发表机构
Cross-Domain Computing Solutions, Bosch（博世跨域计算解决方案）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
FeasibleFlow提出单步端到端生成框架，联合传输配置可行性与多模态轨迹，通过非对称联合MeanFlow和锚定相对排序器及Pareto-ReinFlow平衡安全与进度，在NAVSIM基准上验证了有效性。
AI 中文摘要
端到端自动驾驶直接将当前观测映射到未来轨迹，然而这些轨迹必须随着场景演变而保持有效。未来状态建模旨在解决这种时间错配问题，但通用表示通常包含与自车规划无关的信息，并且仅通过辅助监督、静态条件或提议评估来影响轨迹生成。我们提出FeasibleFlow，一个单步端到端生成框架，联合传输配置空间可行性场和多模态自车轨迹。我们的非对称联合MeanFlow利用MeanFlow恒等式中的路径雅可比向量积，将场演变纳入轨迹传输。由于安全反馈比进度反馈更稀疏，我们进一步引入锚定相对排序器（ARR）和Pareto-ReinFlow，分别在候选选择和生成中平衡安全与进度。在NAVSIM基准上的实验展示了FeasibleFlow的强劲性能，并验证了可行性与轨迹的联合传输以及所提出的安全优先机制。
英文摘要
End-to-end autonomous driving maps current observations directly to future trajectories, yet those trajectories must remain valid as the scene evolves. Future state modeling aims to address this temporal mismatch, but general representations often contain information unrelated to ego planning and affect trajectory generation only through auxiliary supervision, static conditioning, or proposal evaluation. We propose FeasibleFlow, a one-step end-to-end generative framework that jointly transports a configuration-space feasibility field and multimodal ego trajectories. Our Asymmetric Joint MeanFlow uses the pathwise Jacobian-vector product in the MeanFlow identity to incorporate field evolution into trajectory transport. Because safety feedback is sparser than progress feedback, we further introduce the Anchor-relative ranker (ARR) and Pareto-ReinFlow to balance safety and progress in candidate selection and generation, respectively. Experiments on the NAVSIM benchmark demonstrate the strong performance of FeasibleFlow and validate both the joint transport of feasibility and trajectories and the proposed safety-first mechanisms.
