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, Amy Hodgkin, Dewi Gould, John Korna, Marc Thomas

TL;DR
This paper presents an assurance framework for verifying the accuracy and reliability of a probabilistic Digital Twin of UK airspace, supporting AI-based air traffic control testing and regulatory compliance.
Contribution
It introduces a structured assurance framework based on Trustworthy and Ethical Assurance methodology for Digital Twins in air traffic management.
Findings
Developed a probabilistic Digital Twin for UK en route airspace
Created an assurance case demonstrating Digital Twin fidelity
Provided guidance for regulatory engagement and improvement areas
Abstract
Digital Twins combine simulation, operational data and Artificial Intelligence (AI), and have the potential to bring significant benefits across the aviation industry. Project Bluebird, an industry-academic collaboration, has developed a probabilistic Digital Twin of en route UK airspace as an environment for training and testing AI Air Traffic Control (ATC) agents. There is a developing regulatory landscape for this kind of novel technology. Regulatory requirements are expected to be application specific, and may need to be tailored to each specific use case. We draw on emerging guidance for both Digital Twin development and the use of Artificial Intelligence/Machine Learning (AI/ML) in Air Traffic Management (ATM) to present an assurance framework. This framework defines actionable goals and the evidence required to demonstrate that a Digital Twin accurately represents its physical…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Ethics and Social Impacts of AI · Air Traffic Management and Optimization
