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).

The system will bring together state-of-the-art from different fields:

• Prediction models to foresee future situations, ATCO activities and their impact on the same human performance;
• Neurophysiological assessment of mental states to enable the system to know operators' real current level of workload, attention, stress, fatigue, and vigilance by validating the predicted cognitive models and maximising the effectiveness of the interaction between the human and the machine by developing an HMPE (Human Machine Performance Envelope);
• Implementation of advanced adaptable and adaptive automation principles to dynamically guide the allocation of tasks to the ATCO to the digital assistant, reacting to real-time and predicted operator's cognitive status by calculating the impact of future tasks in terms of cognitive complexity.