Project description
Air traffic control is reputed as one of the five most stressful professions there is. Addressing the mental workload of controllers is therefore an important area of SESAR research and innovation and one that can be addressed using artificial intelligence. The project aims to develop a digital assistant capable of predicting future traffic, and assessing controllers’ stress levels and attention span, and whether they would be capable of handling the anticipated workload. The assistant would decide how to act, following an adaptation strategy: it may, for instance, increase the level of automation, enable additional AI-based tools, or request changes to the airspace (sector splitting).
Project objectives
The strategic objective of the project is to develop a system in which tasks are performed collaboratively by hybrid human-machine teams and dynamically allocated through adaptive automation principles. This will increase the efficiency, capacity, and safety of ATM, maximizing Human-AI teaming.
Latest news & events
SESAR Innovation Days 2025 #2
5 of December 2025
Our paper, “Human–Machine Performance Envelope: Controller Adaptive Digital Assistant Evaluation”, was featured in the Human Factors and Decision Support Tools technical session. Presented by Stefano B., CODA Project Coordinator, the work dives into the exercises validation results — offering new insights into how adaptive digital […]
SESAR Innovation Days 2025 #1
4 of December 2025
The conference turned its focus to human-machine teaming, as the session—moderated by SESARJU’s Olivia Nunez—explored how advanced automation, AI, and operational expertise can converge to support safer, more efficient, and more sustainable ATM. Juan Alberto Besada Portas Universidad Politécnica de Madrid said ATM must manage […]
Technical results accomplished
17 of November 2025
With the submission of the Exploratory Research Report, all technical activities and deliverables of the project have now been successfully completed. If you are interested in the project’s insights into AI models, system architecture, impacts on ATCO performance, and other key aspects of the technical implementation, you can explore […]
ACKNOWLEDGEMENT
This project has received funding from the SESAR Joint Undertaking under the European Union’s Horizon Europe research and innovation programme under grant agreement No 101114765
CONTACTS
Project Coordinator
STEFANO BONELLI
Dissemination Leader
JUAN ALBERTO BESADA PORTAS
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