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This research introduces E2E-CDiff, an end-to-end conditional diffusion framework that unifies trajectory prediction and control to generate realistic yet controllable traffic scenarios for autonomous driving evaluation. By jointly denoising motion states and executable controls conditioned on visual inputs, the method effectively resolves planning-control mismatches inherent in traditional two-stage pipelines while supporting safety-critical interactions like collision avoidance or seeking behaviors. The approach demonstrates a superior trade-off between behavioral plausibility and fine-grained controllability compared to existing reinforcement learning baselines, offering significant utility for stress-testing autonomous vehicle systems under rare edge cases.
End-to-end Conditional Diffusion for Realistic and Controllable Visual Traffic Scenario Generation
arXiv:2607.18637v1 Announce Type: new Abstract: 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 background vehicles. This unified state-action generation mitigates the planning-control mismatch in conventional two-stage trajectory-then-controller pipelines. Differentiable guidance further regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors, enabling both naturalistic and safety-critical scenario generation. Experiments on Bench2Drive show that E2E-CDiff achieves a favorable controllability-realism trade-off compared with representative reinforcement- and imitation-learning baselines, while its collision-guided variant induces challenging interactions across multiple autonomous driving systems. E2E-CDiff also performs competitively as a learning-based ego planner, demonstrating the generality of end-to-end state-action diffusion.
Abstract: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 background vehicles. This unified state-action generation mitigates the planning-control mismatch in conventional two-stage trajectory-then-controller pipelines. Differentiable guidance further regulates speed, enforces drivable-area compliance, and supports collision-avoidance or collision-seeking behaviors, enabling both naturalistic and safety-critical scenario generation. Experiments on Bench2Drive show that E2E-CDiff achieves a favorable controllability-realism trade-off compared with representative reinforcement- and imitation-learning baselines, while its collision-guided variant induces challenging interactions across multiple autonomous driving systems. E2E-CDiff also performs competitively as a learning-based ego planner, demonstrating the generality of end-to-end state-action diffusion.
Robotics (cs.RO); Machine Learning (cs.LG)
arXiv:2607.18637 [cs.RO]
(or arXiv:2607.18637v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2607.18637 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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