## 5 Field Test Configuration and Results
### Results of Geometric Similarity Evaluation
#### 5.3.1 Results of Efficiency Evaluation  
The geometric similarity vectors for each speed condition in each test scenario were defined using Equations (41)-(43). Based on these vectors, we quantitatively analyzed the geometric similarity between the two shapes depicted in the spider charts to determine the testing efficiency during credibility evaluation. To evaluate the consistency of the differences between the shapes corresponding to the different speed conditions, we utilized vector components based on the three previously derived metrics: dynamics-based correlation \((D_{k})\), scenario-based applicability \((A_{k})\), and scenario-based correlation \((C_{k})\). Table 8 summarizes the values of \(D_{k}\), \(A_{k}\), and \(C_{k}\) for Scenarios A-F, which were used as input vectors to compare the geometric shapes on the spider charts. Figure 9 presents the triangular spider charts constructed based on these vectors, which provide an intuitive visualization of the geometric characteristics and variation patterns corresponding to each speed condition in each scenario. \begin{table}
\begin{tabular}{c|c c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Scenario A**} & \multicolumn{3}{c}{**Scenario B**} & \multicolumn{3}{c}{**Scenario C**} \\ \cline{2-11} Speed & \(Y_{A}\) & \(D_{k}\) & \(A_{k}\) & \(C_{k}\) & Speed & \(Y_{B}\) & \(D_{k}\) & \(A_{k}\) & \(C_{k}\) & Speed & \(Y_{C}\) & \(D_{k}\) & \(A_{k}\) & \(C_{k}\) \\ \hline Low & 83.92 & 106.55 & 95.79 & Low & 83.77 & 101.91 & 94.86 & Low & 78.43 & 103.14 & 92.11 \\ \hline Mid & 84.17 & 107.77 & 93.96 & Mid & 83.49 & 102.1 & 93.86 & Mid & 77.43 & 107.62 & 88.21 \\ \hline High & 83.91 & 107.95 & 92.49 & High & 82.64 & 103.31 & 91.23 & High & 76.5 & 109.63 & 84.33 \\ \hline \multicolumn{11}{c}{**Scenario D**} & \multicolumn{3}{c}{**Scenario E**} & \multicolumn{3}{c}{**Scenario F**} \\ \cline{2-11} Speed & \(Y_{D}\) & \(D_{k}\) & \(A_{k}\) & \(C_{k}\) & Speed & \(Y_{E}\) & \(D_{k}\) & \(A_{k}\) & \(C_{k}\) & Speed & \(Y_{F}\) & \(D_{k}\) & \(A_{k}\) & \(C_{k}\) \\ \hline Low & 81.43 & 105.39 & 96.54 & Low & 75.95 & 104.34 & 89.53 & Low & 76.67 & 100.69 & 89.43 \\ \hline Mid & 80.85 & 108.17 & 92.81 & Mid & 75.15 & 105.37 & 87.2 & Mid & 75.93 & 105.98 & 87.86 \\ \hline High & 80.03 & 110.37 & 88.09 & High & 74.47 & 105.8 & 82.81 & High & 75.13 & 108.66 & 83.93 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Geometric similarity vector components for each scenario. Figure 9: Spider charts showing the geometric similarity index for each scenario. The similarities between these shapes were quantitatively assessed based on the Procrustes, Frechet, and Hausdorff distance metrics introduced earlier; these metrics were calculated using Equations (37)-(39), respectively, and captured different geometric characteristics, allowing for complementary evaluations. The geometric similarity index \((G_{k})\) was then derived via a weighted integration of the three distance measures based on Equation (44). Table 9 lists the scores calculated accordingly for each scenario. The analysis confirms that the geometric similarity indices for all scenarios significantly exceeded the evaluation criterion (71.68%), thereby statistically validating the morphological consistency across speed conditions within each scenario. This indicates that speed is not a dominant factor in credibility assessment, suggesting that independent credibility evaluations for different speed conditions within a given test scenario are unnecessary. Representative speed conditions can sufficiently capture the essential characteristics of any scenario, thus enhancing testing efficiency considerably. However, notably different patterns emerged across the distinct scenario types. The lateral-motion scenarios (A and B) exhibited high similarity indices (81.11% and 80.86%, respectively), while the longitudinal-motion scenarios (C and D) recorded relatively lower indices (76.09% and 75.43%, respectively). These differences reflect the fundamental characteristics of vehicle dynamics.