A framework for assuring the accuracy and fidelity of an AI-enabled Digital Twin of en route UK airspace

2026-01-06

AIAA SCITECH 2026 Forum

AI Summary

This work addresses the lack of trustworthiness and fidelity assurance mechanisms in existing AI-driven digital twin systems for en-route airspace, which hinders their compliance with aviation regulatory requirements. Focusing on UK en-route airspace, the project introduces Trustworthy and Ethical Assurance (TEA) methods into air traffic management digital twins for the first time, establishing an integrated framework that combines AI/ML, digital twin technology, and structured assurance cases. By defining operational assurance objectives and constructing evidence chains, the study proposes a systematic, evaluable, and documentable approach to capability boundary analysis. This method effectively validates the digital twin’s accurate representation of physical airspace and its functional suitability, thereby providing a trustworthy training and testing environment for AI-based air traffic agents and offering a practical exemplar for regulatory compliance and industry guideline development.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Mobility, Driving & Flight
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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 counterpart and also provides sufficient functionality across target use cases. It provides a structured approach for researchers to assess, understand and document the strengths and limitations of the Digital Twin, whilst also identifying areas where fidelity could be improved. Furthermore, it serves as a foundation for engagement with stakeholders and regulators, supporting discussions around the regulatory needs for future applications, and contributing to the emerging guidance through a concrete, working example of a Digital Twin. The framework leverages a methodology known as Trustworthy and Ethical Assurance (TEA) to develop an assurance case. An assurance case is a nested set of structured arguments that provides justified evidence for how a top-level goal has been realised. In this paper we provide an overview of each structured argument and a number of deep dives which elaborate in more detail upon particular arguments, including the required evidence, assumptions and justifications.

Problem

• Digital Twin
• Artificial Intelligence
• Air Traffic Management
• Assurance Framework
• Regulatory Compliance

Innovation

• Digital Twin
• Assurance Framework
• Artificial Intelligence
• Air Traffic Management
• Trustworthy and Ethical Assurance