SimWAM：一种面向端到端自动驾驶的简单世界动作模型

摘要

世界-动作模型（WAMs）通过将视频动态先验迁移到动作预测中，提升了端到端自动驾驶的性能，但现有方法在推理时仍需昂贵的未来帧生成。我们提出SimWAM，一种简单而有效的WAM，将视频生成纯粹用作训练信号。它采用联合流匹配的方式，共同训练一个预训练视频专家和一个轻量级动作专家。隔离注意力掩码使动作预测独立于未来帧，模型训练后即可丢弃视频分支，留下一个可直接预测轨迹的自包含规划器。由于两个专家不共享参数，仅通过统一的注意力接口交互，因此可以在不修改学习目标或推理流程的情况下替换视频主干网络，并独立扩展动作专家。我们进一步应用强化学习来优化超越轨迹模仿的组合式驾驶奖励。我们的SimWAM在NAVSIM上达到91.5 PDMS，以显著更低的延迟超越了最先进的基于WAM的规划器，并实现零样本迁移至nuScenes。这些结果使SimWAM成为一个简单而稳固的基线，能够直接受益于视频生成的进展，实现高效自动驾驶。代码和模型权重可在 https://github.com/H-EmbodVis/SimWAM/ 获取。

English

World-Action Models (WAMs) improve end-to-end autonomous driving by transferring video dynamics priors to action prediction, but existing methods require costly future generation at inference. We present SimWAM, a simple yet effective WAM that uses video generation purely as a training signal. It co-trains a pretrained video expert and a lightweight action expert with joint flow matching. An isolated attention mask keeps action prediction independent of future frames, allowing the video branch to be discarded after training and leaving a self-contained planner that directly predicts trajectories. Since the two experts share no parameters and interact only through a unified attention interface, the video backbone could be replaced and the action expert scaled independently without modifying the learning objective or inference pipeline. We further apply reinforcement learning to optimize a compositional driving reward beyond trajectory imitation. Our SimWAM achieves 91.5 PDMS on NAVSIM, surpasses state-of-the-art WAM-based planners with substantially lower latency, and transfers zero-shot to nuScenes. These results position SimWAM as a simple yet solid baseline that could readily benefit from advances in video generation for efficient autonomous driving. The code and model weights are available at https://github.com/H-EmbodVis/SimWAM/