About<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Context<\/p>\n<\/div><\/div><\/div><\/div><\/div>
In the forthcoming years, the role of highly automated and autonomous systems, including Artificial Intelligence (AI)-powered technologies, is going to increase at a fast pace in complex socio-technical systems. The Air Traffic Management (ATM) domain is not an exception.<\/p>\n
Certification of AI in ATM is a complex and evolving field that requires collaboration between aviation experts, AI researchers, regulatory bodies, and industry stakeholders to address these challenges and ensure the safe and efficient integration of AI into the aviation sector.<\/p>\n
The HUCAN project will develop a novel, holistic certification approach tailored to AI-enabled ATM airborne and ground systems, setting new standards for transparency, certification, and approval processes, ensuring the highest levels of safety and efficiency in the aviation sector.<\/p>\n
The HUCAN project also plans to provide a comprehensive set of design guidelines and accompanying toolkits that will streamline the development of automation and AI-powered technologies for manufacturers in the aviation industry.<\/p>\n
The project is aligned with the main European research and innovation initiatives in the field.<\/p>\n<\/div><\/div><\/div><\/div><\/div>
Case driven approach<\/p>\n<\/div>
The HUCAN project adopts a case study-based approach<\/strong> and focuses on novel operational concepts and technologies for capacity on demand and dynamic airspace, including the integration of highly automated and AI-powered solutions and innovative services for U-Space.<\/p>\n Four case studies<\/strong> will be used to support the design and the validation of the holistic and unified approach to certification defined by the project.<\/p>\n<\/div><\/div><\/div><\/div><\/div> Dynamic airspace sectoring<\/u><\/strong><\/p>\n<\/div> Purpose<\/strong><\/p>\n Improvement of middle airspace utilisation obtained by means of dynamic optimization of the airspace sector configuration.<\/p>\n Objectives<\/strong><\/p>\n Dynamically define and apply the best allocation of elementary sectors for:<\/p>\n Flow management optimization<\/p>\n<\/div><\/div><\/div> AI-powered digital assistants in Terminal Manoeuvring Area<\/u><\/b><\/p>\n<\/div> Purpose<\/strong><\/p>\n Enhance runway efficiency by optimising aircraft routing, ensuring adherence to procedures, and preventing potential conflicts.<\/span><\/p>\n Objectives<\/strong><\/p>\n Provide an AI-powered digital assistant to support ATCOs to effectively manage inbound traffic and ensure continuous descent operations, with benefits for:<\/span><\/p>\n Dynamic airspace reconfiguration service for U-Space<\/u><\/b><\/p>\n<\/div> Purpose<\/strong><\/p>\n Dynamic U-Space volumes definition and information exchanges between ATM and U-Space<\/span><\/p>\n Objectives<\/strong><\/p>\n Support ATCOs in shaping, activating\/deactivating U-Space volumes to UAS traffic for management of priority operations, emergencies, of manned aviation in U-Space, with benefits in<\/span><\/p>\n Dynamic allocation of traffic between ATCO and system<\/u><\/b><\/p>\n<\/div> Purpose<\/strong><\/p>\n Improvement of upper airspace utilisation by means of dynamic allocation of traffic between the ATCO and ARGOS (Air traffic control Real Ground-breaking Operational System).<\/span><\/p>\n Objectives<\/strong><\/p>\n Dynamically support the ATCOs in managing the traffic in the sector, by means of issuing operational clearances to safely handle basic traffic situations and aid controllers in handling complex traffic situations. ARGOS has 3 modes of use. Two of them will be taken into account in HUCAN: the autonomous management of the traffic by ARGOS in specific circumstances and the hybrid management of the traffic between the ATCO and the ARGOS system (dynamic allocation of traffic)<\/span><\/p>\n<\/div><\/div><\/div><\/div><\/div> Expected outcomes<\/p>\n<\/div> A new holistic and unified certification method<\/strong> for ATM related systems based on advanced automation, whose main targeted users are EASA, national aviation authorities, qualified entities and Air navigation Service Providers.<\/p>\n<\/div><\/div><\/div><\/div><\/div> Stakeholder Consultation Group<\/p>\n<\/div> The Stakeholder Consultation Group (SCG) will serve as the nexus for stakeholder engagement. This group will be involved in direct and ongoing dialogues to discuss the project’s outputs and their practical applications.<\/span><\/p>\n Members of the SCG are the <\/span>SESAR projects<\/b> aligned with the flagship of <\/span>Capacity-on-demand and dynamic airspace and Artificial Intelligence for aviation<\/a><\/i><\/b>.<\/span><\/p>\n The diversity in developmental stages among the projects within the SCG is crucial as it offers a broader perspective and deeper insights into the challenges and opportunities within the domain of ATM automation and AI integration.<\/span><\/p>\n<\/div><\/div><\/div><\/div><\/div> Newsletter<\/p>\n<\/div> Want to know about the future of Artificial Intelligence in aviation? Join HUCAN Community!<\/b><\/p>\n<\/div><\/div><\/div>\n
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<\/span>ARGOS plan<\/h2><\/div><\/div><\/div><\/div><\/div>