Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control
Year: 2023
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Guo, Dongyue, Zhang, Zheng, Yang, Bo, Zhang, Jianwei, Yang, Hongyu, Lin, Yi
Institution: Sichuan University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4368754592