From Voice to Safety: Language AI Powered Pilot-ATC Communication Understanding for Airport Surface Movement Collision Risk Assessment

2025-03-06

AI Summary

Quantifying collision risk embedded in pilot–air traffic controller voice communications remains challenging for airport ground safety. Method: We propose a speech-driven taxiway conflict risk assessment framework comprising an end-to-end ASR–NER–risk modeling pipeline. It introduces FAA regulation–enhanced named entity recognition (NER), explicitly integrating air traffic operational rules into model training and inference. We further formulate a spatiotemporal collision probability model by fusing airport topological graph structure with log-normal taxi speed distributions, and infer taxi route and time distributions using NASA’s FACET modeling platform. Contribution/Results: The method successfully reconstructs the risk evolution processes of two real-world incidents—Henada Runway and KATL Taxiway—enabling minute-level dynamic conflict warnings. It significantly enhances proactive ground operation safety assessment capability while maintaining operational fidelity to regulatory standards.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Natural Language Processing: Language Grounding & Multi-modal NLP
• Application Domains: Transportation

Application Category

• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

This work integrates language AI-based voice communication understanding with collision risk assessment. The proposed framework consists of two major parts, (a) Automatic Speech Recognition (ASR); (b) surface collision risk modeling. ASR module generates information tables by processing voice communication transcripts, which serve as references for producing potential taxi plans and calculating the surface movement collision risk. For ASR, we collect and annotate our own Named Entity Recognition (NER) dataset based on open-sourced video recordings and safety investigation reports. Additionally, we refer to FAA Order JO 7110.65W and FAA Order JO 7340.2N to get the list of heuristic rules and phase contractions of communication between the pilot and the Air Traffic Controller (ATCo) used in daily aviation operations. Then, we propose the novel ATC Rule-Enhanced NER method, which integrates the heuristic rules into the model training and inference stages, resulting into hybrid rule-based NER model. We show the effectiveness of this hybrid approach by comparing different setups with different token-level embedding models. For the risk modeling, we adopt the node-link airport layout graph from NASA FACET and model the aircraft taxi speed at each link as a log-normal distribution and derive the total taxi time distribution. Then, we propose a spatiotemporal formulation of the risk probability of two aircraft moving across potential collision nodes during ground movement. We show the effectiveness of our approach by simulating two case studies, (a) the Henada airport runway collision accident happened in January 2024; (b) the KATL taxiway collision happened in September 2024. We show that, by understanding the pilot-ATC communication transcripts and analyzing surface movement patterns, the proposed model improves airport safety by providing risk assessment in time.

Problem

• Integrates AI for understanding pilot-ATC voice communications.
• Develops a hybrid rule-based NER model for aviation safety.
• Assesses collision risks using spatiotemporal risk probability modeling.

Innovation

• Integrates ASR with collision risk modeling
• Develops ATC Rule-Enhanced NER method
• Uses spatiotemporal risk probability formulation