## 5 Driver Acceptance Evaluation Model
### Verifications of the Suggested Model  
To verify the driver acceptance models developed in Section 5.2, another data group consisting of self-decided lane changes was utilized and the suggested models were developed based on the data group with the as-commanded lane changes. For this validation study, both expert and novice drivers were used in the data group for the self-decided lane changes. Additionally, the performance of the driver acceptance models was compared between the two different parameter selection methods.  
Referring to the target data groups designated above, Figures 6 and 7 show the estimated results and ROC analysis of the two types of suggested models, respectively. Tables 7 and 8 present a summary of the performances for each data set in several indexes.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model Classification** & **Performance Index** & **Rejected (Sample Size: 56)** & **Accepted (Sample Size: 131)** & **Total (Sample Size: 187)** \\ \hline \multirow{3}{*}{Driver Acceptance Model using Statistical Significance \(p\)-value} & E[SE] & 0.428 & 0.183 & 0.256 \\ \cline{2-5}  & Var[SE] & 0.108 & 0.025 & 0.062 \\ \cline{2-5}  & SSE & 16.267 & 7.638 & 23.905 \\ \cline{2-5}  & Accuracy & 53.57\% & 75.57\% & 68.98 \\ \cline{2-5}  & (number of correct) & (30) & (99) & (129) \\ \cline{2-5}  & AUC & & & 0.848 \\ \hline \multirow{3}{*}{Driver Acceptance Model using Acceptance Sensitivity (\(S\)_Acceptance_)} & E[SE] & 0.107 & 0.046 & 0.064 \\ \cline{2-5}  & Var[SE] & 0.028 & 0.010 & 0.016 \\ \cline{1-1} \cline{2-5}  & SSE & 2.200 & 1.622 & 3.822 \\ \cline{1-1} \cline{2-5}  & Accuracy & 94.64\% & 98.47\% & 97.33 \\ \cline{1-1} \cline{2-5}  & (number of correct) & (53) & (129) & (182) \\ \cline{1-1} \cline{2-5}  & AUC & & & 0.998 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance comparisons of the driver acceptance models using the different parameter selection methods, the statistical significance test and the acceptance sensitivity analysis, for a specific data set classified as experts/commanded/with traffic.  
As seen in Tables 7 and 8, the model derived from the sensitivity analysis showed better performances compared with the other model in most indexes for both the experts and novices. The comparisons of the classification accuracy were as follows: 78.31% vs. 96.79% for the experts and 68.27% vs. 99.52% for the novices. The AUC comparison results were as follows: 0.868 vs. 0.991 for the experts and 0.902 vs. 0.995 for the novices.  
In the results presented in this section, the same aspects shown in Section 5.2 were observed, i.e., that the driver acceptance model derived with the acceptance sensitivity analysis showed a better performance in most of the indexes. Moreover, it can be stated that the acceptance model with the sensitivity analysis showed much better performances in estimation accuracy. Meanwhile, both the acceptance evaluation models with the two different methods of parameter selection were satisfactory in estimating acceptance when considering the AUC.  
Figure 6: Comparisons of the estimation results for the observed true values between 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 experts/self-decided: (**a**) the estimated values for the subjective assessment and (**b**) the ROC curve.  
Figure 7: Comparisons of the estimation results for the observed true values between 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/self-decided: (**a**) the estimated values for the subjective assessment and (**b**) the ROC curve.