Geo-VLA: Geometry-Aware Vision-Language-Action Planning via Internalization of Map Semantics
Vision-language-action (VLA) models have advanced end-to-end autonomous driving by leveraging foundation models for semantic reasoning and long-tail generalization. However, their planning performance remains limited in complex driving environments because image-only representations inadequately capture planning-relevant road geometry and topology. In this paper, we propose Geo-VLA, a plug-and-play framework that enhances VLA models by learning geometry-aware visual representations. During training, Geo-VLA internalizes geometric map semantics to strengthen road-structure representations, while requiring no HD maps or additional lane information during inference. To support this approach,...
| Authors | Ran Chen, Jiaxing Ren, Zhikun Zhang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 18 August 2026 |
| Last updated at source | 18 August 2026 |
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