DA-WAM是专门给自动驾驶做决策优化的框架，用它模拟未来场景选路径，比以前方法在NAVSIM那些测试里效果好，你感兴趣可以去了解下。
DA-WAM是一种针对自动驾驶场景的决策优化框架，通过统一预测与动作条件未来建模来提升决策质量；该框架在NAVSIM-v1和NAVSIM-v2基准上实现领先表现，用行动条件预测生成不同轨迹的未来潜在状态；未来潜在条件化因子化评分器评估各候选轨迹，保障安全边界下的规划效果。
DA-WAM: Decision-Aligned Future Latents for Driving World Models
Anticipating how scenes evolve under ego actions is fundamental to safe autonomous driving, yet the full potential of world models for decision-making remains unrealized. The critical challenge lies in ensuring that future modeling is not merely predictive, but decision-informative: the predicted future must directly shape which trajectory is selected. Existing approaches decouple future representation learning from planning optimization, or share predicted states across trajectory candidates, thereby diluting the action-specific consequences that ought to guide selection. To bridge this gap, we propose DA-WAM, a framework that unifies predictive representation learning, action-conditioned future modeling, and trajectory scoring under a single decision-making objective. DA-WAM maintains predictive supervision throughout planner optimization via an online encoder and a stable momentum target, allowing future representations to co-evolve with the driving task. An action-conditioned predictor generates a distinct future latent state per trajectory candidate, which is then evaluated by a future-latent-conditioned factorized scorer. For the expert-matched trajectory, the predicted future latent is supervised by the observed future representation, while safety-critical hard negatives provide additional supervision near planning boundaries. Extensive experiments on NAVSIM-v1 and NAVSIM-v2 demonstrate state-of-the-art performance, while ablations and diagnostic analyses validate the key components.