# Modeling Human Encounter Situation Awareness Results Using Support Vector Machine Models
## 4 Discussion  
This study used a support vector machine to build the encounter situation recognized by the navigator as a classification model. The main contribution of this study was to develop a model that predicts encounters recognized by navigators in situations with a risk of collision. This section describes the data, models, and results used in the study. As described in Section 2, three numerical predictors were applied: relative bearing, aspect, and speed ratio. As reported in previous studies, these variables can be obtained from the relative relationship between the OS and TS. This study shows a difference in that the distance between the OS and TS is designated as 6 miles, which is known to initiate collision avoidance action. The variables used in the model are data obtained from surveys based on scenarios estimated based on the CRI that there is a collision risk in the relative relationship between the OS and TS. The CRI figures are generally accepted as having a risk of collision [21]. Therefore, the question that answered that it was safe to a given question was not used to build the model. The survey was conducted on 40 navigators. However, we judged that model construction is sufficiently possible because there are 4767 data points used for model construction and verification.  
For the model in this study to be used for situation prediction, a high-performance model must be built using the results of human situation awareness in situations with a risk of collision. As mentioned in Section 3.2, a model was established to label the results of situation recognition as head-on, give-away, and stand-on and predict the recognition results.  
As a result of the results mentioned in Section 3.2.1 and the verification through the ROC and AUC metrics and confusion matrix in Section 3.2.2, the model proposed in this study obtained a high performance. In particular, it is inferred that the classifier's performance is good, given that the ROC and AUC metrics score was 0.9 or higher [37].  
Table 7 shows some examples of the results predicted using the polynomial (3d) model and the previous methods. In Table 7, the proposed model gives the prediction results of the polynomial (3d) model. Method 1 gives the criteria of [PERSON] and [PERSON] [16], Method 2 gives the criteria of [PERSON] et al. [17] and [PERSON] [18], Method 3 gives the criteria of [PERSON] and [PERSON] [19], and Method 4 gives the criteria of [PERSON] et al. [20]. At this time, Method 1 classified most cases into a head-on situation. It is because the standard of classification is 22.5 degrees, which has the most extensive range. Meanwhile, there was a section in which the awareness results of the navigator were collected as a sample, and the reported methods were calculated differently. The reason is that humans contain errors and are influenced by situational awareness based on skills and technical knowledge, so these effects are presumed to have been reflected.  
On the other hand, the limitations of this study are as follows. First, the data were collected through a survey. Surveys are reported in a non-experimental way in data collection. It also has the advantage of acquiring data relatively simply. However, errors in the collected survey data and problems with the representability of the population have been reported [38]. Second, the model built in this study is a model that predicts only situation recognition results. Therefore, it is not easy to analyze in detail the factors that affect the results of each explanatory variable. Third, situational awareness and decision-making are influenced by human skills, rules, and knowledge [39]. The effect of these three factors has yet to be specifically analyzed in this study. These have been discovered as challenges to be solved in future works.