## 5 Field Test Configuration and Results
### Credibility Evaluation Results
#### 5.2.1 Results of Key Parameter-Based Evaluation
The relative longitudinal distance plot shows the gap between the vehicles widening during the initial 0-5 s period and then gradually narrowing after the cut-in maneuver, while the relative velocity plot exhibits a peak of about -6 m/s at the cut-in point, indicating that the target vehicle entered the subject vehicle's lane at a lower speed. The relative lateral distance displayed greater variability during the real-vehicle  
Figure 7: Test results of key parameters related to lateral behavior in Scenario B.  
tests due to initial-position offsets, lane-alignment differences, and sensor-perception errors, whereas the VILS test yielded more consistent results because of the virtual sensor modeling and automated scenario execution. Table 2 lists the consistency indices of the main test parameters calculated using equations formulated in previous studies [29; 30], while Table 3 presents the correlation and applicability indices calculated using Equations (7) and (8). Compared with the prior study [31], the ordering of the parameters in this study was different, e.g., our \(p_{1}\) (lateral acceleration) corresponds to \(p_{8}\) in the previous study. Considering this, a particularly noteworthy finding is that, whereas the earlier paper reported a correlation index of 71.8% for \(p_{8}\), thus failing to meet the evaluation criterion (\(\alpha_{p}=76.3\%\)), we achieved an improved correlation index of approximately 88.25% for p\({}_{1}\). This improvement stems from enhancements to the simulation logic: unlike in the previous study, we automated event triggering (e.g., lane changes) within fixed segments to minimize manual tester intervention, which markedly boosted reproducibility in the lateral performance scenarios. Furthermore, our \(p_{12}\) (relative velocity) consolidates \(RV_{x}\) and \(RV_{y}\) from the prior study into a single parameter. Despite this simplification, this parameter exhibited a high correlation index of 93.89%, which demonstrates the effectiveness of our parameter de  
Figure 8: Test results of key parameters related to longitudinal behavior in Scenario C. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} Parameter Index & \(p_{1}\) & \(p_{2}\) & \(p_{3}\) & \(p_{4}\) & \(p_{5}\) & \(p_{6}\) & \(p_{7}\) & \(p_{8}\) & \(p_{9}\) & \(p_{10}\) & \(p_{11}\) & \(p_{12}\) & **Criterion** \\ \hline \(C_{m}\)(\%) & 88.25 & 88.39 & 84.42 & 97.02 & 92.61 & 87.68 & 91.69 & 95.74 & 89.25 & 95.02 & 88.24 & 93.89 & 76.34 (\(\alpha_{p}\)) \\ \hline \(A_{m}\)(\%) & 106.11 & 102.84 & 102.71 & 99.91 & 101.48 & 110.33 & 103.7 & 100.12 & 101.44 & 100.31 & 119.38 & 105.19 & 83.51 (\(\beta_{P}\)) \\ \hline \end{tabular}
\end{table} 
Table 3: Correlation and applicability indices of key parameters, along with evaluation criteria. sign. The applicability index (\(A_{m}\)) exceeded 100% for all parameters, thereby confirming the superior reproducibility and repeatability of the VILS tests. Specifically, \(p_{11}\) (relative lateral distance) exhibited an applicability index of 119.38%, attributable to our improved perception sensor model and virtual traffic generation algorithm. In summary, the VILS system implemented in this study--improving on the system from the previous research--achieved greater reliability in terms of certain parameters through reduced tester involvement and a more refined automation logic. Given that all parameters satisfied their respective evaluation criteria, the simulation environment for ADS verification can be considered credible.