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 need
for more powerful, reliable and robust equipment for future
software defined aircraft functions. Of particular interest in
achieving this is the field of Artificial Intelligence (AI) and its
subset of Machine Learning (ML) algorithms. The use of AI/ML
within the aviation industry, however, poses significant challenges,
particularly connected to safety, reliability and certifiability.
This paper is about the OpenCAS, a collision avoidance system
based on Feed-Forward Neural Networks. It reports hands-on
experience and outlooks on systems engineering practice for ML
model integration. The architectural design considerations are
elaborated. Particular focus is laid on constraints imposed by
the use of multiple networks within the system