# DriveFuture: Future-Aware Latent World Models for Autonomous Driving

arXiv: https://arxiv.org/abs/2605.09701 · HTML: https://mlsanity.com/paper/2605.09701/

## Key facts

- Cited by 17 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 61 of its 66 references that are in the map.
- First posted on arXiv 10 May 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 61)

- [Planning-oriented Autonomous Driving](https://mlsanity.com/paper/2212.10156/) (2022)
- [GAIA-1: A Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2309.17080/) (2023)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles](https://mlsanity.com/paper/2106.11810/) (2021)
- [TransFuser: Imitation with Transformer-Based Sensor Fusion for Autonomous Driving](https://mlsanity.com/paper/2205.15997/) (2022)
- [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://mlsanity.com/paper/2405.17398/) (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)
- [Hydra-MDP: End-to-end Multimodal Planning with Multi-target Hydra-Distillation](https://mlsanity.com/paper/2406.06978/) (2024)
- [SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation](https://mlsanity.com/paper/2405.19620/) (2024)
- [OccWorld: Learning a 3D Occupancy World Model for Autonomous Driving](https://mlsanity.com/paper/2311.16038/) (2023)
- [GenAD: Generative End-to-End Autonomous Driving](https://mlsanity.com/paper/2402.11502/) (2024)
- [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)
- [Senna: Bridging Large Vision-Language Models and End-to-End Autonomous Driving](https://mlsanity.com/paper/2410.22313/) (2024)
- [End-to-End Driving with Online Trajectory Evaluation via BEV World Model](https://mlsanity.com/paper/2504.01941/) (2025)
- [Pseudo-Simulation for Autonomous Driving](https://mlsanity.com/paper/2506.04218/) (2025)
- [DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving](https://mlsanity.com/paper/2510.12796/) (2025)
- [GoalFlow: Goal-Driven Flow Matching for Multimodal Trajectories Generation in End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.05689/) (2025)
- [Epona: Autoregressive Diffusion World Model for Autonomous Driving](https://mlsanity.com/paper/2506.24113/) (2025)
- [ADriver-I: A General World Model for Autonomous Driving](https://mlsanity.com/paper/2311.13549/) (2023)
- [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)
- [DRAMA: An Efficient End-to-end Motion Planner for Autonomous Driving with Mamba](https://mlsanity.com/paper/2408.03601/) (2024)
- [Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving](https://mlsanity.com/paper/2503.03125/) (2025)
- [OccLLaMA: An Occupancy-Language-Action Generative World Model for Autonomous Driving](https://mlsanity.com/paper/2409.03272/) (2024)

## Built on by (most cited first, 17 of 17)

- [Test-Time Trajectory Optimization for Autonomous Driving](https://mlsanity.com/paper/2606.07170/) (2026)
- [WA-JEPA: Rethinking the Video JEPA Paradigm for World-Action Modeling in Autonomous Driving](https://mlsanity.com/paper/2608.20974/) (2026)
- [DA-WAM: Decision-Aligned Future Latents for Driving World Models](https://mlsanity.com/paper/2608.19085/) (2026)
- [RISE: Adaptive Imagination for World Action Models](https://mlsanity.com/paper/2608.20430/) (2026)
- [ForgeDrive: Bidirectional Cross-Conditioning for Unified Visual-Action Generation in Autonomous Driving](https://mlsanity.com/paper/2606.31226/) (2026)
- [MM-Future: Multi-Mode Joint World-Action Modeling for Autonomous Driving](https://mlsanity.com/paper/2609.20377/) (2026)
- [Sometimes You Gotta Run Before You Can Walk: Run-then-Walk Scheduling Strategy for VLM Autonomous Driving](https://mlsanity.com/paper/2609.25831/) (2026)
- [Relationally Grounded Latent World Models for Autonomous Driving](https://mlsanity.com/paper/2609.24626/) (2026)
- [FocusDrive: Reasoning with Visual Focus for Autonomous Driving](https://mlsanity.com/paper/2609.33190/) (2026)
- [MomWorld: Momentum-Aware Latent World Model for Long-Horizon Autonomous Driving](https://mlsanity.com/paper/2609.33737/) (2026)
- [ReDrive: Shaping Representations with World Modeling for End-to-End Driving](https://mlsanity.com/paper/2609.33854/) (2026)
- [World4Scorer: Outcome-Grounded World Modeling for Autonomous Driving](https://mlsanity.com/paper/2609.36438/) (2026)
- [PhysWAM: Physically Consistent World Action Model for Autonomous Driving](https://mlsanity.com/paper/2609.37970/) (2026)
- [AffordDrive3D: Affordance-Aware World-Action Modeling with Spatial Understanding](https://mlsanity.com/paper/2610.11060/) (2026)
- [PlanWAM: Planning-Shaped Future Representations for End-to-End Autonomous Driving](https://mlsanity.com/paper/2610.11382/) (2026)
- [MORE: Learning Motion-Oriented Registers for Driving World Models and Planning](https://mlsanity.com/paper/2610.14871/) (2026)
- [DriveWLA: Unifying Future Dynamics and Scene Semantics through World-Language-Action Modeling for Autonomous Driving](https://mlsanity.com/paper/2610.17903/) (2026)

## Abstract (arXiv)

Existing latent world models for autonomous driving have opened a promising path toward future-aware driving intelligence. However, they typically treat future latent states as prediction targets or auxiliary signals, rather than directly conditioning trajectory planning. This can entangle current and future features in latent space. In this work, we propose DriveFuture, a future-aware latent world modeling framework for autonomous driving that explicitly learns planning-oriented foresight by conditioning the current latent state modeling process on future world states. Specifically, during training, the model first predicts future latent world states from the current latent state and ego action, and then refines the prediction against the ground-truth future latent state via cross-attention. The resulting future-aware latent serves as an explicit condition for a diffusion-based trajectory planner. During inference, DriveFuture conditions on the predicted future latent state instead of the ground-truth future state. DriveFuture achieves SOTA performance on the public NAVSIM benchmarks, reaching 55.5 EPDMS on NAVSIM-v2 textcolorbluetextitnavhard, 89.9 EPDMS on NAVSIM-v2 textcolorbluetextitnavtest, and 90.7 PDMS on NAVSIM-v1 textcolorbluetextitnavtest, respectively. These results suggest that the key to latent world modeling lies not merely in simulating future states, but more importantly in conditioning current decision-making on future states. Notably, as of April 2026, DriveFuture ranks 1st on the hrefhttps://huggingface.co/spaces/AGC2025/e2e-driving-navhardNAVSIM-v2 textcolorbluetextitnavhard leaderboard and achieves SOTA performance on hrefhttps://huggingface.co/spaces/AGC2024-P/e2e-driving-navtestNAVSIM-v1 textcolorbluetextitnavtest.