Monika Mittendorf

Frame Rate Effects on Visual Discrimination of Landing Aircraft Deceleration Implications for Virtual Tower Design and Speed Perception

In order to help determine the required visual frame rate for the design of remote/virtual airport towers, thirteen active air traffic controllers viewed high dynamic-fidelity simulations of landing aircraft and decided whether the aircraft would stop before the end of the runway, as if to be able to make a runway turnoff. The viewing conditions and simulation dynamics replicated visual rates and environments of transport aircraft landing at small commercial airports. Three frame rates were used: 6, 12, and 24 fps. The frame rate that would be needed to produce asymptotic performance was estimated from a model fit to perceptual discriminability (d’) of the condition in which the aircraft would stop. The required frame rate appears to range from 30-60 fps, but definitive recommendations require further testing at a higher rate in the range of 45-60 fps. Errors and reports of judgment certainty show performance was roughly steady state. Anecdotal reports of increased apparent speed due to low frame rates are objectively confirmed. Some implications for the perceptual design of a remote tower are briefly discussed.

Discriminability of flight maneuvers and risk of false decisions derived from dual choice decision errors in a videopanorama-based remote tower work position

Future remote control of small low traffic airports (Remote Tower Operation, RTO) will rely on the replacement of the conventional control tower out-of-windows view by a panoramic digital reconstruction with high resolution and pan-tilt zoom (PTZ) video cameras as basic sensor system. This provides the required visual cues for aerodrome traffic control without a local control tower. Here we show that with a 2 arcmin-per-pixel resolution panorama system even with the use of a manually controlled (analog) PTZ camera (with PAL TV-resolution and selectable zoom factor setting) experiments under operational conditions indicate a significant increase of decision errors under RTO as compared to the conventional out-of-windows view. We quantify the corresponding discrimination difference by means of detection theory (discriminability, decision criteria) and Bayes inference (risk of false decisions) using the response errors of tower controllers with regard to dual choice decision tasks. The results extend the performance and subjective data analysis of safety related maneuvers in 11.

Bernoulli-Langevin wind Speed model for Simulation of storm events

Abstract We present a simple nonlinear dynamics Langevin model for predicting the instationary wind speed profile during storm events typically accompanying extreme low-pressure situations. It is based on a second-degree Bernoulli equation with δ-correlated Gaussian noise and may complement stationary stochastic wind models. Transition between increasing and decreasing wind speed and (quasi) stationary normal wind and storm states are induced by the sign change of the controlling time-dependent rate parameter k(t). This approach corresponds to the simplified nonlinear laser dynamics for the incoherent to coherent transition of light emission that can be understood by a phase transition analogy within equilibrium thermodynamics [H. Haken, Synergetics, 3rd ed., Springer, Berlin, Heidelberg, New York 1983/2004.]. Evidence for the nonlinear dynamics two-state approach is generated by fitting of two historical wind speed profiles (low-pressure situations “Xaver” and “Christian”, 2013) taken from Meteorological Terminal Air Report weather data, with a logistic approximation (i.e. constant rate coefficients k) to the solution of our dynamical model using a sum of sigmoid functions. The analytical solution of our dynamical two-state Bernoulli equation as obtained with a sinusoidal rate ansatz k(t) of period T (=storm duration) exhibits reasonable agreement with the logistic fit to the empirical data. Noise parameter estimates of speed fluctuations are derived from empirical fit residuals and by means of a stationary solution of the corresponding Fokker-Planck equation. Numerical simulations with the Bernoulli-Langevin equation demonstrate the potential for stochastic wind speed profile modeling and predictive filtering under extreme storm events that is suggested for applications in anticipative air traffic management.