DA-WAM: Decision-Aligned Future Latents for Driving World Models

2026-08-19

AI Summary

为解决自动驾驶中场景预测与决策分离的问题，提出DA-WAM框架，通过统一表示学习、行动条件未来建模和轨迹评分来直接指导决策。

Abstract

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.

Problem

• autonomous driving
• world models
• decision-making
• future modeling
• trajectory selection

Innovation

• Decision-Aligned
• Action-Conditioned
• Future Latents
• Trajectory Scoring
• Predictive Supervision