Abstract
The objective of this paper is to present a new adaptive automation concept which offers an innovative ‘team’ centred approach to solving human factors/workload management problems. The A-PiMod concept/approach is defined by the concept of partnership – specifically, the “Third Pilot” and the crew and automation are in charge together. We are proposing partnership as opposed to dynamic changes in control function where changes can be controlled autonomously by the system. In support of this, a new multimodal concept is proposed which supports improved assessment of crew state/workload (i.e. information inputs re crew activity/interactions provides a means to communicate with the crew in relation to crew state and decision support, and allows for flexible crew/cockpit interaction).
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Acknowledgments
The research leading to these results/preliminary outcomes has received funding from the European Commission’s Seventh Framework Programme (FP7/2007-2013) under grant agreement N. 605141 - Applying Pilot Models for Safety Aircraft (A-PiMod) Project. We would like to thank member of the A-PiMod Project Team and our COP members – particularly, Paul Cullen, William Butler, Martin Duffy and Stephen Duffy.
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Appendices
Appendices
1.1 Systems to Monitor Observed Behavior
Eye Tracking.
In both sets of simulator evaluation sessions, SMI Eye Tracking Glasses 2 were used to measure the gaze positions. Please note that his was demonstrated – but not implemented in real time. SMI Eye Tracking Glasses is a binocular tracking device which operates with 60 Hz. It is connected with a Laptop via USB on which videos of the eyes and the scene camera are recorded and the gaze position is calculated. The system is combined with an A.R.T Optical Head Tracking system. Retro reflective targets are attached to the glasses which are recorded by infrared cameras installed in the simulator. This system allows us to calculate the head position and orientation. With both systems combined, it is possible to calculate a 3D gaze vector for each eye.
Gesture Recognition.
The Gesture recognition is meant to recognize the upper body parts of a human operator – aeronautic pilot – in order to detect “implicit gestures”. Implicit gestures refer to movements of the upper body parts, which are normal actions taken by the crew (e.g. controlling different parts of the cockpit, or interaction among the crew).
Head Pose Recognition.
The head pose estimation functionality serves to provide information about where the pilot is looking within the cockpit (which instruments, screens, control elements, etc.). The technology is designed to be completely passive and non-intrusive in the sense that the pilot does not wear (or otherwise consciously interact with) any additional pieces of equipment, such as eye-tracking glasses. Also, the head pose estimation device does not emit any infrared light, which would be the case for contemporary remote eye trackers or depth cameras (based on structured light projection or infrared time-of-flight sensors). In A-PiMod, the first application of the said technology in the cockpit is detecting “missed events” – when the pilot is provided with a piece of information by the cockpit, but she misses it by not looking at the appropriate display for a time period. The cockpit display (MC-M Display) provides a notification of the missed event. Depending on the Pilots response, the saliency of such message is increased. The second application is contribution to the estimation of the pilot’s state of mind and workload level from the patterns of the head motion.
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Cahill, J. et al. (2017). Adaptive Automation and the Third Pilot: Managing Teamwork and Workload in an Airline Cockpit. In: Longo, L., Leva, M. (eds) Human Mental Workload: Models and Applications. H-WORKLOAD 2017. Communications in Computer and Information Science, vol 726. Springer, Cham. https://doi.org/10.1007/978-3-319-61061-0_10
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DOI: https://doi.org/10.1007/978-3-319-61061-0_10
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