BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data

BiDGCNLLM: A Graph–Language Model for Drone State Forecasting and Separation in Urban Air Mobility Using Digital Twin-Augmented Remote ID Data

Accurate prediction of drone motion within structured urban air corridors is essential for ensuring safe and efficient operations in Urban Air Mobility (UAM) systems. Although real-world Remote Identification (Remote ID) regulations require drones to broadcast critical flight information such as vel...

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Bibliographic Details
Main Authors: Zhang Wen, Junjie Zhao, An Zhang, Wenhao Bi, Boyu Kuang, Yu Su, Ruixin Wang
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Drones
Subjects:
Urban Air Mobility
remote ID
air corridor
large language model
time series prediction
UAS traffic management
Online Access:https://www.mdpi.com/2504-446X/9/7/508
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