Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control

Dongyue Guo, Zheng Zhang, Bo Yang, Jianwei Zhang, Hongyu Yang and Yi Lin ()
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Dongyue Guo: Sichuan University
Zheng Zhang: Sichuan University
Bo Yang: Sichuan University
Jianwei Zhang: Sichuan University
Hongyu Yang: Sichuan University
Yi Lin: Sichuan University

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract The booming air transportation industry inevitably burdens air traffic controllers’ workload, causing unexpected human factor-related incidents. Current air traffic control systems fail to consider spoken instructions for traffic prediction, bringing significant challenges in detecting human errors during real-time traffic operations. Here, we present an automation paradigm integrating controlling intent into the information processing loop through the spoken instruction-aware flight trajectory prediction framework. A 3-stage progressive multi-modal learning paradigm is proposed to address the modality gap between the trajectory and spoken instructions, as well as minimize the data requirements. Experiments on a real-world dataset show the proposed framework achieves flight trajectory prediction with high predictability and timeliness, obtaining over 20% relative reduction in mean deviation error. Moreover, the generalizability of the proposed framework is also confirmed by various model architectures. The proposed framework can formulate full-automated information processing in real-world air traffic applications, supporting human error detection and enhancing aviation safety.

Date: 2024
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DOI: 10.1038/s41467-024-54069-5

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