Optimization of Pilot Fatigue Evaluation Method for Tunnel Sections Based on Attention Mechanism

To investigate pilot fatigue during tunnel navigation, we developed a simulated experimental platform based on a large-scale ship maneuvering simulator (LSSM). EEG data were collected from 12 pilots while they navigated the tunnel using a saline-electrode wireless portable EEG device (EMOTIV FLEX). We processed the pilot’s EEG signal data using the toolbox, which was EEGLAB (via MATLAB), to extract the wave characteristics that reflect the fatigue/multi-branch longitudinal short memory (LSTM) performance (i.e., Alpha to Delta waves). We developed a multi-branch LSTM model on the foundation of an optimized attention mechanism. The conventional machine-learning models showed limited performance, with random forest achieving an accuracy of 82.75% and an AUC of 0.75, and SVM achieving an accuracy of 75.00% and an AUC of 0.70. The experiment findings revealed that using EEG temporal features in conjunction with the task performance of pilots in the LSTM model delivered an accuracy of 91.67% and an AUC of 0.95, and precision/recall levels above 89. The study offers an efficient and accurate technical solution to check the fatigue of pilots in tunnel navigation and will substantially contribute to the safety of the cargo shipments with the help of the inland waterways.