AnchorVLA: Bridging Discrete Decisions and Continuous Trajectories for Vision-Language-Action Planning
Autonomous driving planning requires translating navigation intent, traffic rules, dynamic interactions, and language instructions into executable continuous trajectories. Vision-Language-Action models have been introduced into driving planning to improve long-tail generalization, commonsense reasoning, high-level semantic understanding, and explainability. However, existing VLA planners mainly follow planning-head-based trajectory prediction or full-trajectory autoregressive generation. The former only weakly constrains continuous trajectory generation with VLA reasoning, while the latter relies on long sequences of low-information-density coordinate tokens, making semantic-action alignm...
| Authors | Qi Liu, Yabei Li, Hongsong Wang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 3 July 2026 |
| Last updated at source | 3 July 2026 |
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