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

• URL: http://arxiv.org/abs/2503.18108v1
• Date: Sun, 23 Mar 2025 15:27:43 GMT
• Title: Unraveling the Effects of Synthetic Data on End-to-End Autonomous Driving
• Authors: Junhao Ge, Zuhong Liu, Longteng Fan, Yifan Jiang, Jiaqi Su, Yiming Li, Zhejun Zhang, Siheng Chen,
• Abstract summary: We introduce SceneCrafter, a realistic, interactive, and efficient autonomous driving simulator based on 3D Gaussian Splatting (3DGS)<n>SceneCrafter efficiently generates realistic driving logs across diverse traffic scenarios.<n>It also enables robust closed-loop evaluation of end-to-end models.
• Score: 35.49042205415498
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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