Crew coordination in the context of aviation is a specifically choreographed set of tasks performed by each pilot, defined for each phase of flight. Based on the constructs of effective Crew Resource Management and SOPs for each phase of flight, a shared understanding of crew workload and task responsibility is considered representative of well-coordinated crews. Nominal behavior is therefore defined by SOPs and CRM theory, detectable through pilot eye-scan. This research investigates the relationship between the eye-scan exhibited by each pilot and the level of coordination between crewmembers.
Crew coordination was evaluated based on each pilot's understanding of the other crewmember's workload. By contrasting each pilot's workload-understanding, crew coordination was measured as the summed absolute difference of each pilot's understanding of the other crewmember's reported workload, resulting in a crew coordination index. The crew coordination index rates crew coordination on a scale ranging across Excellent, Good, Fair and Poor.
Eye-scan behavior metrics were found to reliably identify a reduction in crew coordination. Additionally, crew coordination was successfully characterized by eye-scan behavior data using machine learning classification methods. Identifying eye-scan behaviors on the flight deck indicative of reduced crew coordination can be used to inform training programs and design enhanced avionics that improve the overall coordination between the crewmembers and the flight deck interface. Additionally, characterization of crew coordination can be used to develop methods to increase shared situation awareness and crew coordination to reduce operational and flight technical errors. Ultimately, the ability to reduce operational and flight technical errors made by pilot crews improves the safety of aviation.
Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-5352 |
Date | 01 July 2014 |
Creators | Ellis, Kyle Kent Edward |
Contributors | Schnell, Thomas |
Publisher | University of Iowa |
Source Sets | University of Iowa |
Language | English |
Detected Language | English |
Type | dissertation |
Format | application/pdf |
Source | Theses and Dissertations |
Rights | Copyright 2014 Kyle Kent Edward Ellis |
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