SparseOccVLA: Bridging Occupancy and Vision-Language Models via Sparse Queries for Unified 4D Scene Understanding and Planning
Chenxu Dang1,2,3*, Jie Wang2, Guang Li2, Zhiwen Hou2, Zihan You3, Hangjun Ye2,
Jie Ma1, Long Chen2†, Yan Wang3†
1Huazhong University of Science and Technology
2Xiaomi EV 3Institute for AI Industry Research (AIR), Tsinghua University
(*) Work done during the internship at Xiaomi EV and AIR. (†) Corresponding authors.
In autonomous driving, Vision Language Models (VLMs) excel at high-level reasoning , whereas semantic occupancy provides fine-grained details. Despite significant progress in individual fields, there is still no method that can effectively integrate both paradigms. Conventional VLMs struggle with token explosion and limited spatiotemporal reasoning, while semantic occupancy provides a unified, explicit spatial representation but is too dense to integrate efficiently with VLMs. To address these challenges and bridge the gap between VLMs and occupancy, we propose SparseOccVLA, a novel vision-language-action model that unifies scene understanding, occupancy forecasting, and trajectory planning powered by sparse occupancy queries. Starting with a lightweight Sparse Occupancy Encoder, SparseOccVLA generates compact yet highly informative sparse occupancy queries that serve as the single bridge between vision and language. These queries are aligned into the language space and reasoned by the LLM for unified scene understanding and future occupancy forecasting. Furthermore, we introduce an LLM-guided Anchor-Diffusion Planner featuring decoupled anchor scoring and denoising, as well as cross-model trajectory-condition fusion. SparseOccVLA achieves a 7% relative improvement in CIDEr over the state-of-the-art on OmniDrive-nuScenes, a 0.5 increase in mIoU score on Occ3D-nuScenes, and sets state-of-the-art open-loop planning metric on nuScenes benchmark, demonstrating its strong holistic capability.
2026/1.13: The paper is released on arXiv.
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If this work is helpful for your research, please consider citing:
@article{dang2026sparseoccvla,
title={SparseOccVLA: Bridging Occupancy and Vision-Language Models via Sparse Queries for Unified 4D Scene Understanding and Planning},
author={Dang, Chenxu and Wang, Jie and Li, Guang and You, Zihan and Ye, Hangjun and Ma, Jie and Chen, Long and Wang, Yan},
journal={arXiv preprint arXiv:2601.06474},
year={2026}
}
@article{dang2025sparseworld,
title={SparseWorld: A Flexible, Adaptive, and Efficient 4D Occupancy World Model Powered by Sparse and Dynamic Queries},
author={Dang, Chenxu and Liu, Haiyan and Bao, Guangjun and An, Pei and Tang, Xinyue and Ma, Jie and Sun, Bingchuan and Wang, Yan},
journal={arXiv preprint arXiv:2510.17482},
year={2025}
}