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2022 | OriginalPaper | Chapter

Videopanorama Frame Rate Requirements Derived from Visual Discrimination of Deceleration During Simulated Aircraft Landing

Authors : Norbert Fürstenau, Stephen R. Ellis

Published in: Virtual and Remote Control Tower

Publisher: Springer International Publishing

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Abstract

In order to determine the required visual frame rate (FR) for minimizing prediction errors with out-the-window video displays at remote/virtual airport towers, thirteen active air traffic controllers viewed high dynamic fidelity simulations of landing aircraft and decided whether aircraft would stop as if to be able to make a turnoff or whether a runway excursion would be expected. The viewing conditions and simulation dynamics replicated visual rates and environments of transport aircraft landing at small commercial airports. The required frame rate was estimated using Bayes inference on prediction errors by linear FR-extrapolation of event probabilities conditional on predictions (stop, no-stop). Furthermore estimates were obtained from exponential model fits to the parametric and non-parametric perceptual discriminabilities d′ and A (average area under ROC-curves) as dependent on FR. Decision errors are biased towards preference of overshoot and appear due to illusionary increase in speed at low frames rates. Both Bayes and A—extrapolations yield a framerate requirement of 35 < FRmin < 40 Hz. When comparing with published results (Claypool and Claypool Multimedia Systems 13:3–17, 2007) on shooter game scores the model based d′(FR)-extrapolation exhibits the best agreement and indicates even higher FRmin > 40 Hz for minimizing decision errors. Definitive recommendations require further experiments with FR > 30 Hz.

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Footnotes
1
Eye movements will not be discussed in this Chapter. For analysis of eye movements see chapter “Remotely-Operated AFIS in Japan” and references therein.
 
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Metadata
Title
Videopanorama Frame Rate Requirements Derived from Visual Discrimination of Deceleration During Simulated Aircraft Landing
Authors
Norbert Fürstenau
Stephen R. Ellis
Copyright Year
2022
DOI
https://doi.org/10.1007/978-3-030-93650-1_16

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