Integrating spoken instructions into flight trajectory prediction to optimize automation in air traffic control
This addresses the problem of human error detection and automation in air traffic control, but it is incremental as it builds on existing prediction methods by adding a new modality.
The paper tackles the problem of air traffic controllers' workload and human errors by integrating spoken instructions into flight trajectory prediction, achieving over 20% relative reduction in mean deviation error.
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.