AI Situational Awareness Foundation for Advancing Automation

Dissemination

Radišić T., Andraši .P., Novak D., Rogošić T.: The Proposal of a Concept of Artificial Situational Awareness in ATC in Engineering Power : Bulletin of the Croatian Academy of Engineering, Vol. 15 (2), 2020

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This concept of operations is developed as a base for human-machine distributed situational awareness that will be used as support in en-route ATC monitoring tasks. It proposes the monitoring tasks which could be assigned to AI in this project. The concept of operations describes the expected changes between the current concept of operations, future concepts which do not consider humanmachine distributed situational awareness, and the proposed concept which includes the AI into the team situational awareness.

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The analysis shows which monitoring tasks exist, which of them can be automated in different scenarios (medium/high automation), and most importantly what are requirements for their automation in terms of needed data, changes in operations, changes in the user interface, and the possible effect on human operators.

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This work deals with monitoring tasks focusing on situational awareness. This approach applies ML techniques to perform predictions about separation infringements and safety metrics associated with the intrinsic characteristics of the separation between an aircraft pair. Herein, this work focuses on the concept of Situation of Interest (SI). One SI is when an aircraft pair is expected to intersect with a horizontal separation lower than a pre-defined separation and infringe the vertical separation minima. The safety metrics are the Minimum Distance, the distance and the time to reach the Minimum Distance for each aircraft pair. Moreover, it has been developed two approaches with similar roles of the Air Traffic Controller’s (ATCO) team. The Static mode focuses on planner ATCO. This mode predicts SI and their safety metrics when an aircraft pierces into the airspace with the aircraft located within the airspace. The Dynamic mode focuses on tactical ATCO. This mode predicts SI and their safety metrics throughout the aircraft's evolution within the airspace, and it receives the 4DT prediction of the aircraft within the airspace.

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Air traffic complexity models use different bases for determining complexity. The goal is to build an air traffic complexity model which will be able to determine complexity regardless of the traffic situation, observed airspace, or air traffic controller. This is done by implementing a novel solution that uses air traffic controller tasks, defined depending on the traffic situation, to determine air traffic complexity. This approach offers a solution to certain problems recognized in previous complexity models.

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In this deliverable (D 4.1) we describe an architecture for the data and metadata in such a KG and for the software components for incrementally processing and querying the data and metadata in the KG. The Proof-of-Concept KG System exemplifies this architecture and has the purpose of guiding further developments in WP 4 and WP 5. The proposed architecture of a KG system facilitates SPARQL Queries Capturing Monitoring Tasks based on traffic/airspace data converted to RDF. It further accomodates the integration with other components such as the Reasoning Engine in Prolog developed in Task 4.2 and Machine Learning Modules developed in WP3. The main goal is to develop and assess the concept of the artificial SA based on a KG from a functional perspective rather than to consider requirements of a real-time life system. In this deliverable we also describe the UML-to-RDFS/SHACL mapper which facilitates the transformation from information exchange models such as AIXM and FIXM modeled in UML to KG schemas in RDFS and SHACL.

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The AISA website is obviously the main tool for communication, a central reference for the project and a data repository of all the main results. It is also a dissemination tool, e.g. the technical deliverables, papers will be accessible there, but the main purpose is to support communication, either indirectly or directly. Indirectly, it means that there are and will be information there, other communication tools can refer to and directly, when there is an events page for example where participants can register for a workshop.

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This document describes the Data Management Plan (DMP) of the AISA project according to the guidelines described in the Guidelines on Data Management in Horizon 2020 document [1]. As such, this DMP describes the data management life cycle for all datasets to be collected, processed, or generated by AISA project during its research activities. It details all data the project will collect and generate, how it will be exploited or made accessible for verification and re-use, and how it will be curated and preserved.

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Previous related work can be found at the website of the BEST project: www.project-best.eu

Contact information

Prof. Tomislav Radišić
University of Zagreb
tradisic@fpz.unizg.hr

This project has received funding from the SESAR Joint Undertaking under Grant Agreement No. 892618, under European Union’s Horizon 2020 Research and Innovation programme.