# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
###### Abstract  
This study constructs a support vector machine model based on supervised learning to model the results of situation awareness for ship collision avoidance. To explain the model, collision risk situations were defined, and human situation recognition results were collected in the specified cases. Moreover, it was used to build predictors and outcome variables. Finally, the constructed variable was applied to the classification model. This model provides insight into the results of the navigator's encounter situation awareness when collision avoidance is required. The results indicate that the proposed model can be used to predict human situation awareness outcomes in given cases. collision avoidance; encounter situation; classification model +
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situation when the TS should avoid the OS. However, the COLREGs define only the basic concepts and principles and do not provide specific numerical figures [10]. Furthermore, the navigator's judgment is sometimes respected, and exceptions are recognized [11]. Therefore, the navigator analyzes the encounter situation and determines the action by applying his experience and judgment in the field. Moreover, at this time, the operator's decision is subjective and likely to include errors [12; 13]. This error has been reported to account for most collision accidents [14; 15]. There have been interesting reports to reduce these errors and build algorithms to determine quantitative collision risk. [PERSON] and [PERSON] [16] classified the type of collision risk encounters with obstacles as an area-based method to develop an evaluation method. [PERSON] et al. [17] and [PERSON] [18] divided the areas facing obstacles into six based on the relative bearing of the OS in order to build the algorithm. [PERSON] and [PERSON] [19] numerically classified encounter situations based on the COLREGs to verify the environmental stress model, one of the collision risk indexes. [PERSON] et al. [20] classified encounter situations based on areas and angles to develop a collision avoidance decision-making system. However, it was discovered that the figures for the relative bearings used to distinguish the head-on and the crossing are different in each study. Through a review of previous studies, we confirmed the flow of decision-making regarding collision avoidance by navigators. Here, the navigator's decision is reported to be subjective and contains errors. Interesting contributions have been reported to compensate for the errors and help with safe navigation. The goal of these reports is to develop a decision-making system for collision avoidance that reduces errors. Although many results have been reported, there are still some drawbacks to existing methods for determining encounter situations:  
1. COLREGs use somewhat ambiguous words to distinguish between encounters and do not provide figures for specific angles. 2. Various reports are numerically distinguishing encounters based on the COLREGs. However, we found that the COLREGs do not report specific figures and that the figures are applied differently for each study. As a result, there can be a significant difference in the encounters classified when performing actual collision avoidance. 3. The navigator who learns the regulations and operates the actual ship is a presence that includes errors. There have been few reports of how they judge encounter situations. It can affect collision avoidance behavior. The purpose of this study is to model the results of the encounter situation recognition by the navigator who detected the risk of collision by the COLREGs. Our contribution is focused on modeling the human situation awareness results that distinguish between crossing and head-on. According to the rules of the COLREGs, the overtake could be determined using the angle of the stern light. To explain this process, human situation awareness results were collected in the presence of a collision risk. The variables required for the model were constructed using the collected data.