Analysis of Visual Cues During Landing Phase by Using Neural Network Modeling

Abstract

A neural network modeling approach has been developed to analyze the pilot's use of visual cues for landing a transport airplane. Time sequences of the visual cues and pilot control inputs obtained by using a flight simulator can be analyzed to quantitatively estimate the relationship between the visual cues and the pilot control inputs. In this paper, visual cues such as the horizon, runway shape, and runway marker are compared based on their importance. By using a flight simulator, neural network models are obtained in all cases wherein a pilot intentionally alters his or her attentiveness to the visual cues. The contribution ratios analysis reflects the attentiveness to each visual cue. The Monte Carlo landing simulation shows the difference in robustness of each obtained neural network model. It is confirmed that the timely choice of the appropriate visual cue is necessary for a smooth and safe landing.