用压缩的隐空间建模世界状态，实现高效端到端自动驾驶规划。
Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving
- 通过可学习查询将多视角图像压缩为紧凑场景令牌，提升空间感知能力。
- 采用因果Transformer预测未来世界状态，在NAVSIM v2上达89.3 EPDMS新高。
- 模型仅104M参数，训练数据少却超越现有无感知方法，适合资源受限场景。
我们提出Latent-WAM，一种高效的端到端自动驾驶框架，通过空间感知与动态信息丰富的隐空间世界表征实现优异轨迹规划。现有基于世界模型的规划器存在表征压缩不足、空间理解有限、时序动态利用不充分等问题，导致在数据与算力受限条件下规划效果不佳。Latent-WAM引入两个核心模块：空间感知压缩世界编码器（SCWE），从基础模型中提取几何知识，通过可学习查询将多视角图像压缩为紧凑场景令牌；动态隐空间世界模型（DLWM），采用因果Transformer自回归预测未来世界状态，条件于历史视觉与运动表征。在NAV SIM v2和HUG SIM上的大量实验表明，该框架达到新最佳性能：NAV SIM v2上89.3 EPDMS，HUG SIM上28.9 HD-Score，相比最优前序无感知方法提升3.2 EPDMS，且显著减少训练数据需求，模型规模仅为104M参数。
原文摘要 · Abstract (English)
We introduce Latent-WAM, an efficient end-to-end autonomous driving framework that achieves strong trajectory planning through spatially-aware and dynamics-informed latent world representations. Existing world-model-based planners suffer from inadequately compressed representations, limited spatial understanding, and underutilized temporal dynamics, resulting in sub-optimal planning under constrained data and compute budgets. Latent-WAM addresses these limitations with two core modules: a Spatial-Aware Compressive World Encoder (SCWE) that distills geometric knowledge from a foundation model and compresses multi-view images into compact scene tokens via learnable queries, and a Dynamic Latent World Model (DLWM) that employs a causal Transformer to autoregressively predict future world status conditioned on historical visual and motion representations. Extensive experiments on NAVSIM v2 and HUGSIM demonstrate new state-of-the-art results: 89.3 EPDMS on NAVSIM v2 and 28.9 HD-Score on HUGSIM, surpassing the best prior perception-free method by 3.2 EPDMS with significantly less training data and a compact 104M-parameter model.
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