用3D高斯点云构建高效逼真的自动驾驶仿真器，提升模型泛化能力。
Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving
- 基于3D高斯点云的实时场景生成，兼顾真实感与效率
- 支持复杂交通交互，实现闭环评估并提升模型鲁棒性
- 可替代真实数据，显著增强端到端自动驾驶模型泛化
端到端自动驾驶模型需在多样、高质量数据上训练以应对各类驾驶场景。然而，大规模真实数据采集成本高、耗时长，因此高保真合成数据至关重要。现有驾驶模拟器存在明显局限：游戏引擎生成的传感器数据不真实，而基于NeRF和扩散模型的方法效率低下；此外，针对闭环评估设计的模拟器对其他车辆的交互能力有限，难以模拟复杂真实交通动态。为此，我们提出SceneCrafter，一个基于3D高斯点云（3DGS）的逼真、交互性强且高效的自动驾驶模拟器。它不仅能高效生成多样化交通场景下的真实驾驶日志，还可实现端到端模型的稳健闭环评估。实验表明，SceneCrafter既是可靠的评估平台，也是高效的训练数据生成工具，显著提升了端到端模型的泛化能力。
原文摘要 · Abstract (English)
End-to-end (E2E) autonomous driving (AD) models require diverse, high-quality data to perform well across various driving scenarios. However, collecting large-scale real-world data is expensive and time-consuming, making high-fidelity synthetic data essential for enhancing data diversity and model robustness. Existing driving simulators for synthetic data generation have significant limitations: 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 real-world 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 reliable evaluation platform and a efficient data generator that significantly improves end-to-end model generalization.
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