Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation

• URL: http://arxiv.org/abs/2605.04366v1
• Date: Wed, 06 May 2026 00:08:25 GMT
• Title: Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
• Abstract summary: We introduce a conditional latent flow matching approach for scalable and realistic safety-critical scenario generation.<n>Our method uses distribution matching to transform nominal scenes into safety-critical rollouts.
• Score: 22.114606251423925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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