## 6 Conclusions  
Herein, we proposed an integrated verification framework to comprehensively evaluate the credibility of XILS platforms for ADS validation. Through parameter-based and scenario-based evaluations, we quantitatively demonstrated the correlation between VILS and real-vehicle tests and their applicability. Additionally, we introduced a dynamics-based evaluation method, which enables an integrated credibility assessment of the VILS platform. Accordingly, the platform can be deemed credible if it satisfies specific evaluation criteria for parameter-based reliability, scenario-based reliability, and dynamics-based fidelity indicators.  
After conducting the credibility assessment, we visualized each evaluation indicator through spider charts and performed similarity evaluations using three geometric indicators: the Procrustes, Frechet, and Hausdorff distance metrics. Thus, we empirically demonstrated that for a given scenario, credibility can be verified under specific representative speed conditions rather than individually testing all velocity conditions. Furthermore, through geometric similarity evaluations between different scenarios, we highlighted the possibility that an integrated behavior scenario could represent certain other scenarios for credibility evaluation.  
Nevertheless, the experiments in this study covered limited scenarios, driving situations, and velocity conditions. To provide clearer guidance for future research and strengthen the generalizability of the proposed framework, several essential areas warrant further investigation: (1) Environmental scenarios including urban traffic environments with complex intersections and pedestrian interactions, highway scenarios with high-speed merging and convoy driving, and adverse weather conditions such as rain, fog, snow, and varying road surface friction that significantly affect vehicle dynamics and sensor performance; (2) operational conditions encompassing extreme speed ranges (very low speeds < 10 km/h for parking scenarios and high speeds > 120 km/h for highway scenarios), sensor degradation and failure modes including partial occlusion and noise interference, and edge cases such as construction zones and emergency vehicle interactions. These expanded experimental conditions would provide a more comprehensive validation framework and enhance the statistical significance of the geometric similarity analysis across diverse operational domains. Therefore, future studies should pursue credibility verification across these diverse and challenging scenarios to fully validate the robustness and applicability of the proposed methodology.  
In conclusion, the proposed framework provides a standardized platform for XILS-based ADS verification. The integrated credibility evaluation and geometric similarity evaluation methodologies are expected to facilitate the commercialization of safe autonomous driving technology by improving the efficiency of systematic verification processes.  
Conceptualization, S.H. and T.O.; methodology, S.H.; software, S.H.; validation, S.H., T.O. and S.L.; investigation, S.H., S.L. and S.P.; data curation, S.H. and S.P.; writing\(-\) original draft preparation, S.H.; writing\(-\)review and editing, J.Y.; visualization, S.H.; supervision, J.Y. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. NRF-RS-2021-NR060086).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy restrictions and confidentiality agreements with the vehicle testing facility.  
The authors declare no conflicts of interest.