Humans are trained to understand the capabilities, limitations, and functionality of the machines they use. Machines, on the other hand, remain disconnected from humans and lack understanding of them. In a complex environment the necessity for machines to comprehend the humans and to be able to close the loop of reciprocal understanding, grows more important.
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.
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.
To develop an enhanced artificial situational awareness system by implementing methods for assessment of AT CO's intent and goals.
To develop a method for the adaptable selection and implementation of actions that support ATCOs, utilizing both ML and non-ML tools.
To develop the capability to track human visual attention in combination with other inputs and to give them semantics by putting them in context of the current traffic situation.
Research the methods of identifying loss of situational awareness (ranging from inadequate selectivity to degraded 5A to complete out-of-the-loop state) and exploring options for bringing the human back into the loop.
To define specifications for interoperability with other systems and roles in ATM (e.g. AT5EP, FMP) in order to explore benefits of artificial situational awareness system beyond ATC tactical operations.
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Develop the Artificial Situational Awareness Solution to TRL2
We will improve on the capabilities of the Artificial Situational Awareness (ASA) System
Develop a method for assessment of ATCO’s intent
Develop an AI Assistant Application showcasing the capabilities of the ASA solution in the HITL simulations experiment.
Develop a method for adaptable selection and implementation of supporting and/or direct actions
The system will provide on additional safety net by keeping ATCOs in the loop and developing strategies in the case of loss of situational awareness. Keeping the ATCO in the loop would prevent activation of other safety nets so we expect reduction of those alerts by 20%.
With ASA-based Al Assistant Application upporting ATCOs inperforming their tasks or performing additional tasks autonomously, the amount of traffic that the ATCO can handle will increase. We expect significant increase in en-route capacity.
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This project is supported by the European Union, the SESAR 3 Joint Undertaking and its founding members. Views and opinions expressed are, however, those of the author(s) only and do not necessarily reflect those of the European Union or SESAR 3 Joint Undertaking. Neither the European Union nor the granting authority can be held responsible for them.
