We give a first rigorous characterization of Operational Design Domains (ODDs) for Machine
Learning (ML)-based aeronautical products. Unlike in other application sectors (such as self …

In this case study, we have explored the use of a neural network model checker to analyze
the safety characteristics of a neural network trained using reinforcement learning to …

We demonstrate the use of formal methods to verify properties of a deep convolutional
neural network that estimates remaining useful life of aircraft mechanical equipment. We …

The use of machine learning (ML) in airborne safety-critical systems requires new methods
for certification, as the current standards and practices were defined and refined over …

The aviation literature gives relatively little guidance to practitioners about the specifics of
architecting systems for safety, particularly the impact of architecture on allocating safety …

Emerging trends in Advanced Air Mobility (AAM) are pushing the boundaries of the
established design approaches and are forcing developers to find new ways to fulfill the …

Advances in machine learning (ML) open the way to innovating functions in the avionic
domain, such as navigation/surveillance assistance (eg, vision-based navigation, obstacle …

While Artificial Intelligence (AI) has become an important asset in many areas of science and
technology, safety is often not treated as important as required for aviation. Neglecting safety …

Goal structuring notation (GSN) is commonly proposed as a structuring tool for arguing
about the high-level properties (eg safety) of a system. However, this approach does not …

Driven by recent successes in artificial intelligence, new autonomous navigation systems
are emerging in the urban space. The adoption of such systems raises questions about …