Unifying Language-Action Understanding and Generation for Autonomous Driving

• URL: http://arxiv.org/abs/2603.01441v1
• Date: Mon, 02 Mar 2026 04:41:10 GMT
• Title: Unifying Language-Action Understanding and Generation for Autonomous Driving
• Authors: Xinyang Wang, Qian Liu, Wenjie Ding, Zhao Yang, Wei Li, Chang Liu, Bailin Li, Kun Zhan, Xianpeng Lang, Wei Chen,
• Abstract summary: Vision-Language-Action (VLA) models are emerging as a promising paradigm for end-to-end autonomous driving.<n>Existing methods suffer from two critical limitations: a persistent misalignment between language instructions and action outputs, and the inherent inefficiency of typical auto-regressive action generation.<n>We introduce LinkVLA, a novel architecture that directly addresses these challenges to enhance both alignment and efficiency.
• Score: 25.23561391638388
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Vision-Language-Action (VLA) models are emerging as a promising paradigm for end-to-end autonomous driving, valued for their potential to leverage world knowledge and reason about complex driving scenes. However, existing methods suffer from two critical limitations: a persistent misalignment between language instructions and action outputs, and the inherent inefficiency of typical auto-regressive action generation. In this paper, we introduce LinkVLA, a novel architecture that directly addresses these challenges to enhance both alignment and efficiency. First, we establish a structural link by unifying language and action tokens into a shared discrete codebook, processed within a single multi-modal model. This structurally enforces cross-modal consistency from the ground up. Second, to create a deep semantic link, we introduce an auxiliary action understanding objective that trains the model to generate descriptive captions from trajectories, fostering a bidirectional language-action mapping. Finally, we replace the slow, step-by-step generation with a two-step coarse-to-fine generation method C2F that efficiently decodes the action sequence, saving 86% inference time. Experiments on closed-loop driving benchmarks show consistent gains in instruction following accuracy and driving performance, alongside reduced inference latency.

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