## 1 Introduction  
The rapid development of autonomous driving systems (ADSs) has led to an exponential increase in software and hardware complexity. This trend is further accelerated by the emergence of autonomous aerial vehicles (AAVs), which extend autonomous technology beyond ground vehicles and introduce even more complex safety challenges [1]. These developments highlight the importance of sophisticated verification methodologies to ensure overall system safety [2, 3, 4]. As the level of autonomous driving advances, driver intervention progressively diminishes, which inevitably shifts the responsibility for accidents caused by system defects or limitations toward the manufacturers [5, 6]. Therefore, ADSs must perform robustly under various driving conditions and unpredictable circumstances,which necessitates simulation-based verification frameworks that can evaluate system behavior quantitatively and reproducibly [7; 8]. Real-vehicle testing conducted on actual roads offers clear advantages in terms of directly validating systems in real driving situations and naturally reflecting the many variables and uncertainties inherent in real-world environments. However, such testing involves fundamental constraints, including difficulties in ensuring reproducibility, limitations in implementing extreme situations, substantial costs, and safety concerns [9; 10; 11]. To overcome these limitations, X-in-the-loop simulation (XILS) has been widely adopted, with representative methodologies including model-in-the-loop simulation (MILS), software-in-the-loop simulation (SILS), and hardware-in-the-loop simulation (HILS) [12; 13; 14]. However, such simulation-based verification methods cannot perfectly reflect the dynamic characteristics of actual vehicles. Among the various XILS approaches, this limitation has prompted a growing interest in vehicle-in-the-loop simulation (VILS), which integrates simulation environments with real vehicles to achieve higher fidelity [15; 16]. VILS is considered highly credible as it enables not only repetitive and safe reproduction of various scenarios, but also precise evaluation based on the physical responses of actual vehicles [17; 18]. However, existing research has focused primarily on verification at a simple functional level, with very few systematic methodologies available for quantitatively evaluating the credibility of simulation platforms [19]. Thus, although research is being actively conducted to validate ADSs through XILS, a system for quantitatively evaluating the credibility of the simulation platforms as a virtual tool chain remains to be developed. Given the critical importance of reliable simulation platforms for ADS validation, standardized evaluation frameworks have become essential. Recognizing these challenges, the United Nations Economic Commission for Europe (UNECE) has included credibility-assessment principles for virtual tool chains in its SAE Level 2 ADS Driver Control Assistance Systems (DCAS) regulation (ECE/TRANS/WP.29/2024/37) [20]. According to the DCAS regulation, credibility defines whether a simulation is fit for its intended purpose, based on a comprehensive assessment of five key characteristics of modeling and simulation: capability, accuracy, correctness, usability, and fitness for purpose. The concept of credibility in this regard is distinct from simply reliability. Whereas reliability refers to the consistency of results obtained under the same conditions, credibility is a metric that comprehensively quantifies how well a simulation reflects the relevant real-world system. Thus, for a simulation platform to be deemed credible, it must not only be reliable (i.e., capable of providing reproducible results) but also offer fidelity (i.e., the ability to accurately replicate the physical characteristics and behavior of the actual system) [21].