End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation
Generating closed-loop traffic scenarios that are both realistic and controllable is crucial for evaluating autonomous driving systems, especially under rare safety-critical interactions. Existing learning-based methods often struggle to balance controllability and realism, offering either limited fine-grained control over traffic behavior or controllable scenarios at the expense of behavioral plausibility. This paper presents E2E-CDiff, an end-to-end conditional diffusion framework for controllable and realistic scenario generation. Conditioned on front-view visual observations, E2E-CDiff jointly denoises future motion states and executable low-level controls for route-interacting backgr...
| Authors | Jingzheng Li, Yufei Ge, Zhijun Chen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 21 July 2026 |
| Last updated at source | 21 July 2026 |
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