面向基于点云的车辆分类的视觉语言模型研究
Investigating Vision-Language Model for Point Cloud-based Vehicle Classification
AI总结：
针对传统激光雷达卡车分类依赖大量标注的问题，提出融合路测点云与视觉语言模型的新框架，通过点云预处理和少样本上下文学习实现高效分类，可降低标注成本并提升精度。
摘要：
与乘用车相比，重型卡车因体积大、机动性差带来了严峻的安全挑战。深入理解卡车特性对于提升协同自动驾驶的安全性至关重要。传统基于激光雷达（LiDAR）的卡车分类方法依赖大量人工标注，存在人力投入大、成本高的问题。在海量数据集上训练的大语言模型（LLM）快速发展，为利用其少样本学习能力实现卡车分类提供了契机。但现有视觉语言模型（VLM）主要基于图像数据集训练，难以直接处理点云数据。本研究提出一种将路侧激光雷达点云数据与VLM融合的新型框架，可实现高效精准的卡车分类，为协同安全驾驶环境提供支撑。本研究有三项核心创新：（1）采用真实场景激光雷达数据集开展模型开发；（2）设计预处理流水线将点云数据适配为VLM输入，包括用于密集3D渲染的点云配准，以及增强特征表达的数学形态学技术；（3）结合上下文学习与少样本提示，仅需极少标注训练数据即可实现车辆分类。实验结果表明，该方法性能表现优异，有望在减少标注工作量的同时提升分类精度。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
