Scene-Adaptive VLA: Efficient Autonomous Driving via Dynamic Layer Routing
Vision-Language-Action (VLA) models offer a promising paradigm for autonomous driving. However, the massive parameter scale of VLAs strains onboard computational resources during real-time inference. This directly increases energy consumption and limits vehicle battery range, making it critical to minimize computational overhead without compromising driving capability and explainability. Current research on efficient VLA-based driving focuses on sequential adaptation (\textit{e.g.}, Chain-of-Thought and token adjustment), leaving structural adaptation underexplored despite parameter redundancy across dynamic driving scenes. To address this gap, we propose Scene-Adaptive VLA, an efficient ...
| Authors | Yujia Yang, Quan Yuan, Jiawei Fan et al. |
|---|---|
| Primary category | cs.CV |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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