ABSTRACT: In recent years, most maritime accidents have been caused by deficiencies in navigators’ situational awareness. Previous studies have evaluated the navigators' situation awareness (SA) through the application of the Situation Awareness Global Assessment Technique (SAGAT) in ship maneuvering simulations. Researchers have developed collision avoidance support systems and collision risk assessment models to mitigate maritime accidents. However, existing models often apply conventional weight parameters for collision risk factors, which may not be appropriate for navigators with different experience levels. To refine these weight parameter sets tailored to each navigator level, based on each navigator's significant SA, derived from experimental navigators' situation awareness measurement. In addition, grid search-based weight aggregation was employed to systematically refine the weight distributions, optimize the impact of collision risk factors, and ensure improved model accuracy. The results demonstrate that the proposed weight parameters improve the detection rate of significant targets according to navigator’s experience level in congested waters.

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