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AISA - AI Situational Awareness Foundation for Advancing Automation

Project duration: June 2020 - November 2022
Project website: https://aisa-project.eu/
Funded by: EU Horizon 2020, SESAR JU
Project number: 892618
  
Short description: 

This proposal addresses the topic “Digitalisation and Automation principles for ATM”. Automation is one of the mostpromising solutions for the capacity problem, however, to implement advanced automation concepts it is required thatthe AI and human are able to share the situational awareness. Exploring the effect of, and opportunities for, distributedhuman-machine situational awareness in en-route ATC operations is one of the main objectives of this project. Insteadof automating isolated individual tasks, such as conflict detection or coordination, we propose building a foundationfor automation by developing an intelligent situationally-aware system. Sharing the same team situational awarenessamong ATCO team members and AI will enable the automated system to reach the same conclusions as ATCOs whenconfronted with the same problem and to be able to explain the reasoning behind those conclusions. The challengesof transparency and generalization will be solved by combining machine learning with reasoning engine (includingdomain-specific knowledge graphs) in a way that emphasizes their advantages. Machine learning will be used forprediction, estimation and filtering at the level of individual probabilistic events, an area where it has so far showngreat prowess, whereas reasoning engine will be used to represent knowledge and draw conclusions based on allthe available data and explain the reasoning behind those conclusions. We will explore to what extent it is possibleto 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.


AISA Project - Brief intro video


Keywords:Human-Systems Integration, Automation, Artificial Situational Awareness, Team Situational Awareness, Reasoning, KnowledgeGraph, Machine Learning, Ontology, Air Traffic Control, Air Traffic Management

Project partners: 
Project team: 
Publications: 
B. Neumayr, C. Schütz, E. Gringinger, C. Fabianek, A. Vennesland, M. Schrefl, S. Wilson:
Providing Packages of Relevant ATM Information: An Ontology-based Approach
In: Journal of Air Transport Management, Vol 90, Jan. 2021, Elsevier Science Publ., Article 101937, ISSN: 0969-6997, DOI: https://doi.org/10.1016/j.jairtraman.2020.101937, 2021.
M. Bardach, E. Gringinger, M. Schrefl, C. Schütz:
Predicting Flight Delay Risk Using a Random Forest Classifier Based on Air Traffic Scenarios and Environmental Conditions
In: Proceedings of the 2020 AIAA/IEEE 39th Digital Avionics Systems Conference (DASC), October 11-15, 2020, San Antonio, Texas, U.S.A., IEEE Computer Society, DOI: 10.1109/DASC50938.2020.9256474, 2020.
M. Schrefl, B. Neumayr, S. Gruber, M. Hartmann, I. Tukaric, T. Radisic:
Creating an ATC knowledge graph in support of the artificial situational awareness system
In: Proceedings of the International Scientific Conference "The Science and Development of transport" (ZIRP 2022), September 28-30, 2022, Sibenik, Croatia, Published in journal: Transportation Research Procedia, Edited by Marjana Petrovic, Irina Dovbischuk and André Luiz Cunha, Volume 64, Elsevier Publ., doi: https://doi.org/10.1016/j.trpro.2022.09.037, pp. 328-336, 2022