Florian Holzapfel

Konstantin Dmitriev, Johann Schumann, Islam Bostanov et al.

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.

Daniel Dollinger, Julian Rhein, Kevin Schmiechen et al.

An emerging service is moving the known aviation sector in terms of technology, paradigms, and key players - the Urban Air Mobility. The reason: new developments in non-aviation industries are driving technological progress in aviation. For instance electrical motors, modern sensor technologies and better energy storage expand the possibilities and enable novel vehicle concepts which require also novel system architectures for flight control systems. Their development is governed by aviation authority and industry recognized standards, guidelines and recommended practices. Comprehensive methods for Model-Based Systems Engineering exist which address these guidance materials but their setup and their application can be quite resource-demanding. Especially the new and rather small key players - start-ups and development teams in an educational environment - can be overwhelmed to setup such development processes. For these clients, the authors propose a custom workflow for the development of system architectures. It shall ensure development rigor, quality and consistency. The authors show how the custom workflow has been established based on the ARP4754A and its level of compliance to the standard's process objectives. Based on automation of life cycle activities, manual effort can be reduced to allow the application even in small teams. The custom workflow's activities are explained and demonstrated within a case study of an Experimental Autopilot system architecture.

Konstantin Dmitriev, Shanza Ali Zafar, Kevin Schmiechen et al.

The emergence of a global market for urban air mobility and unmanned aerial systems has attracted many startups across the world. These organizations have little training or experience in the traditional processes used in civil aviation for the development of software and electronic hardware. They are also constrained in the resources they can allocate for dedicated teams of professionals to follow these standardized processes. To fill this gap, this paper presents a custom workflow based on a subset of objectives derived from the foundational standards for safety critical software DO-178C/DO-331. The selection of objectives from the standards is based on the importance, degree of automation, and reusability of specific objectives. This custom workflow is intended to establish a lean and highly automated development life cycle resulting in higher quality software with better maintainability characteristics for research and prototype aircraft. It can also be proposed as means of compliance for software of certain applications such as unmanned aircraft systems, urban air mobility and general aviation. By producing the essential set of development and verification artifacts, the custom workflow also provides a scalable basis for potential future certification in compliance with DO-178C/DO-331. The custom workflow is demonstrated in a case study of an Autopilot Manual Disconnection System.