## 4 Results  
### GSR Analysis Data  
The features from the GSR data increased as the risk level of the scenario increased, indicating that the risk scenarios that we implemented were designed properly to invoke the intended relative level of intense emotions. Thus, we were able to validate that the scenarios designed with higher risk induced intense feelings in the subjects. We analyzed the GSR phasic data in the 10 s before and after the event in all eight cases, and extracted the maximum and mean values. We performed repeated measures ANOVA analysis on these features for a total of eight scenarios using JASP software. The analysis revealed significant differences according to the risk level for the two features of GSR Phasic Max and GSR Phasic Mean as shown in Figure 5: GSR Phasic Max, F(3, 132) = 34.871, \(p\) < 0.001; GSR Phasic Mean, F(3, 132) = 25.567, \(p\) < 0.001. We conducted the post-hoc analysis to find the change in GSR feature values according to the level of risk and information on the HUD. In the case of GSR Phasic Max, the high-risk condition was significantly higher than the medium-risk (\(p\) = 0.003), low-risk (\(p\) < 0.001), and no-risk conditions (\(p\) < 0.001). In the case of medium risk, the low-risk (\(p\) < 0.001) and no-risk conditions were significantly higher than that in the control condition (\(p\) < 0.001). In addition, when the AR information was provided, the value of GSR Phasic Max was slightly higher than that when the information was not provided (\(p\) = 0.350). In the case of GSR Phasic Mean, the high-risk condition was significantly higher than the medium-risk (\(p\) = 0.001), low-risk (\(p\) < 0.001), and no-risk conditions (\(p\) < 0.001). The medium-risk condition was significantly higher than the low-risk (\(p\) < 0.001) and the no-risk (\(p\) = 0.004) conditions. In addition, when information was provided, the value of GSR Phasic Mean was slightly higher than when the information was not provided (\(p\) = 0.735). From these results, we can validate the design of the risk scenarios.  
We investigated any significant changes in the GSR phasic data before and after the event. Figure 6 left shows the average GSR phasic data in the 10 s before and after the event for each case for the 45 subjects. From the graph, we can see that there is a rapid change in the GSR phasic value due to the event occurrence for high-risky and medium-risky conditions. In order to compare statistical values, we compared the maximum values of GSR phasic data, 10 s before and after the event occurred. We also found that there was a significant increase in the GSR phasic value in the 10 s before the event for 6 cases (Figure 6 right). A \(t\)-test was conducted to confirm the significant relationship between data before and after the event: high-risk and no-information, \(t(44)=-6.754\), \(p\) < 0.001; high-risk and information, \(t(44)=-7.481\), \(p\) < 0.001; medium-risk and no-information, \(t(44)=-6.006\), \(p\) < 0.001; medium-risk and information, \(t(44)=-6.681\), \(p\) < 0.001; low-risk and no-information, \(t(44)=-2.195\), \(p\) = 0.033; low-risk and information, \(t(44)=-2.521\), \(p\) = 0.015. In other words, the biometric data showed a rapid change according to our risk level, which implies that autonomous vehicle research using our motion simulation system is quite meaningful.  
Figure 5: A graph that compares the maximum, mean, and accumulation value of GSR phasic data according to risk level and whether or not information was provisioned through HUD. The use of asterisks in the figure indicates statistical significance. Two (**) denotes a \(p\)-value less than 0.01, while three (***) asterisks denote \(p\)-values less than 0.001, respectively.