用条件流匹配生成真实且多样的自动驾驶安全场景。
Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
- 基于分布匹配将正常场景转为高风险动态
- 结合仿真与实车数据提升生成多样性
- 适合自动驾驶系统训练与评测使用
安全关键场景对自动驾驶车辆开发至关重要，但在真实驾驶数据中极为罕见。虽然仿真可生成此类场景，但人工设计测试案例难以扩展，而对抗优化常导致行为不现实。本文提出一种条件隐变量流匹配方法，实现可扩展且真实的高风险场景生成。该方法通过分布匹配将常规场景转化为安全关键的动态轨迹。此外，实验表明融合仿真与真实数据可高效生成多样、数据驱动的场景。结果验证了本方法在生成新颖、一致且逼真的安全关键场景方面具有优势，是训练与评估自动驾驶系统的重要工具。
原文摘要 · Abstract (English)
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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