Air traffic control forgetting prediction based on eye movement information and hybrid neural network

Abstract

Control forgetting accounts for most of the current unsafe incidents. In the research field of radar surveillance control, how to avoid control forgetting to ensure the safety of flights is becoming a hot issue which attracts more and more attention. Meanwhile, aviation safety is substantially influenced by the way of eye movement. The exact relation of control forgetting to eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on Convolutional Neural Networks and Long-Short Term Memory (CNN-LSTM). In this model, the eye movement characteristics are classified in terms of whether they are time-related, and then regulatory forgetting can be predicted by virtue of CNN-LSTM. The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. Results show that this method, using eye movement data, can provide control forgetting prediction with remarkably high accuracy. This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.