## 3 Proposed Credibility Evaluation Methodology  
Before utilizing XILS environments for ADS validation, the credibility of the XILS platform itself must be rigorously assessed. It is important to clarify the distinction between credibility, reliability, and fidelity as used in this framework. Following UNECE DCAS regulations, credibility represents a comprehensive assessment that encompasses both reliability (the consistency and repeatability of results under identical conditions) and fidelity (the accuracy with which simulation models reproduce real-world physical characteristics and behaviors). While reliability focuses on reproducibility and consistency between repeated tests, fidelity emphasizes the physical accuracy of dynamic responses. Credibility, therefore, serves as an overarching metric that ensures a simulation platform is both consistent in its outputs and accurate in its representation of real-world phenomena, making it fit for its intended validation purpose. We propose a framework to quantify the credibility of XILS through a comprehensive assessment from parameter-based, scenario-based, and dynamics-based perspectives (Figure 2). Figure 2 presents the comprehensive three-dimensional credibility evaluation framework for XILS platforms, building upon the reliability assessment methodology established by [PERSON] [31]. The framework illustrates a systematic matrix-based comparison structure that processes similarity calculations between different test configurations (virtual-virtual, real-real, and virtual-real combinations) across multiple dimensions. The core of the methodology centers on structured similarity matrices where each table represents different comparison types indicated by the (virtual, real) labels at the top and left sides. Within each matrix, black-bordered cells represent the similarity comparison results for individual parameters (one of m parameters) between specific test iterations, enabling detailed component-level analysis. Red-bordered horizontal sections encompass the similarity results for all m parameters within a single scenario (one of k scenarios), providing scenario-level aggregated comparisons. Blue-bordered vertical stacks represent similarity results for a single parameter (one of m parameters) across all k scenarios, facilitating parameter-level cross-scenario analysis. These multi-layered matrix structures feed into the three parallel evaluation streams: (1) Parameter-based reliability assessment utilizes the blue-bordered parameter stacks to calculate correlation indices \((C_{m})\) and applicability indices \((A_{m})\) for each of the m key parameters, identifying specific modeling deficiencies in simulation components; (2) scenario-based reliability assessment leverages the red-bordered scenario sections to compute scenario-level correlation \((C_{k})\) and applicability \((A_{k})\) indices, determining replacement feasibility of real-world tests; (3) dynamics-based fidelity assessment employs additional orange-bordered matrices that specifically compare six-degree-of-freedom motion characteristics between virtual and real test configurations, yielding dynamic correlation indices \((D_{k})\). Validation test scenarios and key test parameters are defined, and data are then collected by performing repeated real-world and XILS tests under identical conditions. The collected data are analyzed across three core dimensions before being integrated into the final credibility assessment. The parameter-based reliability assessment involves calculating the applicability index (\(A_{m}\)) and correlation index (\(C_{m}\)) for each key test parameter. The applicability index determines whether XILS tests offer better repeatability and reproducibility than real-world tests, quantifying the consistency of individual component models within the XILS platform in maintaining their outputs across multiple test iterations.