Autonomous driving requires reasoning about interactions with surrounding traffic. A prevailing approach is large-scale imitation learning on expert driving datasets, aimed at generalizing across diverse real-world scenarios. For online trajectory generation, such methods must operate at real-time rates. Diffusion models require hundreds of denoising steps at inference, resulting in high latency. Consistency models mitigate this issue but rely on carefully tuned noise schedules to capture the multimodal action distributions common in autonomous driving. Adapting the schedule, typically requires expensive retraining. To address these limitations, we propose a framework based on conditional flow matching that jointly predicts future motions of surrounding agents and plans the ego trajectory in real time. We train a lightweight variance estimator that selects the number of inference steps online, removing the need for retraining to balance runtime and imitation learning performance. To further enhance ride quality, we introduce a trajectory post-processing step cast as a convex quadratic program, with negligible computational overhead. Trained on the Waymo Open Motion Dataset, the framework performs maneuvers such as lane changes, cruise control, and navigating unprotected left turns without requiring scenario-specific tuning. Our method maintains a 20 Hz update rate on an NVIDIA RTX 3070 GPU, making it suitable for online deployment. Compared to transformer, diffusion, and consistency model baselines, we achieve improved trajectory smoothness and better adherence to dynamic constraints. Experiment videos and code implementations can be found at https://flow-matching-self-driving.github.io/.

翻译：自动驾驶需要推理与周围交通的交互。主流方法是在专家驾驶数据集上进行大规模模仿学习，旨在泛化至多样化的现实场景。对于在线轨迹生成，此类方法必须以实时速率运行。扩散模型在推理时需要数百步去噪，导致高延迟。一致性模型缓解了此问题，但依赖于精心调校的噪声调度来捕捉自动驾驶中常见的多模态动作分布。调整调度通常需要昂贵的重新训练。为应对这些限制，我们提出了一个基于条件流匹配的框架，可实时联合预测周围智能体的未来运动并规划自车轨迹。我们训练了一个轻量级方差估计器，在线选择推理步数，无需重新训练即可平衡运行时间与模仿学习性能。为进一步提升乘坐质量，我们引入了一个轨迹后处理步骤，将其表述为凸二次规划问题，计算开销可忽略不计。在Waymo Open Motion Dataset上训练的框架能够执行变道、巡航控制和无保护左转等操作，无需针对特定场景进行调优。我们的方法在NVIDIA RTX 3070 GPU上保持20 Hz的更新速率，适合在线部署。与Transformer、扩散模型和一致性模型基线相比，我们的方法实现了更高的轨迹平滑度，并更好地遵循动态约束。实验视频和代码实现可在https://flow-matching-self-driving.github.io/查看。