Core Insights - The article discusses the development of a new planning framework called GuideFlow, which addresses the challenges of trajectory generation in end-to-end autonomous driving by incorporating explicit constraints and enhancing model optimization capabilities [3][11][49] - GuideFlow integrates various conditional signals to guide the generation process, improving the robustness and safety of autonomous driving systems [11][49] Summary by Sections Background Review - End-to-end autonomous driving (E2E-AD) has emerged as an attractive alternative to traditional modular approaches, allowing for unified training through data [9] - Recent advancements have shifted from single-modal to multi-modal trajectory generation to better reflect inherent uncertainties in real driving scenarios [9][10] GuideFlow Framework - GuideFlow explicitly models the flow matching process to alleviate mode collapse issues and flexibly integrates multiple guiding signals [3][11] - The framework combines flow matching with Energy-Based Model (EBM) training to enhance the model's ability to meet physical constraints [3][11] Experimental Results - GuideFlow demonstrated superior performance on various benchmark datasets, achieving state-of-the-art (SOTA) results, particularly on the challenging NavSim dataset with an Extended PMD Score (EPDMS) of 43.0 [3][34][37] - The framework's collision rate was notably low, with an average of 0.07% on the NuScenes dataset, showcasing its safety capabilities [40][41] Contributions and Innovations - The article highlights three core strategies within GuideFlow: speed field constraints, flow state constraints, and EBM flow optimization, which collectively enhance trajectory feasibility and safety [11][28][31] - The integration of driving aggressiveness scoring allows for dynamic adjustments in trajectory styles during inference, further refining the model's adaptability [33][49] Conclusion - GuideFlow represents a significant advancement in trajectory planning for autonomous driving, effectively embedding safety constraints into the generation process and demonstrating robust performance across various datasets [49]

点击下方 卡片 ，关注" 自动驾驶之心 "公众号 戳我-> 领取 自动驾驶近30个 方向 学习 路线 >>自动驾驶前沿信息获取 → 自动驾驶之心知识星球 论文作者 | Lin Liu等 编辑 | 自动驾驶之心 今年学术界和工业界很大的精力都投入在Action的建模上，也就是自车轨迹的输出。先前的MLP只能输出单模 的轨迹，实际使用中无法满足下游不确定性的需求。所以从去年开始，我们看到了生成式的很多算法问世。 经过这一年的发展，生成式的算法进一步收敛到Diffusion和Flow matching两个方向上。 自动驾驶之心了解到 上半年有不少公司都在尝试将这两种方法落地量产，期间坎坷无需多言。 今天为大家分享的是一篇北交&轻舟智航等团队最新的工作，提出一种基于Constrained Flow Matching的新型规 划框架 GuideFlow ，整体效果还不错。 具体而言，GuideFlow显式建模流匹配过程，该过程本质上可缓解模态坍塌的问题，并能灵活融合多种条件信 号的引导。本文的核心贡献在于， 将显式约束直接嵌入流匹配生成过程 ，而非依赖隐式约束编码。关键创新 点在于， GuideFlow将流匹配与Ene ...