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

• URL: http://arxiv.org/abs/2305.01661v2
• Date: Fri, 18 Oct 2024 07:15:51 GMT
• Title: Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control
• Authors: Dongyue Guo, Zheng Zhang, Bo Yang, Jianwei Zhang, Hongyu Yang, Yi Lin,
• Abstract summary: Current air traffic control systems fail to consider spoken instructions for traffic prediction. We present an automation paradigm integrating controlling intent into the information processing loop. A 3-stage progressive multi-modal learning paradigm is proposed to address the modality gap between the trajectory and spoken instructions.
• Score: 20.718663626382995
• License:
• 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.

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