Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
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| _version_ | 1866918485833023488 |
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| author | Gong, Zimu Zhang, Brian Zhaoning Zhang, Chris Wong, Kelvin Urtasun, Raquel |
| author_facet | Gong, Zimu Zhang, Brian Zhaoning Zhang, Chris Wong, Kelvin Urtasun, Raquel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_04366 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation Gong, Zimu Zhang, Brian Zhaoning Zhang, Chris Wong, Kelvin Urtasun, Raquel Robotics Machine Learning 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. |
| title | Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation |
| topic | Robotics Machine Learning |
| url | https://arxiv.org/abs/2605.04366 |