Hidden Semi-Markov Models-Based Visual Perceptual State Recognition for Pilots

Pilots' loss of situational awareness is one of the human factors affecting aviation safety. Numerous studies have shown that pilot perception errors are one of the main reasons for a lack of situational awareness without a proper system to detect these errors. The main objective of this study is to examine the changes in pilots' eye movements during various flight tasks from the perspective of visual awareness. The pilot's gaze rule scanning strategy is mined through cSPADE, while a hidden semi-Markov model-based model is used to detect the pilot's visuoperceptual state, linking the correlation between the hidden state and time. The performance of the proposed algorithm is then compared with that of the hidden Markov model (HMM), and the more flexible hidden semi-Markov model (HSMM) is shown to have an accuracy of 93.55%.

Automated summary

The loss of situational awareness among pilots is a significant human factor that affects aviation safety. Without a proper system to detect pilot perception errors, studies have shown that pilot perception errors are one of the main reasons for the lack of situational awareness. This study examines the changes in pilots' eye movements during various flight tasks from the perspective of visual awareness. The proposed algorithm, based on a hidden semi-Markov model (HSMM), shows an accuracy of 93.55% in detecting the pilot's visuoperceptual state, outperforming the hidden Markov model (HMM) in flexibility.