## 3 Proposed Credibility Evaluation Methodology
### Dynamics-Based Fidelity Evaluation  
While the parameter-based and scenario-based evaluations discussed earlier are useful for quantifying the consistency between real-vehicle tests and XILS tests, they cannot fully capture the complex physical phenomena that a vehicle experiences during actual driving. For example, the vertical behavior of a vehicle when driving on irregular surfaces, the interaction between translational and rotational motions due to crosswinds, and the nonlinear lateral dynamics that occur during sudden steering cannot be fully assessed through simple input-output comparisons [38; 39; 40]. If these vehicle dynamics characteristics under real disturbance conditions are not properly reflected in the simulation model, applying results obtained from the virtual environment to real-world situations can lead to unpredictable errors and compromise safety [41; 42; 43]. Therefore, we propose a dynamics-based evaluation method to assess how faithfully the simulation reproduces the physical dynamics of the vehicle. Simulations using 3 DOF models have been performed to quantitatively analyze the longitudinal and lateral behavior of vehicles, and various control strategies and driving stability techniques have been developed accordingly [44; 45]. However, as these models do not fully incorporate the roll, pitch, and yaw variations of the vehicle, they cannot accurately reproduce the dynamic responses observed in real vehicles during high-speed driving, during sudden steering, or under complex road and weather conditions [46; 47]. Moreover, with the focus of previous research being on system verification, the physical credibility of simulation platforms themselves has yet to be appropriately assessed. To overcome these limitations, we introduce a 6 DOF model that can precisely evaluate the accuracy of the simulation platform in reproducing the complex dynamic responses of the vehicle body. This method enables objective comparison of the dynamic consistency between the results of simulations and actual tests under various disturbance conditions. The proposed 6 DOF model accounts for the vehicle's longitudinal (\(X\)), lateral (\(Y\)), and vertical (\(Z\)) translational motions, along with its roll (\(\varphi\)), pitch (\(\theta\)), and yaw (\(\psi\)) rotational motions; thus, it can describe the vehicle's complete dynamics by assuming it to be a rigid body based on Newton-Euler equations [48; 49]. However, as Coriolis and gyroscopic effects are minimal for vehicles in contact with the ground, these terms are omitted to simplify the model [50; 51]. This exclusion significantly reduces the computational load of the simulation while enabling more intuitive and efficient comparison with actual experimental data. The 6 DOF model-based dynamics evaluation significantly enhances the physical credibility of the simulation platform by considering both translational and rotational motions, which 3 DOF models fail to achieve. Thus, this evaluation method provides an essential foundation for verifying the credibility of autonomous driving simulation platforms and improving the adaptability of ADSs to real-world driving environments. The equation of a vehicle's translational motion explains the relationship between the forces acting when a vehicle moves in space and the resulting acceleration. Considering \(m\) to be the vehicle's mass and \(g\) to be Earth's gravitational acceleration, the forces acting on the vehicle can be defined based on [PERSON]'s second law (\(F=m\cdot a\)). Along the vehicle's \(X\)-axis (forward-backward direction) and \(Y\)-axis (left-right direction), the force is the product of the respective mass and acceleration values. Along the \(Z\)-axis (vertical direction), the influence of gravitational acceleration must be additionally considered. Accordingly, these translational motions can be represented by Equation (17), which mathematically expresses the vehicle's motion in each direction.