To maintain situation awareness (SA) when exposed to emergencies during pilotage, a pilot needs to selectively allocate attentional resources to perceive critical status information about ships and environments. Although it is important to continuously monitor a pilot’s SA, its relationship with attention is still not fully understood in ship pilotage. This study performs bridge simulation experiments that include vessel departure, navigation in the fairway, encounters, poor visibility, and anchoring scenes with 13 pilots (mean = 11.3 and standard deviation = 1.4 of experience). Individuals were divided into two SA group levels based on the Situation Awareness Rating Technology (SART-2) score (mean = 20.13 and standard deviation = 5.83) after the experiments. The visual patterns using different SA groups were examined using heat maps and scan paths based on pilots’ fixations and saccade data. The preliminary visual analyses of the heat maps and scan paths indicate that the pilots’ attentional distribution is modulated by the SA level. That is, the most concerning areas of interest (AOIs) for pilots in the high and low SA groups are outside the window (AOI-2) and electronic charts (AOI-1), respectively. Subsequently, permutation simulations were utilized to identify statistical differences between the pilots’ eye-tracking metrics and SA. The results of the statistical analyses show that the fixation and saccade metrics are affected by the SA level in different AOIs across the five scenes, which confirms the findings of previous studies. In encounter scenes, the pilots’ SA level is correlated with the fixation and saccade metrics: fixation count ( p  = 0.034 < 0.05 in AOI-1 and p  = 0.032 < 0.05 in AOI-2), fixation duration ( p  = 0.043 < 0.05 in AOI-1 and p  = 0.014 < 0.05 in AOI-2), and saccade count ( p  = 0.086 < 0.1 in AOI-1 and p  = 0.054 < 0.1 in AOI-2). This was determined by the fixation count ( p  = 0.024 < 0.05 in AOI-1 and p  = 0.034 < 0.05 in AOI-2), fixation duration ( p  = 0.036 < 0.05 in AOI-1 and p  = 0.047 < 0.05 in AOI-2), and saccade duration ( p  = 0.05 ≤ 0.05 in AOI-1 and p  = 0.042 < 0.05 in AOI-2) in poor-visibility scenes. In the remaining scenes, the SA could not be measured using eye movements alone. This study lays a foundation for the cognitive mechanism recognition of pilots based on SA via eye-tracking technology, which provides a reference to establish cognitive competency standards in preliminary pilot screenings.

Cognitive competency is an essential complement to the existing ship pilot screening system that should be focused on. Situation awareness (SA), as the cognitive foundation of unsafe behaviors, is susceptible to influencing piloting performance. To address this issue, this paper develops an identification model based on random forest- convolutional neural network (RF-CNN) method for detecting at-risk cognitive competency (i.e., low SA level) using wearable EEG signal acquisition technology. In the poor visibility scene, the pilots’ SA levels were correlated with EEG frequency metrics in frontal (F) and central (C) regions, including α/β (p = 0.071 &lt; 0.1 in F and p = 0.042 &lt; 0.05 in C), θ/(α + θ) (p = 0.048 &lt; 0.05 in F and p = 0.026 &lt; 0.05 in C) and (α + θ)/β (p = 0.046 &lt; 0.05 in F and p = 0.012 &lt; 0.05 in C), and then a total of 12 correlation features were obtained based on a 5 s sliding time window. Using the RF algorithm developed by principal component analysis (PCA) for further feature combination, these salient combinations are used as input sets to obtain the CNN algorithm with optimal parameters for identification. The comparative results of the proposed RF-CNN (accuracy is 84.8%) against individual RF (accuracy is 78.1%) and CNN (accuracy is 81.6%) methods demonstrate that the RF-CNN with feature optimization provides the best identification of at-risk cognitive competency (accuracy increases 6.7%). Overall, the results of this paper provide key technical support for the development of an adaptive evaluation system of pilots’ cognitive competency based on intelligent technology, and lay the foundation and framework for monitoring the cognitive process and competency of ship piloting operation in China.

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With the rapid development of economy, our country's marine transportation passageways get more crowded. Especially in the region of Taiwan Strait, crude oil import in East Asian districts as well as the transportation between lines in the North and South makes traffic busier than ever before. Besides, since the permission of vessels correspondence between Taiwan and China accompanied by greatly increasing encounter probability of vessels, the navigation safety in Taiwan Strait appears extremely essential. This article aims to provide security guidance covering risk aversion for vessels sailing in the Taiwan Strait by integrating information such as stream, physiognomy and meteorology of straits