Abstract
Air traffic controllers (ATCs) are required to focus on flight information, make instant decisions and give instructions to pilots with high attention and responsibility. Human factors related to aviation risks should be monitored, such as fatigue, distraction, and so on. However, existing methods have two major problems: 1) Wearable or invasive devices may interfere with ATCs’ work; 2) Appropriate state indicator for ATCs is still not clear. Therefore, we propose a working state analysis solution, called ATC-WSA, and solve the above questions by 1) Computer vision and speech techniques without contact; 2) Specific models and indexes optimized by collected real ATCs’ data, including video, audio, annotation, and questionnaire. Three layers’ architecture is designed for AI detection, state analysis, and high-level indexes calculation. Overall, our demo can monitor and analyze the working state of ATCs and detect abnormal states in time. Key parts of this demo have already been applied to North China Air Traffic Control Center (Beijing) and the control tower of Beijing Capital International Airport.
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Liu, B., Wang, X., Dong, J., Li, D., Lu, F. (2022). ATC-WSA: Working State Analysis for Air Traffic Controllers. In: Fang, L., Povey, D., Zhai, G., Mei, T., Wang, R. (eds) Artificial Intelligence. CICAI 2022. Lecture Notes in Computer Science(), vol 13606. Springer, Cham. https://doi.org/10.1007/978-3-031-20503-3_42
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DOI: https://doi.org/10.1007/978-3-031-20503-3_42
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