Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving

End-to-end ($E 2 E$) autonomous driving ($A D$) models require diverse, high-quality data to perform well across various driving scenarios. However, collecting large-scale realworld data is expensive and time-consuming, making highfidelity synthetic data essential for enhancing data diversity and model robustness. Existing driving simulators have significant limitations for synthetic data generation: game-engine-based simulators struggle to produce realistic sensor data, while NeRF-based and diffusion-based methods face efficiency challenges. Additionally, recent simulators designed for closed-loop evaluation provide limited interaction with other vehicles, failing to simulate complex realworld traffic dynamics. To address these issues, we introduce SceneCrafter, a realistic, interactive, and efficient AD simulator based on 3D Gaussian Splatting (3DGS). SceneCrafter not only efficiently generates realistic driving logs across diverse traffic scenarios but also enables robust closed-loop evaluation of end-to-end models. Experimental results demonstrate that SceneCrafter serves as both a Abstract reliable evaluation platform and a efficient data generator that significantly improves end-to-end model generalization. Our code will be released at https://github. com/cancaries/SceneCrafter.