Thesis Description & Objectives
Problem:
Traditional Air Traffic Control (ATC) systems can lead to high cognitive workload, especially with increasing air traffic density and the introduction of new air vehicles like drones. Current visualization methods may not fully support complex trajectory planning and real-time monitoring.
Objective:
Augmented Reality (AR) tactical table for ATC that enhances situational awareness, supports efficient trajectory planning and monitoring, and includes Early Warning Systems (EWS).
Approach:
Develop an AR-enabled tactical table visualizing the urban airspace, including all aircraft (traditional and Urban Air Mobility), their trajectories, and potential conflicts. Integrate semi-automatic processes to assist controllers, reducing cognitive workload while maintaining human oversight.
Multiple theses are available under this project:
– AI/ML backend only: Design and develop the EWS system based on ML and Foundational Models (based on Radio Communications, ATC Visualization systems (radar, GPS), etc.
– XR front-end/dashboard: Design and develop the XR visualization
Expected Impact:
Improved decision-making speed and accuracy; reduced cognitive workload for air traffic controllers; enhanced safety through advanced EWS; efficient management of complex and dense urban airspaces.
References:
https://ieeexplore.ieee.org/document/10764466, https://ieeexplore.ieee.org/document/9089606, https://dl.acm.org/doi/10.1145/3385378.3385380
