Investigating Vision-Language Model for Point Cloud-based Vehicle Classification

• URL: http://arxiv.org/abs/2504.08154v1
• Date: Thu, 10 Apr 2025 22:37:27 GMT
• Title: Investigating Vision-Language Model for Point Cloud-based Vehicle Classification
• Authors: Yiqiao Li, Jie Wei, Camille Kamga,
• Abstract summary: Heavy-duty trucks pose significant safety challenges due to their large size and limited maneuverability.<n>Traditional LiDAR-based truck classification methods rely on extensive manual annotations.<n>This study introduces a novel framework that integrates roadside LiDAR point cloud data with vision-language models.
• Score: 3.9148444463558465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Heavy-duty trucks pose significant safety challenges due to their large size and limited maneuverability compared to passenger vehicles. A deeper understanding of truck characteristics is essential for enhancing the safety perspective of cooperative autonomous driving. Traditional LiDAR-based truck classification methods rely on extensive manual annotations, which makes them labor-intensive and costly. The rapid advancement of large language models (LLMs) trained on massive datasets presents an opportunity to leverage their few-shot learning capabilities for truck classification. However, existing vision-language models (VLMs) are primarily trained on image datasets, which makes it challenging to directly process point cloud data. This study introduces a novel framework that integrates roadside LiDAR point cloud data with VLMs to facilitate efficient and accurate truck classification, which supports cooperative and safe driving environments. This study introduces three key innovations: (1) leveraging real-world LiDAR datasets for model development, (2) designing a preprocessing pipeline to adapt point cloud data for VLM input, including point cloud registration for dense 3D rendering and mathematical morphological techniques to enhance feature representation, and (3) utilizing in-context learning with few-shot prompting to enable vehicle classification with minimally labeled training data. Experimental results demonstrate encouraging performance of this method and present its potential to reduce annotation efforts while improving classification accuracy.

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