Core Viewpoint - The article discusses advancements in autonomous driving simulation technology, highlighting the integration of various components such as scene rendering, data collection, and intelligent agents to create realistic driving environments [1][2][3]. Group 1: Simulation Components - The first step involves creating a static environment using 3D Gaussian Splatting and Diffusion Models to build a realistic cityscape, capturing intricate details [1]. - The second step focuses on data collection from panoramic views to extract dynamic assets like vehicles and pedestrians, enhancing the realism of simulations [2]. - The third step emphasizes relighting techniques to ensure that assets appear natural under various lighting conditions, simulating different times of day and weather scenarios [2]. Group 2: Intelligent Agents and Weather Systems - The fourth step introduces intelligent agents that mimic real-world behaviors, allowing for complex interactions within the simulation [3]. - The fifth step incorporates weather systems to enhance the atmospheric realism of the simulation, enabling scenarios like rain or fog [4]. Group 3: Advanced Features - The sixth step includes advanced features that challenge autonomous vehicles with unexpected obstacles, simulating real-world driving complexities [4].