5 papers
Human-in-the-Loop Testing of AI Agents for Air Traffic Control with a Regulated Assessment Framework
Ben Carvell, Marc Thomas, Andrew Pace +7
We present a rigorous, human-in-the-loop evaluation framework for assessing the performance of AI agents on the task of Air Traffic Control, grounded in a regulator-certified simul…
Online Action-Stacking Improves Reinforcement Learning Performance for Air Traffic Control
Ben Carvell, George De Ath, Eseoghene Benjamin +1
We introduce online action-stacking, an inference-time wrapper for reinforcement learning policies that produces realistic air traffic control commands while allowing training on a…
A Future Capabilities Agent for Tactical Air Traffic Control
Paul Kent, George De Ath, Martin Layton +3
Escalating air traffic demand is driving the adoption of automation to support air traffic controllers, but existing approaches face a trade-off between safety assurance and interp…
A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control
Nick Pepper, Adam Keane, Amy Hodgkin +11
This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the…
Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
Edward Henderson, Dewi Gould, Richard Everson +2
Real-time assessment of near-term Air Traffic Controller (ATCO) task demand is a critical challenge in an increasingly crowded airspace, as existing complexity metrics often fail t…