Publications (15)
Context-Aware Generative Models for Prediction of Aircraft Ground Tracks
Nick Pepper, George De Ath, Marc Thomas +2
Trajectory prediction (TP) plays an important role in supporting the decision-making of Air Traffic Controllers (ATCOs). Traditional TP methods are deterministic and physics-based,…
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…
Probabilistic Simulation of Aircraft Descent via a Physics-Informed Machine Learning Approach
Amy Hodgkin, Nick Pepper, Marc Thomas
This paper presents a method for generating probabilistic descent trajectories in simulations of real-world airspace. A dataset of 116,066 trajectories harvested from Mode S radar…
SeAr PC: Sensitivity Enhanced Arbitrary Polynomial Chaos
Nick Pepper, Francesco Montomoli, Kyriakos Kantarakias
This paper presents a method for performing Uncertainty Quantification in high-dimensional uncertain spaces by combining arbitrary polynomial chaos with a recently proposed scheme…
Learning Generative Models for Climbing Aircraft from Radar Data
Nick Pepper, Marc Thomas
Accurate trajectory prediction (TP) for climbing aircraft is hampered by the presence of epistemic uncertainties concerning aircraft operation, which can lead to significant misspe…
Conditioning Aircraft Trajectory Prediction on Meteorological Data with a Physics-Informed Machine Learning Approach
Amy Hodgkin, Nick Pepper, Marc Thomas
Accurate aircraft trajectory prediction (TP) in air traffic management systems is confounded by a number of epistemic uncertainties, dominated by uncertain meteorological condition…
A Probabilistic Model for Aircraft in Climb using Monotonic Functional Gaussian Process Emulators
Nick Pepper, Marc Thomas, George De Ath +4
Ensuring vertical separation is a key means of maintaining safe separation between aircraft in congested airspace. Aircraft trajectories are modelled in the presence of significant…
AirTrafficGen: Configurable Air Traffic Scenario Generation with Large Language Models
Dewi Sid William Gould, George De Ath, Ben Carvell +1
The manual design of scenarios for Air Traffic Control (ATC) training is a demanding and time-consuming bottleneck that limits the diversity of simulations available to controllers…
Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
Edward Henderson, George De Ath, Nick Pepper
Effectively managing Air Traffic Control Officer (ATCO) workload is crucial in maintaining operational safety. Group supervisors use tools that estimate upcoming traffic load to ai…
Fast Surrogate Models for Adaptive Aircraft Trajectory Prediction in En route Airspace
Nick Pepper, Marc Thomas, Zack Xuereb Conti
Trajectory prediction (TP) is crucial for ensuring safety and efficiency in modern air traffic management systems. It is, for example, a core component of conflict detection and re…
Geometric Principles for Machine Learning of Dynamical Systems
Zack Xuereb Conti, David J Wagg, Nick Pepper
Mathematical descriptions of dynamical systems are deeply rooted in topological spaces defined by non-Euclidean geometry. This paper proposes leveraging structure-rich geometric sp…
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…
A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace
Adam Keane, Nick Pepper, Chris Burr +4
Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Blue…
Probabilistic Machine Learning to Improve Generalisation of Data-Driven Turbulence Modelling
Joel Ho, Nick Pepper, Tim Dodwell
A probabilistic machine learning model is introduced to augment the turbulence model in order to improve the modelling of separated flows and the generalisability of lea…
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…