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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 with eye movement, however, still remains puzzling. Motivated by this, a control forgetting prediction method is proposed based on the combination of 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 the prediction accuracy of this method is up to 79.2%, which is substantially higher than that of Binary Logistic Regression, CNN and LSTM (71.3%, 74.6%, and 75.1% respectively). This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation.

To explore how pilots’ distribution of visual attention affects flight performance, twenty male pilots (novices and experts with 407 ± 11.3 h and 4127 ± 77 h of flight experience, respectively) were enlisted to complete the instrument holding pattern and approach procedure on the DA-42 simulator. The distribution of visual attention was based on eye movement data recorded during the flight to investigate how pilots scan the flight instrument panel, which was divided into six areas of interest (AOIs). To evaluate the pilots’ flight performance an expert scoring method was used. During the outbound-leg stage, experts paid significantly more visual attention to the airspeed indicator, altimeter and reference system, whereas for the approach phase, they devoted more attention to the airspeed indicator, altimeter and vertical speed indicator. Results showed that experts’ proportions of gaze duration on different AOIs contributed to their better performance. An effective visual attention model can be developed on this study to improve air traffic safety.