Spatial-aware Vision Language Model for Autonomous Driving

• URL: http://arxiv.org/abs/2512.24331v1
• Date: Tue, 30 Dec 2025 16:35:00 GMT
• Title: Spatial-aware Vision Language Model for Autonomous Driving
• Authors: Weijie Wei, Zhipeng Luo, Ling Feng, Venice Erin Liong,
• Abstract summary: Vision-Language Models (VLMs) show significant promise for end-to-end autonomous driving by leveraging the common sense embedded in language models.<n>Current image-based methods struggle with accurate metric spatial reasoning and geometric inference, leading to unreliable driving policies.<n>We propose LVLDrive, a novel framework specifically designed to upgrade existing VLMs with robust 3D metric spatial understanding for autonomous driving.
• Score: 16.149511148218497
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: While Vision-Language Models (VLMs) show significant promise for end-to-end autonomous driving by leveraging the common sense embedded in language models, their reliance on 2D image cues for complex scene understanding and decision-making presents a critical bottleneck for safety and reliability. Current image-based methods struggle with accurate metric spatial reasoning and geometric inference, leading to unreliable driving policies. To bridge this gap, we propose LVLDrive (LiDAR-Vision-Language), a novel framework specifically designed to upgrade existing VLMs with robust 3D metric spatial understanding for autonomous driving by incoperating LiDAR point cloud as an extra input modality. A key challenge lies in mitigating the catastrophic disturbance introduced by disparate 3D data to the pre-trained VLMs. To this end, we introduce a Gradual Fusion Q-Former that incrementally injects LiDAR features, ensuring the stability and preservation of the VLM's existing knowledge base. Furthermore, we develop a spatial-aware question-answering (SA-QA) dataset to explicitly teach the model advanced 3D perception and reasoning capabilities. Extensive experiments on driving benchmarks demonstrate that LVLDrive achieves superior performance compared to vision-only counterparts across scene understanding, metric spatial perception, and reliable driving decision-making. Our work highlights the necessity of explicit 3D metric data for building trustworthy VLM-based autonomous systems.

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