HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Driving

• URL: http://arxiv.org/abs/2602.13329v1
• Date: Wed, 11 Feb 2026 07:08:33 GMT
• Title: HiST-VLA: A Hierarchical Spatio-Temporal Vision-Language-Action Model for End-to-End Autonomous Driving
• Authors: Yiru Wang, Zichong Gu, Yu Gao, Anqing Jiang, Zhigang Sun, Shuo Wang, Yuwen Heng, Hao Sun,
• Abstract summary: Vision-Language-Action (VLA) models offer promising capabilities for autonomous driving through multimodal understanding.<n>Their utilization in safety-critical scenarios is constrained by inherent limitations, including numerical reasoning, weak 3D spatial awareness, and high sensitivity to context.<n>We propose HiST-VLA, a novel Hierarchical Spatio-Temporal VLA model designed for reliable trajectory generation.
• Score: 20.266736153749417
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language-Action (VLA) models offer promising capabilities for autonomous driving through multimodal understanding. However, their utilization in safety-critical scenarios is constrained by inherent limitations, including imprecise numerical reasoning, weak 3D spatial awareness, and high sensitivity to context. To address these challenges, we propose HiST-VLA, a novel Hierarchical Spatio-Temporal VLA model designed for reliable trajectory generation. Our framework enhances 3D spatial and temporal reasoning by integrating geometric awareness with fine-grained driving commands and state history prompting. To ensure computational efficiency, we integrate dynamic token sparsification into the VLA architecture. This approach fuses redundant tokens rather than filtering them, effectively reducing redundancy without sacrificing model performance. Furthermore, we employ a hierarchical transformer-based planner to progressively refine coarse VLA waypoints into fine-grained trajectories. Crucially, the planner utilizes dynamic latent regularization to incorporate language commands, ensuring strict spatial grounding and temporal coherence. Extensive evaluation on the NAVSIM v2 benchmark demonstrates state-of-the-art performance on Navtest, achieving an EPDMS of 88.6, and EPDMS of 50.9 on pseudo closed-loop Navhard benchmark.

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