As the aviation sector embraces artificial intelligence (AI) and advanced automation, a profound transformation is underway in the regulatory landscape and certification requirements. To ensure that these emerging solutions are robust, safe, and trustworthy, the industry must gradually bridge the gap between traditional validation frameworks and the evolving certification demands. This transition is key to maximising returns on research and development (R&D) investments and accelerating the journey from research to real-world deployment.

To support this transition, HUCAN has developed a set of preliminary guidelines aimed at facilitating the integration of certification objectives into SESAR-funded research. The HUCAN preliminary guidelines provide practical recommendations to help align SESAR validation frameworks with the evolving certification requirements for AI in aviation. This approach addresses the key performance areas (KPAs) outlined in both the European Union Aviation Safety Agency (EASA) AI Roadmap 2.0 and the SESAR 3JU Project Handbook, including concept design, technical assurance (safety and security), human factors, and ethics.

Targeted at SESAR projects within both exploratory and industrial research streams, the design recommendations are intended to help research teams progressively and consistently incorporate certification considerations across all development stages—complementing existing SESAR processes and guidance materials.

This approach is based on the SESAR  Project Handbook and addresses the KPAs identified in the EASA AI Roadmap 2.0 and its supporting concept papers. It also considers the general provisions and strategic directions introduced by the AI Act (Reg. (EU) 2024/1689) for the implementation of the new requirements related to high-risk AI systems in aviation (especially Article 108).

BENEFITS

• Reduce the gap between research and time to market

• Foster homogeneity of approach among projects

• Exploit synergies between research and certification