用流模型动态建模驾驶场景演化，提升规划可靠性
DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving
- 基于修正流构建速度场，预测不同动作下的场景变化
- 在nuScenes和NavSim上提升多框架规划性能，无额外计算开销
- 适合需要高可靠性的自动驾驶决策系统研究者
近期，世界模型被引入自动驾驶系统以提升规划可靠性。现有方法通常通过外观生成或确定性回归预测未来状态，难以捕捉轨迹条件下的场景演化，导致行动规划不可靠。为此，我们提出DynFlowDrive，一种基于流的潜在世界模型，利用流式动力学建模不同驾驶动作下的世界状态转移。通过采用修正流形式，模型学习描述场景状态在不同驾驶动作下变化的速度场，实现未来潜在状态的逐步预测。在此基础上，我们进一步提出一种稳定性感知的多模式轨迹选择策略，根据诱导场景转换的稳定性评估候选轨迹。在nuScenes和NavSim基准上的大量实验表明，该方法在多种驾驶框架中均取得一致改进，且不增加额外推理开销。源代码将发布于https://github.com/xiaolul2/DynFlowDrive。
原文摘要 · Abstract (English)
Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically predict future states through appearance generation or deterministic regression, which limits their ability to capture trajectory-conditioned scene evolution and leads to unreliable action planning. To address this, we propose DynFlowDrive, a latent world model that leverages flow-based dynamics to model the transition of world states under different driving actions. By adopting the rectifiedflow formulation, the model learns a velocity field that describes how the scene state changes under different driving actions, enabling progressive prediction of future latent states. Building upon this, we further introduce a stability-aware multi-mode trajectory selection strategy that evaluates candidate trajectories according to the stability of the induced scene transitions. Extensive experiments on the nuScenes and NavSim benchmarks demonstrate consistent improvements across diverse driving frameworks without introducing additional inference overhead. Source code will be abaliable at https://github.com/xiaolul2/DynFlowDrive.
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