A benchmarking framework for eye-tracking-based vigilance prediction of vessel traffic controllers

Vessel Traffic Controllers (VTCs) play a crucial role in ensuring safe navigation by maintaining a high level of vigilance. Eye-tracking has been identified as one of the most popular bio-signals for vigilance prediction. However, the existing studies on eye-tracking-based vigilance prediction usually utilized a limited set of features for analysis. A comprehensive analysis of various eye-tracking features and a unified model with general high performance remains a gap in current research. To address this issue, this study introduces a benchmarking framework for eye-tracking-based vigilance prediction and feature analysis. In the framework, a hierarchical analysis method is proposed, which explores a diverse set of eye-tracking features at both individual and group levels. Additionally, a vigilance ensemble model is proposed. Model interpretation is carried out by using the Shapley additive explanation (SHAP) method. The results highlight the superior performance of the proposed ensemble model. Moreover, a comparative analysis between professional VTCs and novices is performed. High-importance features and feature groups are identified separately. Upon comparison, it is also found that professionals demonstrate efficient attention allocation, while novices exhibit unique patterns influenced by their exploration strategies and scattered attention. The conclusions can serve as a valuable reference for maritime practitioners and provide insights into vigilance prediction across various domains.