## 5 Field Test Configuration and Results
### Credibility Evaluation Results
#### 5.2.2 Results of Scenario-Based Evaluation  
The six test scenarios shown in Figure 3 were evaluated using the scenario-based reliability assessment methodology introduced in Section 3.3. Table 4 lists the consistency indices, with values closer to 1 indicating a higher similarity between the two experiments compared. Table 5 presents the correlation and applicability indices for each test scenario. According to the data in Table 4, the inter-repetition consistency indices of the VILS tests generally exceeded those of the real-vehicle tests across all scenarios. This indicates that the VILS tests could reproduce the same scenario with higher precision and repeatability, which highlights their advantage over real-vehicle tests in terms of reproducibility and consistency within a simulation environment. However, the consistency index between the real-vehicle and VILS tests was slightly lower than the consistency index within each testing environment. This implies that within the same experimental configuration, structural differences between the two platforms, along with input errors, affected the consistency between the corresponding results. These observations indicate inherent differences between real-world environments and simulation environments. Notably, Scenarios E and F exhibited lower consistency indices than the other scenarios. This is because these scenarios include both longitudinal and lateral vehicle behavior, which restricts the possibility of achieving consistent results. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \
\begin{tabular}{c} **Index** \\ \end{tabular} &
\begin{tabular}{c} **Scenario** \\ \end{tabular} & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline \multirow{3}{*}{\(S_{k}\left(r\right)\)} & Low & 0.922 & 0.963 & 0.942 & 0.916 & 0.864 & 0.846 \\ \cline{2-8}  & Mid & 0.895 & 0.952 & 0.893 & 0.88 & 0.845 & 0.82 \\ \cline{2-8}  & High & 0.873 & 0.933 & 0.865 & 0.848 & 0.822 & 0.806 \\ \cline{2-8}  & Low & 0.982 & 0.981 & 0.972 & 0.965 & 0.902 & 0.852 \\ \cline{2-8}  & Mid & 0.965 & 0.972 & 0.961 & 0.952 & 0.89 & 0.87 \\ \cline{2-8}  & High & 0.943 & 0.963 & 0.948 & 0.935 & 0.87 & 0.876 \\ \hline \multirow{3}{*}{\(S_{k}\left(r\right)\)} & Low & 0.883 & 0.913 & 0.868 & 0.884 & 0.774 & 0.757 \\ \cline{2-8}  & Mid & 0.841 & 0.894 & 0.786 & 0.817 & 0.737 & 0.721 \\ \cline{2-8}  & High & 0.808 & 0.851 & 0.729 & 0.747 & 0.681 & 0.676 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Consistency indices for each scenario. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \
\begin{tabular}{c} **Index** \\ \end{tabular} &
\begin{tabular}{c} **Scenario** \\ \end{tabular} & **A** & **B** & **C** & **D** & **E** & **F** & **Criterion** \\ \hline \multirow{3}{*}{\(C_{k}\left(\%\right)\)} & Low & 95.79 & 94.86 & 92.11 & 96.54 & 89.53 & 89.43 & 83.9 (\(\mathbf{\kappa_{S}}\)) \\ \cline{2-8}  & Mid & 93.96 & 93.86 & 88.21 & 92.81 & 87.2 & 87.86 & 82.8 (\(\mathbf{\kappa_{S}}\)) \\ \cline{2-8}  & High & 92.49 & 91.23 & 84.33 & 88.09 & 82.81 & 83.93 & 80.7 (\(\mathbf{\kappa_{S}}\)) \\ \hline \multirow{3}{*}{\(A_{k}\left(\%\right)\)} & Low & 106.55 & 101.91 & 103.14 & 105.39 & 104.34 & 100.69 & 96.7 (\(\mathbf{\beta_{S}}\)) \\ \cline{2-8}  & Mid & 107.77 & 102.1 & 107.62 & 108.17 & 105.37 & 105.98 & 97.1 (\(\mathbf{\beta_{S}}\)) \\ \cline{1-1} \cline{2-8}  & High & 107.95 & 103.31 & 109.63 & 110.37 & 105.81 & 108.66 & 96.9 (\(\mathbf{\beta_{S}}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Correlation and applicability indices for each scenario, along with evaluation criteria. Another important pattern in Table 4 is the variation in the consistency index according to the speed condition. In all scenarios, the consistency indices displayed a clear decreasing trend with increasing speed.