This proposal addresses the topic “Digitalisation and Automation principles for ATM”. Automation is one of the most promising solutions for the capacity problem, however, to implement advanced automation concepts it is required that the AI and human are able to share the situational awareness. Exploring the effect of, and opportunities for, distributed human-machine situational awareness in en-route ATC operations is one of the main objectives of this project.

Instead of automating isolated individual tasks, such as conflict detection or coordination, we propose building a foundation for automation by developing an intelligent situationally-aware system. Sharing the same team situational awareness among ATCO team members and AI will enable the automated system to reach the same conclusions as ATCOs when confronted with the same problem and to be able to explain the reasoning behind those conclusions.

The challenges of transparency and generalization will be solved by combining machine learning with reasoning engine (including domain-specific knowledge graphs) in a way that emphasizes their advantages. Machine learning will be used for prediction, estimation and filtering at the level of individual probabilistic events, an area where it has so far shown great prowess, whereas reasoning engine will be used to represent knowledge and draw conclusions based on all the available data and explain the reasoning behind those conclusions. We will explore to what extent it is possible to deduce machine learning false estimates and how resilient such system is to failure. In this way, the artificial situational awareness system will be the enabler of future advanced automation based on machine learning.