求，我们将系统升级以满足更严格的设计保证等级 C 的要求。为了实现 DAL C，我们采用了一种已建立的架构缓解技术，涉及两个冗余且不同的深度神经网络。对于这种方法，采用了创新的 ML 特定数据管理技术进行加强。本文旨在说明如何解决中等重要性空中应用中基于 ML 的系统的认证挑战。

跑道标志分类器：一种可通过 DAL C 认证的机器学习系统

Runway Sign Classifier: A DAL C Certifiable Machine Learning System

The exceptional progress in the field of machine learning (ML) in recent
years has attracted a lot of interest in using this technology in aviation.
Possible airborne applications of ML include safety-critical functions, which
must be developed in compliance with rigorous certification standards of the
aviation industry. Current certification standards for the aviation industry
were developed prior to the ML renaissance without taking specifics of ML
technology into account. There are some fundamental incompatibilities between
traditional design assurance approaches and certain aspects of ML-based
systems. In this paper, we analyze the current airborne certification standards
and show that all objectives of the standards can be achieved for a
low-criticality ML-based system if certain assumptions about ML development
workflow are applied.

本文分析目前航空认证标准，显示特定机器学习开发工作流可做到低级 ML 系统达成标准。