Article(id=1309639700638429197, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639699476606989, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250225, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1747584000000, revisedDateStr=2025-05-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172036561, onlineDateStr=2026-09-23, pubDate=1771862400000, pubDateStr=2026-02-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172036561, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172036561, creator=13701087609, updateTime=1790172036561, 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=18, endPage=31, ext={EN=ArticleExt(id=1309639701422764046, articleId=1309639700638429197, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research Progress on Object Detection Technology with LiDAR and Camera Fusion in Autonomous Driving, 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=

Facing the technical bottlenecks in ensuring the safety and reliability of environmental perception in autonomous driving, this paper systematically reviews the technological roadmap and recent developments of LiDAR-camera fusion-based object detection. By analyzing the limitations of single-sensor perception under complex road conditions, the study demonstrates the inevitability of multimodal sensor fusion. The findings indicate that sensor fusion can significantly improve object detection performance in scenarios involving occlusion, long distances, and complex environments. Experiments on public datasets such as nuScenes and KITTI further verify that sensor fusion effectively enhances perception robustness and detection accuracy in diverse scenes. However, current technologies still face challenges such as insufficient real-time performance, high algorithmic complexity, and cost constraints. Future research should focus on improving the precision and efficiency of fusion algorithms and promoting the standardization of multimodal collaborative perception.

, authors=Yajun Pang^{1, 2}, Jiwen Zhang^{1, 2}, Yifan Ning^{1, 2}, authorsList=Yajun Pang, Jiwen Zhang, Yifan Ning, authorCompany=null, correspAuthors=Yajun Pang, 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=1309639701900914703, articleId=1309639700638429197, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=自动驾驶中LiDAR与相机融合的目标检测技术研究进展, 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=

针对自动驾驶场景中环境感知能力的安全性和可靠性面临的技术瓶颈，系统梳理自动驾驶中激光雷达（LiDAR）与相机融合的目标检测技术路线及发展动态，通过分析单一传感器在复杂路况下的局限性，论证多模态融合感知的必然趋势。研究发现：在遮挡、远距离和复杂环境条件下，传感器融合技术能够显著提升目标检测效果；在nuScenes、KITTI等公开数据集的验证试验中，传感融合技术有效增强系统在多变场景中的感知鲁棒性与检测性能；当前技术仍面临实时性不足、算法复杂度高及成本制约等挑战，未来仍需优化融合算法的精度和实时性，推动多模态协同感知的标准化。

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