HUCAN: Towards Certification-Aware Design For Advanced Automation Solutions

Abstract

This paper presents the outcome of research carried out in the SESAR Joint Undertaking's HUCAN project, which aims to develop a holistic, unified certification framework for highly automated systems, to ensure their safe and efficient integration into air traffic management. Based on an analysis of current and innovative certification approaches, the project developed two solutions: 1) a structured, iterative methodology that facilitates certification alignment and validation of advanced automation; 2) preliminary guidelines, which, based on a gap analysis between EASA AI guidance for Level 1 and 2 Machine Learning (ML) applications and the SESAR Project Handbook, offer directions for addressing these gaps along the research pipeline. The approach proposed by these solutions offers significant benefits by promoting an early and proactive alignment of design strategies with certification objectives, thereby mitigating late-stage risks and fostering a more efficient and predictable path to deployment for these innovative systems.