Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area

Airspace will becoming increasingly crowded and complex, human Air Traffic Control Officers (ATCOs) need to transit from the role of an operator to that of a supervisor of automation. Current Artificial Intelligence in Air Traffic Management only encompasses providing information and advisories, but...

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Main Author: Leon, Jia En
Other Authors: Lye Sun Woh
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/158817
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1588172022-06-07T03:44:20Z Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area Leon, Jia En Lye Sun Woh School of Mechanical and Aerospace Engineering Air Traffic Management Research Institute MSWLYE@ntu.edu.sg Engineering::Mechanical engineering Airspace will becoming increasingly crowded and complex, human Air Traffic Control Officers (ATCOs) need to transit from the role of an operator to that of a supervisor of automation. Current Artificial Intelligence in Air Traffic Management only encompasses providing information and advisories, but not executing solutions automatically. Hence, there is to understand the strategies used by the human ATCOs to provide a template for the Artificial Intelligence to smoothen the process of automation acceptance. This paper will examine the maneuver patterns used by human ATCOs in resolving waypoint conflicts in two cases, the converging conflict and overtaking conflict, and in sequencing conflicting for landing. The focus of this paper will be on the Terminal Maneuvering Area control sector of the Air Traffic Management framework. Adoption rates of maneuver patterns identified will also be computed and compared to uncover preferred methods of resolution maneuvers. The identified maneuver patterns will also be validated with eye tracking data to uncover unique signatures to the maneuver patterns for recommendations for the suitability of integration into the automation system. The performances of each experiment will be studied and correlated to the number of commands executed or number of fixations. An understanding of maneuver patterns used to resolve conflicts in the Terminal Maneuvering Area will help drive future automation acceptance to alleviate the human ATCOs’ workload. However, the overall results have concluded that the maneuver patterns and eye tracking signatures is not suited to for integration with an automation framework. Bachelor of Engineering (Mechanical Engineering) 2022-06-07T03:44:20Z 2022-06-07T03:44:20Z 2022 Final Year Project (FYP) Leon, J. E. (2022). Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158817 https://hdl.handle.net/10356/158817 en B123 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
spellingShingle Engineering::Mechanical engineering
Leon, Jia En
Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area
description Airspace will becoming increasingly crowded and complex, human Air Traffic Control Officers (ATCOs) need to transit from the role of an operator to that of a supervisor of automation. Current Artificial Intelligence in Air Traffic Management only encompasses providing information and advisories, but not executing solutions automatically. Hence, there is to understand the strategies used by the human ATCOs to provide a template for the Artificial Intelligence to smoothen the process of automation acceptance. This paper will examine the maneuver patterns used by human ATCOs in resolving waypoint conflicts in two cases, the converging conflict and overtaking conflict, and in sequencing conflicting for landing. The focus of this paper will be on the Terminal Maneuvering Area control sector of the Air Traffic Management framework. Adoption rates of maneuver patterns identified will also be computed and compared to uncover preferred methods of resolution maneuvers. The identified maneuver patterns will also be validated with eye tracking data to uncover unique signatures to the maneuver patterns for recommendations for the suitability of integration into the automation system. The performances of each experiment will be studied and correlated to the number of commands executed or number of fixations. An understanding of maneuver patterns used to resolve conflicts in the Terminal Maneuvering Area will help drive future automation acceptance to alleviate the human ATCOs’ workload. However, the overall results have concluded that the maneuver patterns and eye tracking signatures is not suited to for integration with an automation framework.
author2 Lye Sun Woh
author_facet Lye Sun Woh
Leon, Jia En
format Final Year Project
author Leon, Jia En
author_sort Leon, Jia En
title Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area
title_short Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area
title_full Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area
title_fullStr Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area
title_full_unstemmed Unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area
title_sort unearthing strategies from visio-physiological data of traffic conflict situations in terminal maneuvering area
publisher Nanyang Technological University
publishDate 2022
url https://hdl.handle.net/10356/158817
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