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Driving simulators are critical tools for automotive human factors research, driver training, and entertainment. However, traditional simulators suffer from a fundamental limitation: the finite, manually crafted size of their virtual environments. This paper proposes and analyzes the architecture for a , a system that leverages Google’s massive geospatial database (Photorealistic 3D Tiles) as the infinite, real-world driving environment. We address the core technical challenges—including dynamic Level-of-Detail (LOD) streaming, collision detection on non-manifold mesh data, and real-time lighting unification between the vehicle model and global imagery. We present a proof-of-concept integration using the Google Maps Tiles API, a custom Unity-based renderer, and a physics engine modified for streaming assets. Results indicate that while visual fidelity at high altitude is excellent, ground-level artifacts and network latency remain significant hurdles. The paper concludes with potential solutions, including neural supersampling and predictive tile pre-fetching, positioning the integrated system as the future of geographic-scale driving simulation. Google Earth’s beauty is in the vastness
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