# SUV: Future Scene Understanding as Video Generation for End-to-End Driving

arXiv: https://arxiv.org/abs/2608.03084 · HTML: https://mlsanity.com/paper/2608.03084/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Generative simulation for driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 41 of its 52 references that are in the map.
- First posted on arXiv 4 Aug 2026.

## Placed in

- End-to-end autonomous driving / [Generative simulation for driving](https://mlsanity.com/driving/generative-simulation-for-driving/)

## Builds on (most cited first, 30 of 41)

- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [VAD: Vectorized Scene Representation for Efficient Autonomous Driving](https://mlsanity.com/paper/2303.12077/) (2023)
- [HOTA: A Higher Order Metric for Evaluating Multi-Object Tracking](https://mlsanity.com/paper/2009.07736/) (2020)
- [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)
- [Vista: A Generalizable Driving World Model with High Fidelity and Versatile Controllability](https://mlsanity.com/paper/2405.17398/) (2024)
- [Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Driving](https://mlsanity.com/paper/2311.17918/) (2023)
- [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)
- [Motus: A Unified Latent Action World Model](https://mlsanity.com/paper/2512.13030/) (2025)
- [SparseDrive: End-to-End Autonomous Driving via Sparse Scene Representation](https://mlsanity.com/paper/2405.19620/) (2024)
- [EMMA: End-to-End Multimodal Model for Autonomous Driving](https://mlsanity.com/paper/2410.23262/) (2024)
- [Enhancing End-to-End Autonomous Driving with Latent World Model](https://mlsanity.com/paper/2406.08481/) (2024)
- [Mixture-of-Transformers: A Sparse and Scalable Architecture for Multi-Modal Foundation Models](https://mlsanity.com/paper/2411.04996/) (2024)
- [OmniDrive: A Holistic Vision-Language Dataset for Autonomous Driving with Counterfactual Reasoning](https://mlsanity.com/paper/2504.04348/) (2025)
- [DriveVLA-W0: World Models Amplify Data Scaling Law in Autonomous Driving](https://mlsanity.com/paper/2510.12796/) (2025)
- [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)
- [WOD-E2E: Waymo Open Dataset for End-to-End Driving in Challenging Long-tail Scenarios](https://mlsanity.com/paper/2510.26125/) (2025)
- [Doe-1: Closed-Loop Autonomous Driving with Large World Model](https://mlsanity.com/paper/2412.09627/) (2024)
- [DriveLaW:Unifying Planning and Video Generation in a Latent Driving World](https://mlsanity.com/paper/2512.23421/) (2025)
- [World Model for Robot Learning: A Comprehensive Survey](https://mlsanity.com/paper/2605.00080/) (2026)
- [Image Generators are Generalist Vision Learners](https://mlsanity.com/paper/2604.20329/) (2026)
- [SGDrive: Scene-to-Goal Hierarchical World Cognition for Autonomous Driving](https://mlsanity.com/paper/2601.05640/) (2026)
- [DriveWorld-VLA: Unified Latent-Space World Modeling with Vision-Language-Action for Autonomous Driving](https://mlsanity.com/paper/2602.06521/) (2026)
- [From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction](https://mlsanity.com/paper/2510.19654/) (2025)
- [IRL-VLA: Training an Vision-Language-Action Policy via Reward World Model](https://mlsanity.com/paper/2508.06571/) (2025)
- [Drive-JEPA: Video JEPA Meets Multimodal Trajectory Distillation for End-to-End Driving](https://mlsanity.com/paper/2601.22032/) (2026)
- [ImagiDrive: A Unified Imagination-and-Planning Framework for Autonomous Driving](https://mlsanity.com/paper/2508.11428/) (2025)
- [UniFuture: A 4D Driving World Model for Future Generation and Perception](https://mlsanity.com/paper/2503.13587/) (2025)

## Built on by (most cited first, 2 of 2)

- [XGenAct: Geometry-Enhanced World Action Models through Cross-Task Generation](https://mlsanity.com/paper/2610.03516/) (2026)
- [VITAL: From Visual Motion to Precise Action](https://mlsanity.com/paper/2610.20604/) (2026)

## Abstract (arXiv)

End-to-end driving requires a coherent understanding of future scenes, yet existing methods model these scenes using task-specific heads and output formats, with limited scalability. Can video generation instead provide a shared predictor? We introduce SUV, a unified end-to-end driving framework that casts future Scene Understanding as Video generation using a pretrained video foundation model. SUV models future appearance, semantics, relative depth, and instance-level dynamics as video streams with a shared video expert, without stream-specific visual prediction heads. Through joint video-action attention, the action expert attends to the latent representations of all future streams and generates the ego trajectory. Experiments show that SUV directly predicts all four future streams, while controlled ablations show that structured future supervision and direct future-stream access yield higher trajectory planning scores. With only a single front camera and no candidate-trajectory selection, SUV outperforms a broad set of recent state-of-the-art methods on both NAVSIM-v2 splits, achieving 91.0 EPDMS on navtest and 36.9 on navhard. On the long-tail WOD-E2E benchmark, SUV achieves a competitive RFS of 7.94.