## 6 Discussion  
This paper proposed an objective evaluation method for passenger acceptance of an autonomous driving system using a stochastic estimation methodology. In this study, an automatic lane change system was the target system as one of the representative autonomous driving systems. In this study, the selection methods for objective parameters related to acceptance were defined in two ways: via a significance test and sensitivity analysis. Additionally, an acceptance evaluation model was proposed to determine acceptance with respect to the previously derived objective parameters. Based on the suggested methods of parameter selection and modeling, two types of acceptance evaluation models were derived and then compared and validated. The validations used other data groups not used for the modeling. In the results, the model derived from the sensitivity analysis showed better performances compared with the other model in most indexes; the accuracy comparisons were 78.31% vs. 96.79% for the experts and 68.27% vs. 99.52% for the novices; the AUC comparisons were 0.868 vs. 0.991 for the experts and 0.902 vs. 0.995 for the novices, respectively. While both acceptance evaluation models using the two different methods of parameter selection were satisfactory \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Classification** & **Performance Index** & **Rejected (Sample Size: 100)** & **Accepted (Sample Size: 149)** & **Total (Sample Size: 249)** \\ \hline \multirow{4}{*}{Driver Acceptance Model using Statistical Significance \(p\)-value} & E[SE] & 0.342 & 0.230 & 0.275 \\ \cline{2-5}  & Var[SE] & 0.095 & 0.035 & 0.062 \\ \cline{2-5}  & SSE & 21.242 & 13.022 & 34.264 \\ \cline{2-5}  & Accuracy & 61.00 & 89.93 & 78.31 \\ \cline{2-5}  & (number of correct) & (61) & (134) & (195) \\ \cline{2-5}  & AUC & & 0.868 \\ \hline \multirow{4}{*}{Driver Acceptance Model using Acetpance Sensitivity \(\big{(}S_{Acceptance}\big{)}\)} & E[SE] & 0.134 & 0.090 & 0.108 \\ \cline{2-5}  & Var[SE] & 0.034 & 0.018 & 0.025 \\ \cline{2-5}  & SSE & 5.150 & 3.910 & 9.059 \\ \cline{2-5}  & Accuracy & 94.00 & 98.66 & 96.79 \\ \cline{2-5}  & (number of correct) & (94) & (147) & (241) \\ \cline{2-5}  & AUC & & 0.991 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance comparisons of the driver acceptance models using the different parameter selection methods, the statistical significance test and the acceptance sensitivity analysis, for specific data sets classified as expert drivers/self-decided/with traffic. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Classification** & **Performance Index** & **Rejected (Sample Size: 62)** & **Accepted (Sample Size: 146)** & **Total (Sample Size: 208)** \\ \hline \multirow{4}{*}{Driver Acceptance Model using Statistical Significance \(p\)-value} & E[SE] & 0.357 & 0.152 & 0.213 \\ \cline{2-5}  & Var[SE] & 0.112 & 0.030 & 0.063 \\ \cline{2-5}  & SSE & 14.845 & 7.803 & 22.648 \\ \cline{2-5}  & Accuracy & 56.45\% & 73.29\% & 68.27\% \\ \cline{2-5}  & AUC & & 0.902 \\ \hline \multirow{4}{*}{Driver Acceptance Model using Statistical Significance \(p\)-value} & E[SE] & 0.060 & 0.026 & 0.036 \\ \cline{2-5}  & Var[SE] & 0.022 & 0.005 & 0.010 \\ \cline{2-5}  & SSE & 1.576 & 0.837 & 2.413 \\ \cline{2-5}  & Accuracy & 98.39\% & 100.00\% & 99.52\% \\ \cline{2-5}  & (number of correct) & (61) & (146) & (207) \\ \cline{2-5}  & AUC & & 0.995 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance comparisons of the driver acceptance models using the different parameter selection methods, the statistical significance test and the acceptance sensitivity analysis, for specific data sets classified as novice drivers/self-decided/with traffic. in estimating the acceptance when considering the AUC, the acceptance model with the sensitivity analysis showed much better performances in estimation accuracy. The differences in the performances are based on the number of modeling parameters used for each one.