Runway Sign Classifier: A DAL C Certifiable Machine Learning System
Konstantin Dmitriev Johann Schumann Islam Bostanov Mostafa Abdelhamid Florian Holzapfel
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.