SUV: Future Scene Understanding as Video Generation for End-to-End Driving
Provides a unified, scalable approach for end-to-end driving by leveraging video generation, achieving strong results with a single camera and no candidate-trajectory selection.
SUV casts future scene understanding as video generation using a pretrained video foundation model, predicting appearance, semantics, depth, and instance dynamics as video streams with a shared video expert. It outperforms recent state-of-the-art methods on NAVSIM-v2 (91.0 EPDMS on navtest, 36.9 on navhard) and achieves competitive RFS 7.94 on WOD-E2E.
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.