Generative models have shown great potential in trajectory planning. Recent studies demonstrate that anchor-guided generative models are effective in modeling the uncertainty of driving behaviors and improving overall performance. However, these methods rely on discrete anchor vocabularies that must sufficiently cover the trajectory distribution during testing to ensure robustness, inducing an inherent trade-off between vocabulary size and model performance. To overcome this limitation, we propose MeanFuser, an end-to-end autonomous driving method that enhances both efficiency and robustness through three key designs. (1) We introduce Gaussian Mixture Noise (GMN) to guide generative sampling, enabling a continuous representation of the trajectory space and eliminating the dependency on discrete anchor vocabularies. (2) We adapt ``MeanFlow Identity" to end-to-end planning, which models the mean velocity field between GMN and trajectory distribution instead of the instantaneous velocity field used in vanilla flow matching methods, effectively eliminating numerical errors from ODE solvers and significantly accelerating inference. (3) We design a lightweight Adaptive Reconstruction Module (ARM) that enables the model to implicitly select from all sampled proposals or reconstruct a new trajectory when none is satisfactory via attention weights. Experiments on the NAVSIM closed-loop benchmark demonstrate that MeanFuser achieves outstanding performance without the supervision of the PDM Score. and exceptional inference efficiency, offering a robust and efficient solution for end-to-end autonomous driving. Our code and model are available at https://github.com/wjl2244/MeanFuser.

翻译：生成模型在轨迹规划中展现出巨大潜力。近期研究表明，锚点引导的生成模型能有效建模驾驶行为的不确定性并提升整体性能。然而，这些方法依赖于离散的锚点词汇表，其必须在测试阶段充分覆盖轨迹分布以确保鲁棒性，这导致了词汇表规模与模型性能之间的固有权衡。为克服此限制，我们提出MeanFuser——一种通过三项关键设计同时提升效率与鲁棒性的端到端自动驾驶方法。（1）我们引入高斯混合噪声（GMN）来引导生成采样，实现对轨迹空间的连续表征，并消除对离散锚点词汇表的依赖。（2）我们将“MeanFlow恒等式”适配于端到端规划，该方法建模GMN与轨迹分布之间的平均速度场，而非传统流匹配方法中使用的瞬时速度场，从而有效消除ODE求解器带来的数值误差，并显著加速推理过程。（3）我们设计了一个轻量级自适应重建模块（ARM），使模型能够通过注意力权重隐式地从所有采样候选轨迹中选择，或在无满意候选时重建一条新轨迹。在NAVSIM闭环基准测试上的实验表明，MeanFuser在不依赖PDM Score监督的情况下取得了卓越性能，并具备优异的推理效率，为端到端自动驾驶提供了一个鲁棒且高效的解决方案。我们的代码与模型已发布于https://github.com/wjl2244/MeanFuser。