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

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 pred

SUBMITTER: Guo D 

PROVIDER: S-EPMC11543814 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature