Article(id=1309639700961395333, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20241037, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1743955200000, revisedDateStr=2025-04-07, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172036637, onlineDateStr=2026-09-23, pubDate=1771862400000, pubDateStr=2026-02-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172036637, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172036637, creator=13701087609, updateTime=1790172036637, updator=13701087609, issue=Issue{id=1309639699476606989, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='2', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1771862400000', pubDateStr='2026-02-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172036284, creator='13701087609', updateTime=1790232907749, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309895012968661940, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309895012968661941, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=39, endPage=47, ext={EN=ArticleExt(id=1309639701590540934, articleId=1309639700961395333, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Design and Experimental Validation of an Enhanced Transformer-UKF Multi-Modal 3D Object Detection System for Intelligent Vehicles, columnId=1309639700797809024, journalTitle=Automobile Technology, columnName=Special Topic on LiDAR Perception and Multimodal 3D Object Detection Technology for Autonomous Driving, runingTitle=null, highlight=null, articleAbstract=

To enhance the accuracy of environmental perception and object detection for intelligent vehicles, this paper proposes a novel feature fusion-based 3D object detection method. First, the Transformer model is improved by leveraging a multi-head self-attention mechanism to fully capture local spatial feature information while preserving diverse feature weights, enabling the extraction of point cloud and image features. An Unscented Kalman Filter (UKF) is then utilized to design a multimodal fusion system that combines image and point cloud data, ultimately achieving 3D object detection. The proposed model is trained and tested on both public and private datasets and is compared against several state-of-the-art algorithms. Experimental results demonstrate that the proposed feature fusion-based 3D object detection model achieves an improvement in mean Average Precision (mAP) of 0.34 percentage points and 3.03 percentage points compared to widely used algorithms such as F-ConvNet and F-PointNet, respectively. For vehicle and cyclist detection tasks, the mAP increases by 2.52 percentage points and 9.32 percentage points, respectively. Moreover, the inference results on real-world vehicle data align closely with the training evaluation outcomes. These findings indicate that the proposed algorithm improves the accuracy of object detection in perception systems to a certain extent and provides a valuable reference for the design of object detection systems in autonomous driving.

, authors=Zhening Zhang^{1, 2}, Zhen Liu^{1, 2}, Huaqiang Wang^{1, 2}, authorsList=Zhening Zhang, Zhen Liu, Huaqiang Wang, authorCompany=null, correspAuthors=Zhen Liu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1309639701867364999, articleId=1309639700961395333, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于改进Transformer-无迹卡尔曼滤波器的智能车辆多模态3D目标检测方法, columnId=1309639700932026754, journalTitle=汽车技术, columnName=激光雷达感知与自动驾驶多模态3D 目标检测技术专题, runingTitle=null, highlight=null, articleAbstract=

为提升智能车辆环境感知目标检测的准确性，提出一种特征融合3D目标检测方法。首先，对Transformer模型进行改进，借助多头自注意力机制，充分考虑数据空间的局部特征信息，并保留不同的特征权重，以提取点云与图像特征；然后，借助无迹卡尔曼滤波器（UKF）设计图像与点云多模态融合系统，最终实现3D目标检测；最后，采用KITTI数据集和实车数据集对模型进行训练和推理，并与多种主流算法进行对比。验证结果显示，与应用广泛的截锥卷积网络（F-ConvNet）、视锥点云网络（F-PointNet）等主流多模态融合算法相比，所提出的目标检测模型多类别平均精度分别提升了0.34百分点和3.03百分点，车辆和骑行者对象的检测平均精度分别提升了2.52百分点和9.32百分点，且该模型在实车数据推理中的表现与训练评测结果基本一致。

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