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.

翻译：基于学习的规划器对驾驶数据的长尾分布敏感。常见驾驶行为在数据集中占据主导，而危险或罕见场景则稀疏。这种不平衡会导致模型偏向频繁情况，在关键场景上性能下降。为解决此问题，我们比较了训练数据采样的平衡策略，发现按轨迹模式重新加权是一种有效方法。我们随后提出FlowDrive，一种流匹配轨迹规划器，通过学习条件修正流，以少量流匹配步骤将噪声直接映射到轨迹分布。我们进一步引入调节式闭环引导机制，通过在流步骤间注入微小扰动，系统性地增加轨迹多样性，同时保持场景一致性。在nuPlan和侧重交互的interPlan基准测试中，FlowDrive在基于学习的规划器中取得了最先进的结果，并接近采用基于规则优化的方法。在加入调节引导和轻量后处理（FlowDrive*）后，该方法在几乎所有基准测试子集上均实现了整体最优性能。我们的代码公开于https://github.com/einsteinguang/flow_drive_planner。