The Flight Centric ATC project (FCA) addresses Industrial Research & Validation for Master Plan Phase C Connected and Automated ATM (HORIZON-SESAR-2022-DES-IR-01-WA6-1). The main objective of FCA is to finally develop (V3 Maturity level) the Flight Centric ATC concept for an ECAC-wide implementation in medium density traffic areas considering the existing national boundaries. Furthermore, the project includes the development of ECAC-wide deployment scenarios, which are consistent with the limitations of the VHF spectrum for voice communications. ECAC-wide benefits of the concept in each of the deployment scenarios will be analyzed as well, based on the exiting SESAR2020, PJ.10-W2-73 FCA Cost-Benefit Analysis. Two different exercises and workshops will be conducted during the project to address the remaining gaps and to take the final step from a V3 on-going solution to a V3 completed solution. Therefore, an analysis will be conducted to validate the Key Performance Indicators: Cost Efficiency (CEF2), Capacity (CAP), Predictability (PRD) and Fuel Efficiency (FEFF), as well as Human Performance and Safety. In addition, final validations will take place to validate e.g. the change from a sectorised airspace to Flight Centric ATC and vice versa, to assess the possibility of a fallback scenario, to evaluate an appropriate allocation strategy and an acceptable approach for the opening and closing of CWPs. The final results will be dissiminated across the relevant stakeholders and the S3JU via scientific papers, open days and a project website specificly designated to the Industrial Research Flight Centric ATC project.

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.