Pilot Intent State Recognition Based on Eye-Movement Behavior Characteristics

This paper aims to uncover the generation patterns of pilot intentions during complex flight missions and to identify pilot intention states, thereby enabling airborne warning systems to understand and predict pilot intentions for early warning strategies. To achieve this, we designed a simulated flight experiment incorporating various risk scenarios to induce pilot intentions, collected eye-tracking data reflecting pilot intention states, and proposed a method for identifying the persistence of pilot intentions based on eye-tracking data. We then constructed a pilot intention dataset and analyzed the time–frequency characteristics of eye-tracking data in the intention persistence state, revealing key behavioral features following the generation of pilot intentions. Furthermore, we examined eye-tracking features that enhance the performance of pilot intention recognition models. Finally, we developed a deep learning model integrating recurrent neural networks (RNN) and bidirectional long short-term memory (BiLSTM) networks to recognize pilot intentions. The results demonstrate that the model achieved a recognition accuracy of 97.8% on the test set, and its performance in identifying pilot intention states was validated through comparison with a baseline model. This study confirms that eye-tracking data can effectively identify pilot intention states and offers new insights into aircraft safety early warning and intelligent control systems.