LVLDrive: Enhancing Autonomous Driving with 3D Spatial Understanding
Paper#autonomous driving, vision-language models, LiDAR, 3D perception🔬 Research|Analyzed: Jan 3, 2026 15:38•
Published: Dec 30, 2025 16:35
•1 min read
•ArXivAnalysis
This paper addresses a critical limitation of Vision-Language Models (VLMs) in autonomous driving: their reliance on 2D image cues for spatial reasoning. By integrating LiDAR data, the proposed LVLDrive framework aims to improve the accuracy and reliability of driving decisions. The use of a Gradual Fusion Q-Former to mitigate disruption to pre-trained VLMs and the development of a spatial-aware question-answering dataset are key contributions. The paper's focus on 3D metric data highlights a crucial direction for building trustworthy VLM-based autonomous systems.
Key Takeaways
- •LVLDrive integrates LiDAR data with Vision-Language Models to improve 3D spatial understanding for autonomous driving.
- •A Gradual Fusion Q-Former is used to integrate LiDAR features without disrupting pre-trained VLMs.
- •A spatial-aware question-answering dataset is developed to enhance 3D perception and reasoning.
- •The framework demonstrates superior performance compared to vision-only methods in driving benchmarks.
Reference / Citation
View Original"LVLDrive achieves superior performance compared to vision-only counterparts across scene understanding, metric spatial perception, and reliable driving decision-making."