Solution Description

The AWARE project proposes a solution designed to enhance human-machine collaboration in the air traffic control environment. This solution focuses on establishing shared situational awareness and sets the groundwork for further ML-based automation in air traffic management.

At its core, the Artificial Situational Awareness system developed by AWARE gathers high-integrity information through aeronautical information exchange models. This information is used to construct a comprehensive knowledge graph that accurately depicts the traffic situation at either a sector or ACC level. The system goes beyond merely compiling factual knowledge; it also incorporates a knowledge-based system that manages and executes rule-based knowledge. This is achieved through a reasoning engine that operates on top of the factual data.

The Artificial Situational Awareness system significantly enhances safety in air traffic control. It acts as an additional safety net by reliably performing monotonous monitoring tasks. Moreover, it improves interoperability among various systems through enhanced data handling, a natural outcome of employing knowledge graphs for data management. Lastly, the system contributes to increasing sector capacity. This is achieved by automating certain monitoring tasks and facilitating the integration of other automation systems, thereby streamlining operations in air traffic control.

Integral to the system are multiple machine-learning modules. These modules are adept at assessing probabilistic events, such as trajectory prediction or conflict detection. By synergizing the reasoning engine with machine learning capabilities, the system can effectively analyze complex interactions between objects, draw insightful conclusions, elucidate the reasoning behind these conclusions, and anticipate future states of the system.