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This paper introduces a conditional Flow-VAE framework for generating realistic, safety-critical traffic scenarios for autonomous vehicle testing. The method uses conditional latent flow matching to transform nominal driving scenes into safety-critical rollouts by matching distributions between real and simulated data. Experiments demonstrate the framework's ability to generate diverse and realistic safety-critical scenarios, improving upon existing manual and adversarial methods.
Generate more realistic and diverse safety-critical autonomous vehicle scenarios by using conditional latent flow matching to bridge the gap between real-world and simulated data.
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.
