From Operational Scenarios to Synthetic Data: Simulation-Based Data Generation for AI-Based Airborne Systems

Abstract

Developing safety-critical AI-based systems is an emerging challenge in aviation. Amongst others, the recent concept paper of the European Union Aviation Safety Agency (EASA), "First usable guidance for Level 1 machine learning applications", provides invaluable insights to tackle this challenge. It particularly highlights the importance of synthetic data for training, validation and testing as a means of complementing real-world data for completeness and representatives. The primary source of synthetic data is simulations. The literature recognizes simulation not only as a crucial data source but an effective method for verification. This paper uses EASA guidance as a baseline to propose a simulation-based data generation process for AI-based airborne systems.