ATC-DA Feature A EXE 001 Real-Time Simulation – SESAR JARVIS Project

On 16th January, the Real-Time Simulation for the Italian exercise of the SESAR JARVIS project was successfully completed at ENAV’s National Test Facility. The activity was carried out in close collaboration with CIRA, Leonardo, and NAIS, with the essential contribution of ENAV’s operational personnel: 6 ATCOs actively participated in the test sessions and provided highly valuable feedback.

The simulations assessed the Digital Assistant Conflict Resolution Advisor CORA (ATC-DA-CORA) as a decision-support tool for air traffic controllers in conflict detection and resolution processes. As part of the project, CORA has been enhanced through Artificial Intelligence (AI) techniques and integrated with the Tactical Conflict Detection Tool (TCT) within the 4-Flight CWP platform.

The RTS preparation and execution have involved about 30 resources, between engineers, HP experts and pilots. The ML training needed about 6 months of real air traffic management historical data and 2 months of reinforcement process to select the most effective conflict resolutions. The simulation execution regarded 6 en-route/TMA sectors within Roma ACC area and performed about 500 minutes of simulated traffic with 12 exercise runs in 1 week.

The partners contributed as follows:

ENAV - Responsible for the task and for organizing the validation activities, provides the historical traffic data, the conflict resolution techniques and the ML algorithm training.

CIRA - Designs the learning strategies and implements the training of the ATC-DA-CORA AI algorithms, supports AI assurance activities with EASA, and contributes to the development of interfaces for integration into the target platform.

Leonardo (LDO) - Develops the CORA prototype, integrates CORA with AI engine and 4-Flight simulation platform, and performs industrial-environment verification; supports validation activities.

NAIS - Performs the Cost-Benefit Analysis (CBA), safety assessment, human performance evaluation, and environmental impact assessment, with support from all partners.

This exercise marks the completion of the project’s validation cycle, achieving Technology Readiness Level (TRL) 4 and providing further evidence of the potential of Artificial Intelligence as an enabler for the evolution of Air Traffic Management (ATM).