Runway Sign Classifier: A DAL C Certifiable Machine Learning System

TL;DR

This paper demonstrates how to certify a machine learning-based airport sign classifier for aviation standards by employing redundancy and data management techniques to meet strict safety requirements.

Contribution

It introduces a certification approach for ML systems in aviation, upgrading a sign classifier to meet DAL C standards using redundancy and data management.

Findings

Achieved DAL C certification for an ML-based sign classifier

Implemented dual DNNs for system redundancy

Enhanced certification process with ML-specific data techniques

Abstract

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.