![]() ![]() 12 used the controllers’ eye movement indicators to evaluate how visual search entropy and participation can predict the performance of multi-target tracking air traffic control tasks. 11 explored the visual search and conflict mitigation strategies used by expert air traffic controllers through eye movement features. can effectively characterize the fatigue state of the controller. conducted an experiment to detect the fatigue state of controllers in real time 9, 10, and found that the controllers’ perclos, fixation point, saccade speed, pupil diameter and etc. Based on the advantages of non-invasiveness of eye movement, Jin et al. It is well known that eye movement indicators can be used to characterize the fatigue state of workers. In recent years, many researchers have carried out a lot of research on eye movement characteristics of controllers. Therefore, it is naturally to explore an innovative way to predict control forgetting behavior by analyzing the eye movement characteristics of the controller. Moreover, working status such as degree, fatigue, and cognitive load is important and effective to control radar surveillance, which is essentially a complicated human–computer interaction process 7. A large number of qualitative and quantitative studies on eye movement behavior data in ergonomics research have shown that operators’ focus can be assessed by eye movement characteristics such as the form of eye movement, blink frequency, saccade speed, and pupil diameter 6, 7, 8. Appropriate selection and processing of visual information is an indispensable cognitive function for the controller 5. Vision is the main way to monitor the status of aircrafts. Studies have shown that control forgetting is the most adverse factor, which is commonly observed all over the world. Thus, the controllers are more likely to make human errors threatening aviation safety in the process of control work 3, 4. The air traffic controller (hereinafter referred to as the controller), as the regulator of air traffic order, should undoubtedly endure the huge pressure from increasing flight tasks 2. In recent years, the number of civil aviation control flights has increased rapidly 1. This work tries to explore an innovative way to associate control forgetting with eye movement, so as to guarantee the safety of civil aviation. 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). The effectiveness of the method is verified by carrying out simulation experiments of eye movement during flight control. 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. 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). The exact relation of control forgetting with eye movement, however, still remains puzzling. Meanwhile, aviation safety is substantially influenced by the way of eye movement. 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. Control forgetting accounts for most of the current unsafe incidents. ![]()
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