UniDriveVLA: Unifying Understanding, Perception, and Action Planning for Autonomous Driving
Vision-Language-Action (VLA) models have recently emerged in autonomous driving, with the promise of leveraging rich world knowledge to improve the cognitive capabilities of driving systems. However, adapting such models for driving tasks currently faces a critical dilemma between spatial perception and semantic reasoning. Consequently, existing VLA systems are forced into suboptimal compromises: directly adopting 2D Vision-Language Models yields limited spatial perception, whereas enhancing them with 3D spatial representations often impairs the native reasoning capacity of VLMs. We argue that this dilemma largely stems from the coupled optimization of spatial perception and semantic reas...
| Authors | Yongkang Li, Lijun Zhou, Sixu Yan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 April 2026 |
| Last updated at source | 2 April 2026 |
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- Mike087/Language2DriveDataset · cited by
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- Mike087/Language2DriveDataset
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