| title | SimWAM: A Simple World Action Model for End-to-End Autonomous Driving |
|---|---|
| authors | Zongchuang Zhao, Xin Zhou, Tianyang Xu, Zhengyang Sun, Kaixuan Zhou, Honglin Li, Dingkang Liang, Xiang Bai |
| date | 2026-08-07 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.07468 |
| affiliation | H-EmbodVis |
| upvotes | 27 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | 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/ |
cite
@misc{zhao2026simwam,
title={SimWAM: A Simple World Action Model for End-to-End Autonomous Driving},
author={Zongchuang Zhao and Xin Zhou and Tianyang Xu and Zhengyang Sun and Kaixuan Zhou and Honglin Li and Dingkang Liang and Xiang Bai},
year={2026},
eprint={2608.07468},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.07468}
}