MeanFuser: 一种基于MeanFlow的高效多模态轨迹生成与自适应重构方法用于端到端自动驾驶
MeanFuser: Fast One-Step Multi-Modal Trajectory Generation and Adaptive Reconstruction via MeanFlow for End-to-End Autonomous Driving
AI总结：
本文提出MeanFuser，通过引入Gaussian Mixture Noise、MeanFlow Identity和轻量级ARM模块，实现了高效且鲁棒的多模态轨迹生成与自适应重构，提升了端到端自动驾驶的性能和效率。
摘要：
生成模型在轨迹规划中展现出巨大潜力。最近的研究表明，锚点引导的生成模型在建模驾驶行为的不确定性并提高整体性能方面是有效的。然而，这些方法依赖于离散的锚点词汇表，必须在测试期间充分覆盖轨迹分布以确保鲁棒性，这导致词汇表大小与模型性能之间存在固有的权衡。为克服这一限制，我们提出MeanFuser，一种端到端自动驾驶方法，通过三个关键设计增强了效率和鲁棒性。（1）我们引入高斯混合噪声（GMN）来引导生成采样，使轨迹空间具有连续表示并消除对离散锚点词汇表的依赖。（2）我们适应“MeanFlow Identity”用于端到端规划，该方法建模GMN与轨迹分布之间的均值速度场，而不是 vanilla 流匹配方法中使用的瞬时速度场，有效消除了来自 ODE 求解器的数值误差，并显著加速了推理。（3）我们设计了一个轻量级的自适应重构模块（ARM），使模型能够通过注意力权重隐式地从所有采样提案中选择或在无合适提案时重构新轨迹。在NAVSIM闭环基准测试中，MeanFuser在无需PDM Score监督的情况下实现了卓越的性能，并且具有出色的推理效率，提供了一种稳健且高效的端到端自动驾驶解决方案。我们的代码和模型可在https://github.com/wjl2244/MeanFuser上获得。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
