## 5 Field Test Configuration and Results
### Credibility Evaluation Results
#### 5.2.3 Results of Dynamics-Based Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & **Scenario** & **A** & **B** & **C** & **D** & **E** & **F** \\ \hline \multirow{3}{*}{\(D_{k}\left(r\right)\)} & Low & 0.880 & 0.887 & 0.728 & 0.781 & 0.711 & 0.703 \\ \cline{2-8}  & Mid & 0.853 & 0.854 & 0.711 & 0.752 & 0.684 & 0.673 \\ \cline{2-8}  & High & 0.814 & 0.818 & 0.668 & 0.721 & 0.658 & 0.644 \\ \hline \multirow{3}{*}{\(D_{k}\left(v\right)\)} & Low & 0.924 & 0.931 & 0.769 & 0.825 & 0.753 & 0.756 \\ \cline{2-8}  & Mid & 0.892 & 0.899 & 0.74 & 0.796 & 0.724 & 0.725 \\ \cline{2-8}  & High & 0.854 & 0.865 & 0.709 & 0.764 & 0.697 & 0.697 \\ \hline \multirow{3}{*}{\(D_{k}\left(r,v\right)\)} & Low & 0.741 & 0.743 & 0.571 & 0.636 & 0.54 & 0.539 \\ \cline{2-8}  & Mid & 0.718 & 0.713 & 0.542 & 0.608 & 0.514 & 0.511 \\ \cline{1-1} \cline{2-8}  & High & 0.683 & 0.676 & 0.511 & 0.577 & 0.491 & 0.483 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Dynamics-based consistency index for each scenario. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & **Scenario** & **A** & **B** & **C** & **D** & **E** & **F** & **Criterion** \\ \hline \multirow{3}{*}{\(D_{k}\left(\%\right)\)} & Low & 84.205 & 83.766 & 78.434 & 81.434 & 75.949 & 76.671 & 68.6 (\(\alpha_{D}\)) \\ \cline{2-8}  & Mid & 84.174 & 83.489 & 77.429 & 80.851 & 75.146 & 75.929 & 65.62 (\(\alpha_{D}\)) \\ \cline{2-8}  & High & 83.907 & 82.641 & 76.497 & 80.028 & 74.468 & 75.127 & 62.55 (\(\alpha_{D}\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Dynamics-based correlation for each scenario, along with evaluation criteria. vidual parameters or scenario-related results and do not comprehensively reflect the dynamics of the vehicle. In contrast, the proposed dynamics-based methodology ensures a comprehensive evaluation by measuring the dynamic response of the entire vehicle system. The consideration of real-time control feedback represents another important advantage. The dynamics-based evaluation includes the vehicle control system feedback loops, as well as their time delays and nonlinear response characteristics. This is particularly important under high-speed conditions or in complex scenarios as it enables a systematic analysis of uncertainty factors. Despite the merits of the proposed methodology, considering the results of our evaluation, specific aspects of the framework can be improved to strengthen the credibility assessment of the VILS platform further. The most important enhancement would be the incorporation of a highly accurate, real-world-based tire-road interaction model. A tire model that accurately simulates the physical properties and behavior of real tires is necessary, as is using high-resolution road-profile data to simulate actual road surface conditions more accurately. The input response of the high-speed area control must be synchronized better as well. Algorithms must be developed to compensate for the time delays and nonlinearities that occur during high-speed driving. Delay compensation within the controller is another important requirement, which necessitates the introduction of compensation techniques to minimize the difference in control signal delay between the real vehicle and the simulated vehicle. The credibility of the evaluation can be improved further by extending the experimental conditions to cover more types of driving situations. Moreover, the statistical analysis should be strengthened with additional scenarios, speed conditions, and experimental trials. Specifically, the scope of the evaluation must be expanded by adding variables such as extreme weather conditions. Overall, we comprehensively validated the credibility of the VILS platform through parameter-based and scenario-based reliability evaluations and a dynamics-based fidelity evaluation.