Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Chaebin Song, Junior Juin Park
Lead Presenter's Name
Chaebin Song
Lead Presenter's College
DB College of Aviation
Faculty Mentor Name
Dr. Chuyang Yang
Abstract
Recent runway safety events have highlighted the importance of accurate and timely interpretation of pilot–controller communications, especially in complex airport environments where aircraft and ground vehicles interact on or near active movement areas. In light of the recent LaGuardia Airport collision, which has drawn attention to communication, coordination, and surface safety challenges, this project investigates how multimodal data can support air traffic controllers’ situational awareness and safety assurance. This study develops an in-progress framework that combines Automatic Speech Recognition (ASR) of pilot–controller radio communications with ADS-B trajectory data to improve interpretation of operational intent and cross-check communications against observed aircraft movement. The approach uses aviation audio samples and trajectory data to identify communication events, infer operational states, and detect potential mismatches between spoken instructions and surface or flight behavior. By integrating semantic and spatial information, the framework is intended to provide a decision-support layer that helps highlight potentially unsafe conditions, strengthen controller awareness, and support post hoc safety analysis. Preliminary progress includes data preparation, communication labeling, and initial multimodal alignment design. The expected contribution is a practical aviation safety framework that can support future ATC tools for runway safety monitoring, controller assistance, and more resilient airport operations.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Included in
Aviation Safety and Security Commons, Human Factors Psychology Commons, Multi-Vehicle Systems and Air Traffic Control Commons
Multimodal Interpretation of Pilot–Controller Communications for Runway Safety Assurance and Enhanced ATC Situational Awareness.
Recent runway safety events have highlighted the importance of accurate and timely interpretation of pilot–controller communications, especially in complex airport environments where aircraft and ground vehicles interact on or near active movement areas. In light of the recent LaGuardia Airport collision, which has drawn attention to communication, coordination, and surface safety challenges, this project investigates how multimodal data can support air traffic controllers’ situational awareness and safety assurance. This study develops an in-progress framework that combines Automatic Speech Recognition (ASR) of pilot–controller radio communications with ADS-B trajectory data to improve interpretation of operational intent and cross-check communications against observed aircraft movement. The approach uses aviation audio samples and trajectory data to identify communication events, infer operational states, and detect potential mismatches between spoken instructions and surface or flight behavior. By integrating semantic and spatial information, the framework is intended to provide a decision-support layer that helps highlight potentially unsafe conditions, strengthen controller awareness, and support post hoc safety analysis. Preliminary progress includes data preparation, communication labeling, and initial multimodal alignment design. The expected contribution is a practical aviation safety framework that can support future ATC tools for runway safety monitoring, controller assistance, and more resilient airport operations.