This project aims to refine and complete the definition of the common ATM U-space interface by identifying new working areas with impact and providing a consolidated interface with a standardized data model, architecture and an operation method to achieve a minimum TRL7. This proposal addresses the topic “ATM – U-space Interface and Services” which is composed of the following solutions: - Solution 1: ATM - U-space Interface:. Achieve full integration of ATM and U-space systems, by refining and completing the definition of the common ATM U-space interface and identifying new working areas with impact on the already started common interface. - Solution 2: Dynamic Airspace Reconfiguration: Develop the complete service mentioned in U-space regulation and in their respective Guidance Material (GM) and Acceptable Means of Compliance (AMC), necessary to stablish the operating methodology and develop the standard interface to help ATC actors in charge of airspace reconfigurations to maintain traffic segregation and to avoid proximity between manned and unmanned aircraft within the designated U-space airspace.

The goal of the project is to enable human-machine collaboration by using an artificial situational awareness system which is enabling AI to anticipate and respond to human needs by understanding human intent and goals. While humans are extensively trained to understand the capabilities, limitations, and functionality of the machines they are using, further improvements in human-machine collaboration are currently hindered by lack of awareness of human's intent on the side of machines. The project will develop and test an AI Assistant Application providing adaptable human-centric support to enhance air traffic controller's (ATCO) performance and to reduce ATCO’s workload despite high task complexity. This will be achieved by development of human-machine collaboration environment that relies on recognition of ATCO intent, ATCO situation awareness (compared to machine situation awareness) and ATCO mental load. ATCO's intent will be analysed by tracking their attention and human-machine interactions and comparing them to the tasks that need solving as assessed by the artificial situational awareness system. Adaptable support will then be provided either in solving the task they are currently focused on or solving an unrelated task autonomously. This will allow ATCOs to maintain their skills and expertise while preventing a shift towards supervisory control that has been demonstrated to undermine human capability to take-over in situations with degraded automation. A goal of the adaptable and human-aware system is to maintain ATCOs in an active role, to train their skills and expertise on the job while selectively using higher levels of automation to augment capacity. ATCOs are supported in their tasks rather than substituted by automation. It is expected that ATCOs can handle high-complexity scenarios when assisted by an attention-aware support system. ATCO workload is expected to decrease with the use of support functions.