解决驾驶数据长尾分布问题，提升罕见场景规划能力
FlowDrive: moderated flow matching with data balancing for trajectory planning
- 用轨迹模式重加权平衡数据，改进训练样本分布
- 基于流匹配快速生成多样化且符合场景的轨迹
- 引入动态调制引导，适合高风险场景规划研究者
基于学习的规划器对驾驶数据的长尾分布敏感：常见行为占主导，而危险或罕见场景数据稀少，导致模型偏向高频案例，降低关键场景表现。我们比较了多种采样平衡策略，发现按轨迹模式重加权有效。在此基础上提出FlowDrive，一种基于流匹配的轨迹规划方法，通过少量流匹配步骤直接从噪声映射到轨迹分布。进一步引入在环调制引导，在流步之间注入小扰动，系统性提升轨迹多样性同时保持场景一致性。在nuPlan和交互聚焦的interPlan基准上，FlowDrive在学习型规划器中达到领先水平，结合调制引导与轻量后处理（FlowDrive*）后，几乎在所有测试集上达到当前最优性能。
原文摘要 · Abstract (English)
Learning-based planners are sensitive to the long-tailed distribution of driving data. Common maneuvers dominate datasets, while dangerous or rare scenarios are sparse. This imbalance can bias models toward the frequent cases and degrade performance on critical scenarios. To tackle this problem, we compare balancing strategies for sampling training data and find reweighting by trajectory pattern an effective approach. We then present FlowDrive, a flow-matching trajectory planner that learns a conditional rectified flow to map noise directly to trajectory distributions with few flow-matching steps. We further introduce moderated, in-the-loop guidance that injects small perturbation between flow steps to systematically increase trajectory diversity while remaining scene-consistent. On nuPlan and the interaction-focused interPlan benchmarks, FlowDrive achieves state-of-the-art results among learning-based planners and approaches methods with rule-based refinements. After adding moderated guidance and light post-processing (FlowDrive*), it achieves overall state-of-the-art performance across nearly all benchmark splits. Our code is available at https://github.com/einsteinguang/flow_drive_planner.
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