Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features
Eye movement features of pilots are critical for aircraft landing, especially in low-visibility and windy conditions. This study conducts simulated flight experiments concerning aircraft approach and landing under three low-visibility and windy conditions, including no-wind, crosswind, and tailwind....
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Aerospace |
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| Online Access: | https://www.mdpi.com/2226-4310/12/6/523 |
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| author | Xiuyi Li Yue Zhou Weiwei Zhao Chuanyun Fu Zhuocheng Huang Nianqian Li Haibo Xu |
| author_facet | Xiuyi Li Yue Zhou Weiwei Zhao Chuanyun Fu Zhuocheng Huang Nianqian Li Haibo Xu |
| author_sort | Xiuyi Li |
| collection | DOAJ |
| description | Eye movement features of pilots are critical for aircraft landing, especially in low-visibility and windy conditions. This study conducts simulated flight experiments concerning aircraft approach and landing under three low-visibility and windy conditions, including no-wind, crosswind, and tailwind. This research collects 30 participants’ eye movement data after descending from the instrument approach to the visual approach and measures the landing position deviation. Then, a random forest method is used to rank eye movement features and sequentially construct feature sets by feature importance. Two machine learning models (SVR and RF) and four deep learning models (GRU, LSTM, CNN-GRU, and CNN-LSTM) are trained with these feature sets to predict the landing position deviation. The results show that the cumulative fixation duration on the heading indicator, altimeter, air-speed indicator, and external scenery is vital for landing position deviation under no-wind conditions. The attention allocation required by approaches under crosswind and tailwind conditions is more complex. According to the MAE metric, CNN-LSTM has the best prediction performance and stability under no-wind conditions, while CNN-GRU is better for crosswind and tailwind cases. RF also performs well as per the RMSE metric, as it is suitable for predicting landing position errors of outliers. |
| format | Article |
| id | doaj-art-4be0e54900cf40ffa78d2062b05c3e7e |
| institution | OA Journals |
| issn | 2226-4310 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Aerospace |
| spelling | doaj-art-4be0e54900cf40ffa78d2062b05c3e7e2025-08-20T02:24:00ZengMDPI AGAerospace2226-43102025-06-0112652310.3390/aerospace12060523Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement FeaturesXiuyi Li0Yue Zhou1Weiwei Zhao2Chuanyun Fu3Zhuocheng Huang4Nianqian Li5Haibo Xu6CAAC Academy, Civil Aviation Flight University of China, Guanghan 618307, ChinaFlight Technology College, Civil Aviation Flight University of China, Guanghan 618307, ChinaFlight Technology College, Civil Aviation Flight University of China, Guanghan 618307, ChinaSchool of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150001, ChinaFlight Technology College, Civil Aviation Flight University of China, Guanghan 618307, ChinaFlight Technology College, Civil Aviation Flight University of China, Guanghan 618307, ChinaGuanghan Brand, Civil Aviation Flight University of China, Guanghan 618307, ChinaEye movement features of pilots are critical for aircraft landing, especially in low-visibility and windy conditions. This study conducts simulated flight experiments concerning aircraft approach and landing under three low-visibility and windy conditions, including no-wind, crosswind, and tailwind. This research collects 30 participants’ eye movement data after descending from the instrument approach to the visual approach and measures the landing position deviation. Then, a random forest method is used to rank eye movement features and sequentially construct feature sets by feature importance. Two machine learning models (SVR and RF) and four deep learning models (GRU, LSTM, CNN-GRU, and CNN-LSTM) are trained with these feature sets to predict the landing position deviation. The results show that the cumulative fixation duration on the heading indicator, altimeter, air-speed indicator, and external scenery is vital for landing position deviation under no-wind conditions. The attention allocation required by approaches under crosswind and tailwind conditions is more complex. According to the MAE metric, CNN-LSTM has the best prediction performance and stability under no-wind conditions, while CNN-GRU is better for crosswind and tailwind cases. RF also performs well as per the RMSE metric, as it is suitable for predicting landing position errors of outliers.https://www.mdpi.com/2226-4310/12/6/523eye movement featureslanding position deviationdeep learningCNN-LSTM modelrandom forest algorithm |
| spellingShingle | Xiuyi Li Yue Zhou Weiwei Zhao Chuanyun Fu Zhuocheng Huang Nianqian Li Haibo Xu Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features Aerospace eye movement features landing position deviation deep learning CNN-LSTM model random forest algorithm |
| title | Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features |
| title_full | Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features |
| title_fullStr | Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features |
| title_full_unstemmed | Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features |
| title_short | Predicting Landing Position Deviation in Low-Visibility and Windy Environment Using Pilots’ Eye Movement Features |
| title_sort | predicting landing position deviation in low visibility and windy environment using pilots eye movement features |
| topic | eye movement features landing position deviation deep learning CNN-LSTM model random forest algorithm |
| url | https://www.mdpi.com/2226-4310/12/6/523 |
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