Periodic Reporting for period 2 - AISA (AI Situational Awareness Foundation for Advancing Automation)
Période du rapport: 2021-06-01 au 2022-11-30
Exploring the effect of, and opportunities for, distributed human-machine situational awareness in en-route ATC operations was one of the main objectives of this project. Instead of automating isolated individual tasks, such as conflict detection or coordination, we proposed building a foundation for automation by developing an intelligent situationally-aware system.
The machine situational awareness achieved by the AISA system was shown to have sufficient accuracy for a proof-of-concept system. The system provided ATCOs with additional support since it enhanced their situational awareness which allowed them to preserve it regardless of the traffic situation. Most of the planned monitoring tasks for the AISA system have been developed and were fully functional. This project also brought together the unification of existing aeronautical exchange models and additionally identified knowledge to represent concepts relevant in en-route Air Traffic Control. This methodology and the accompanying tool are general and applicable to anyone interested in utilizing the same approach.
Task 2.1 and D2.1 developed a Concept of Operations (ConOps) for en-route air traffic control (ATC) performed by a human-machine team with shared situational awareness (SA). Task 2.2 analyzed requirements for automation of monitoring tasks via AI SA, showing which tasks can be automated and what requirements are needed. Task 3.1 developed a 4D trajectory prediction module using a neural network and a two-step approach. Task 3.2 developed a Conflict Detection module, which performed well in identifying errors but needed further research. Task 3.3 developed a complexity assessment model to determine air traffic complexity. Task 4.1-4.4 focused on creating a knowledge graph system in Java, connecting it with Prolog, and encoding facts and rules about ATC operations. Task 5.1 evaluated SA among AI and ATCOs through various measurement tools in Experiment 1 and Experiment 2. Task 5.2 conducted a risk assessment of the AI SA system and proposed measures to ensure safety. Task 5.3 evaluated the impact of AI system on human performance in distributed SA, showing high accuracy for most tasks, particularly for conflict detection.
The project has successfully developed a Proof-of-Concept (PoC) knowledge-based system for automating en-route air traffic control (ATC) tasks. Most of the ATCO tasks chosen in the project's ConOps were successfully automated, tested, and applied to traffic data (46 out of 57 tasks), with a focus on monitoring tasks but also including some tasks that involve prediction and decision-making. This has laid the foundation for further automation within the human-machine ATCO team.
The accuracy of the PoC system was deemed very high according to an analysis of its performance, and it was concluded that there is great potential for real-time operation considering it takes about 5 seconds to process a single traffic situation graph, which is similar to the refresh rate of an ATCO's working position. Machine learning (ML) modules were partially integrated into the system, with the conflict detection module showing an accuracy of 70%.
Different assessments of situation awareness (SA) were completed, including assessments of human SA, machine (or artificial) SA, and team SA. The results of these assessments showed that ATCOs are more critical in judging their own SA when they are aware of machine SA, and that inputs from the AI SA system might be beneficial for ATCOs in terms of SA with some minor adjustments, such as providing warnings for important time-critical aspects and providing other information in a more passive manner. Machine SA was assessed through the system's properties and functions and through comparison with human SA, and the results indicated that the ML modules can predict the future state of traffic.
Overall, the PoC system has shown promising results in terms of automating ATC tasks and improving SA within the ATCO team.
Summarized project results are as follows:
• Development of principles for the AI Situational Awareness System
• Development of human-machine SA concept
• Development of modules for translation of selected aeronautical data into knowledge graph
• Creation of components of the ATC-specific knowledge graph
• Development of reasoning engine
• Concept assessment.
The potential and expected impact of the project is the development of principles that could enable a higher level of automation with direct positive effects on safety and capacity. Indirectly will this approach have a positive impact on all performance areas and by improving them, positive impacts can be expected in the society due to reduced costs of air traffic.