Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models

This study constructs a support vector machine model based on supervised learning to the results of situation awareness for ship collision avoidance. To explain model, risk situations were defined, and human recognition collected in specified cases. Moreover, it was used build predictors outcome variables. Finally, constructed variable applied classification model. provides insight into navigator’s encounter when avoidance is required. The indicate that proposed can be predict outcomes given