[2605.04366] Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
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Computer Science > Robotics
arXiv:2605.04366 (cs)
[Submitted on 6 May 2026]
Title:Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
Authors:Zimu Gong, Brian Zhaoning Zhang, Chris Zhang, Kelvin Wong, Raquel Urtasun
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Abstract:Safety-critical scenarios are essential for the development of autonomous vehicles (AVs) but are rare in real-world driving data. While simulation offers a way to generate such scenarios, manually designed test cases lack scalability, and adversarial optimization often produces unrealistic behaviors. In this work, we introduce a conditional latent flow matching approach for scalable and realistic safety-critical scenario generation. Our method uses distribution matching to transform nominal scenes into safety-critical rollouts. Furthermore, we demonstrate that incorporating both simulation and real-world data enables our framework to efficiently generate diverse, data-driven scenarios. Experimental results highlight that our approach is able to more consistently and realistically generate novel safety-critical scenarios, making it a valuable tool for training and benchmarking AV systems.
Comments: ICRA 2026
Subjects:
Robotics (cs.RO); Machine Learning (cs.LG)
Cite as: arXiv:2605.04366 [cs.RO]
(or arXiv:2605.04366v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.04366
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Chris Zhang [view email] [v1] Wed, 6 May 2026 00:08:25 UTC (1,165 KB)
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