Descrizione del progetto
Esplorare i sistemi intelligenti di consapevolezza situazionale per le operazioni di controllo del traffico aereo
L’automazione offre una soluzione promettente ai problemi di capacità nella gestione del traffico aereo. Tuttavia, se si devono attuare concetti di automazione avanzata, gli esseri umani e i sistemi di IA devono condividere una consapevolezza situazionale. Pertanto, il progetto AISA, finanziato dall’UE, si propone di indagare gli effetti della consapevolezza situazionale uomo-macchina distribuita in operazioni di controllo del traffico aereo in rotta, nonché di esplorarne le opportunità. A tal fine, il progetto non si concentrerà sull’automazione di compiti individuali isolati, ma svilupperà un sistema intelligente consapevole della situazione. Questo sistema artificiale di consapevolezza situazionale aprirà la strada alla futura automazione avanzata basata sull’apprendimento automatico.
Obiettivo
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.
Campo scientifico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringcontrol systems
- social sciencessociologyindustrial relationsautomation
- natural sciencescomputer and information sciencesknowledge engineering
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
Parole chiave
Programma(i)
Meccanismo di finanziamento
RIA - Research and Innovation actionCoordinatore
10000 Zagreb
Croazia