arXiv 2604.126562026-04-30cs.ROcs.LG

FeaXDrive: 为端到端自动驾驶的可行性感知轨迹导向扩散规划

FeaXDrive: Feasibility-aware Trajectory-Centric Diffusion Planning for End-to-End Autonomous Driving

College of Automotive and Energy Engineering, Tongji University（同济大学汽车与能源工程学院）；College of Transportation Engineering, Tongji University（同济大学交通工程学院）；School of Mechanical and Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）

Baoyun Wang, Zhuoren Li, Ran Yu, Yu Che, Xinrui Zhang, Ming Liu, Jia Hu, Chen Lv, Bo Leng

备注：: 22 pages, 6 figures

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AI总结：

FeaXDrive通过轨迹导向的扩散规划方法，提升自动驾驶中轨迹空间的可行性建模，改进轨迹几何和运动学可行性，增强可行驶区域一致性，实验表明其在NAVSIM基准上表现优异。

摘要：

端到端扩散规划在自动驾驶中展现出强大潜力，但生成轨迹的物理可行性仍未充分解决。本文提出FeaXDrive，一种面向端到端自动驾驶的可行性感知轨迹导向扩散规划方法。核心思想是将干净轨迹作为统一对象，贯穿扩散过程进行可行性感知建模。基于此轨迹导向的公式，FeaXDrive集成了自适应曲率约束训练以提升内在几何和运动学可行性，可行驶区域引导以增强与可行驶区域的一致性，并通过可行性感知GRPO后训练进一步提升规划性能，同时平衡轨迹空间的可行性。在NAVSIM基准上的实验表明，FeaXDrive在闭环规划性能方面表现强劲，同时显著提升了轨迹空间的可行性。这些发现突显了在端到端扩散规划中显式建模轨迹空间可行性的的重要性，并为更可靠和物理基础的自动驾驶规划器提供了新方向。

英文摘要：

End-to-end diffusion planning has shown strong potential for autonomous driving, but the physical feasibility of generated trajectories remains insufficiently addressed. In particular, generated trajectories may exhibit local geometric irregularities, violate trajectory-level kinematic constraints, or deviate from the drivable area, indicating that the commonly used noise-centric formulation in diffusion planning is not yet well aligned with the trajectory space where feasibility is more naturally characterized. To address this issue, we propose FeaXDrive, a feasibility-aware trajectory-centric diffusion planning method for end-to-end autonomous driving. The core idea is to treat the clean trajectory as the unified object for feasibility-aware modeling throughout the diffusion process. Built on this trajectory-centric formulation, FeaXDrive integrates adaptive curvature-constrained training to improve intrinsic geometric and kinematic feasibility, drivable-area guidance within reverse diffusion sampling to enhance consistency with the drivable area, and feasibility-aware GRPO post-training to further improve planning performance while balancing trajectory-space feasibility. Experiments on the NAVSIM benchmark show that FeaXDrive achieves strong closed-loop planning performance while substantially improving trajectory-space feasibility. These findings highlight the importance of explicitly modeling trajectory-space feasibility in end-to-end diffusion planning and provide a step toward more reliable and physically grounded autonomous driving planners.

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