Article(id=1309639166426702823, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250416, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1772553600000, revisedDateStr=2026-03-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1790171909194, onlineDateStr=2026-09-23, pubDate=1784822400000, pubDateStr=2026-07-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790171909194, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790171909194, creator=13701087609, updateTime=1790171909194, updator=13701087609, issue=Issue{id=1309639160835698887, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='7', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1784822400000', pubDateStr='2026-07-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790171907861, creator='13701087609', updateTime=1790232758449, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894386750681363, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894386750681364, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639160835698887, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=28, endPage=35, ext={EN=ArticleExt(id=1309639166607057896, articleId=1309639166426702823, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Simulation Test Method for Intelligent Vehicle Camera Sensors in Fog Environments, columnId=1309639161502593225, journalTitle=Automobile Technology, columnName=Special Topic on Object Detection and Road Lane Recognition Technologies for Autonomous Driving, runingTitle=null, highlight=null, articleAbstract=

In the process of testing intelligent vehicle camera sensor functionality in foggy environments, real-world fog test scenarios face high acquisition costs, poor repeatability, and uncontrollable visibility, while virtual simulation methods lack sufficient realism. To address these issues, this paper proposes a simulation test method specifically for camera sensors in fog environments. First, atmospheric light in different scene regions is estimated via semantic segmentation; next, combining target visibility and fog microphysical parameters, the atmospheric scattering coefficient is computed using a scattering model, and depth information is corrected with the camera intrinsic and extrinsic parameters to produce an accurate transmission map; finally, a real, controllable fog test platform is constructed, and the proposed method is validated through image quality evaluation (PSNR/SSIM) and YOLO-V5-based object recognition tests. Experimental results demonstrate that this method generates high-fidelity foggy images across a 25~100 m visibility range and outperforms existing simulation approaches in both image quality and functional testing, significantly reducing data-collection costs while ensuring repeatable, controllable, and high-quality camera sensor validation.

, authors=Xinwei Fan¹, Junchao Wei¹, Yinzi Huang², xin Yan¹, Rongliang Zhang¹, authorsList=Xinwei Fan, Junchao Wei, Yinzi Huang, xin Yan, Rongliang Zhang, authorCompany=null, correspAuthors=xin Yan, 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=1309639168477717481, articleId=1309639166426702823, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=面向雾气环境的智能汽车相机传感器模拟测试方法, columnId=1309639161645199563, journalTitle=汽车技术, columnName=面向智能驾驶的目标检测与道路通行区域感知技术专题, runingTitle=null, highlight=null, articleAbstract=

针对雾气环境下智能汽车相机传感器功能测试过程中，实景雾气测试场景获取成本高、不可重复和雾气能见度不可控，以及虚拟仿真测试方法拟真度不足等问题，提出一种面向雾气环境的智能汽车相机传感器模拟测试方法。首先，基于语义分割对场景中不同区域的大气光进行估计；随后，结合目标能见度和雾微物理参数，通过散射模型计算大气散射系数，并利用相机内外参数校正深度信息，以生成精确的透射率图；最后，构建真实可控的雾化测试平台，并通过图像质量评价（PSNR/SSIM）及基于YOLO-V5的目标识别测试对所提方法进行验证。试验结果表明，该方法在25~100 m能见度范围内均能生成高保真度的雾气图像，并在图像质量与功能测试上均优于现有仿真方案，显著降低了数据采集成本，同时保证了相机传感器验证的质量、可控性和可重复性。

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