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Multi-view representation learning for performance-based tactical conflict resolution in urban air mobility

Published online by Cambridge University Press:  30 June 2025

Cheng Huang Open the ORCID record for Cheng Huang [Opens in a new window] ,
Ivan Petrunin Open the ORCID record for Ivan Petrunin [Opens in a new window]  and
Antonios Tsourdos Open the ORCID record for Antonios Tsourdos [Opens in a new window]
Cheng Huang*
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, UK
Ivan Petrunin
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, UK
Antonios Tsourdos
Affiliation:
School of Aerospace, Transport and Manufacturing, Cranfield University, Bedfordshire, UK
*
Corresponding author: Cheng Huang; Email: cheng-huang.huang@outlook.com
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Abstract

Ensuring safety and security in urban air mobility is of utmost significance. As air traffic becomes more concentrated in urban regions, instances of flight conflicts are on the rise. The complex urban morphology, micro-environmental factors and various flight risks significantly impact flight safety. This paper introduces a comprehensive framework to address these challenges. The framework incorporates random 3D city layouts, encompassing city buildings and terrains to establish a corridor graph structure. Leveraging this graph, a multi-view representation learning approach is proposed, which employs graph neural networks, recurrent neural networks (RNN) and contrastive learning to effectively manage tactical conflicts. Through rigorous testing across diverse scenarios, including the incorporation of uncertainties such as wind turbulence, the model’s performance is extensively evaluated. The conclusive results underscore the robustness and efficacy of the proposed approach in ensuring safety and resolving conflicts within the dynamic urban air mobility landscape.

Keywords

tactical conflict managementgraph neural networksmulti-view representation

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Type
Research Article
Information
The Aeronautical Journal , First View , pp. 1 - 23
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Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of Royal Aeronautical Society

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