# DA-WAM: Decision-Aligned Future Latents for Driving World Models arXiv: https://arxiv.org/abs/2608.19085 ยท HTML: https://mlsanity.com/paper/2608.19085/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Placed in World models for autonomous driving, one of 11 directions in World models. - Builds on 35 of its 35 references that are in the map. - First posted on arXiv 19 Aug 2026. ## Placed in - World models / [World models for autonomous driving](https://mlsanity.com/wm/world-models-for-autonomous-driving/) - End-to-end autonomous driving / [Generative simulation for driving](https://mlsanity.com/driving/generative-simulation-for-driving/) ## Builds on (most cited first, 30 of 35) - [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022) - [V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning](https://mlsanity.com/paper/2506.09985/) (2025) - [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022) - [NAVSIM: Data-Driven Non-Reactive Autonomous Vehicle Simulation and Benchmarking](https://mlsanity.com/paper/2406.15349/) (2024) - [DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving](https://mlsanity.com/paper/2411.15139/) (2024) - [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://mlsanity.com/paper/2311.17918/) (2023) - [VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning](https://mlsanity.com/paper/2402.13243/) (2024) - [AutoVLA: A Vision-Language-Action Model for End-to-End Autonomous Driving with Adaptive Reasoning and Reinforcement Fine-Tuning](https://mlsanity.com/paper/2506.13757/) (2025) - [Parting with Misconceptions about Learning-based Vehicle Motion Planning](https://mlsanity.com/paper/2306.07962/) (2023) - [ReCogDrive: A Reinforced Cognitive Framework for End-to-End Autonomous Driving](https://mlsanity.com/paper/2506.08052/) (2025) - [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024) - [End-to-End Driving with Online Trajectory Evaluation via BEV World Model](https://mlsanity.com/paper/2504.01941/) (2025) - [Unified Vision-Language-Action Model](https://mlsanity.com/paper/2506.19850/) (2025) - [DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving](https://mlsanity.com/paper/2510.12796/) (2025) - [V-JEPA 2.1: Unlocking Dense Features in Video Self-Supervised Learning](https://mlsanity.com/paper/2603.14482/) (2026) - [DrivingGPT: Unifying Driving World Modeling and Planning with Multi-modal Autoregressive Transformers](https://mlsanity.com/paper/2412.18607/) (2024) - [Hydra-MDP++: Advancing End-to-End Driving via Expert-Guided Hydra-Distillation](https://mlsanity.com/paper/2503.12820/) (2025) - [World4Drive: End-to-End Autonomous Driving via Intention-aware Physical Latent World Model](https://mlsanity.com/paper/2507.00603/) (2025) - [DriveSuprim: Towards Precise Trajectory Selection for End-to-End Planning](https://mlsanity.com/paper/2506.06659/) (2025) - [DriveWorld: 4D Pre-trained Scene Understanding via World Models for Autonomous Driving](https://mlsanity.com/paper/2405.04390/) (2024) - [ARTEMIS: Autoregressive End-to-End Trajectory Planning with Mixture of Experts for Autonomous Driving](https://mlsanity.com/paper/2504.19580/) (2025) - [Generalized Trajectory Scoring for End-to-end Multimodal Planning](https://mlsanity.com/paper/2506.06664/) (2025) - [Driving on Registers](https://mlsanity.com/paper/2601.05083/) (2026) - [Centaur: Robust End-to-End Autonomous Driving with Test-Time Training](https://mlsanity.com/paper/2503.11650/) (2025) - [iPad: Iterative Proposal-centric End-to-End Autonomous Driving](https://mlsanity.com/paper/2505.15111/) (2025) - [Latent-WAM: Latent World Action Modeling for End-to-End Autonomous Driving](https://mlsanity.com/paper/2603.24581/) (2026) - [Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving](https://mlsanity.com/paper/2601.22032/) (2026) - [SparseDriveV2: Scoring is All You Need for End-to-End Autonomous Driving](https://mlsanity.com/paper/2603.29163/) (2026) - [DriveX: Omni Scene Modeling for Learning Generalizable World Knowledge in Autonomous Driving](https://mlsanity.com/paper/2505.19239/) (2025) - [DIVER: Reinforced Diffusion Breaks Imitation Bottlenecks in End-to-End Autonomous Driving](https://mlsanity.com/paper/2507.04049/) (2025) ## Built on by (most cited first, 5 of 5) - [ReDrive: Shaping Representations with World Modeling for End-to-End Driving](https://mlsanity.com/paper/2609.33854/) (2026) - [AD-E2E-JEPA: A Joint-Embedding Predictive Architecture For End-to-End Autonomous Driving](https://mlsanity.com/paper/2609.34085/) (2026) - [PhysWAM: Physically Consistent World Action Model for Autonomous Driving](https://mlsanity.com/paper/2609.37970/) (2026) - [FPD-Drive: Future Policy Distillation for End-to-End Autonomous Driving](https://mlsanity.com/paper/2610.13232/) (2026) - [VITAL: From Visual Motion to Precise Action](https://mlsanity.com/paper/2610.20604/) (2026) ## Abstract (arXiv) 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.