Article(id=1309640048488833925, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250600, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1762358400000, revisedDateStr=2025-11-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172119494, onlineDateStr=2026-09-23, pubDate=1782230400000, pubDateStr=2026-06-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172119494, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172119494, creator=13701087609, updateTime=1790172119494, updator=13701087609, issue=Issue{id=1309640041656316677, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='6', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1782230400000', pubDateStr='2026-06-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172117866, creator='13701087609', updateTime=1790232797347, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894549917495577, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894549917495578, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640041656316677, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=45, endPage=52, ext={EN=ArticleExt(id=1309640048744686470, articleId=1309640048488833925, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Electromagnetic Immunity Test Method for ADAS System of Distributed Vision and LiDAR Vehicles, columnId=1309640043162075259, journalTitle=Automobile Technology, columnName=Selected Academic Papers of the 28th CAST Annual Meeting, runingTitle=null, highlight=null, articleAbstract=

Given the current lack of integrated testing solutions for distributed vision and LiDAR systems in Electromagnetic Susceptibility (EMS) testing of Advanced Driver Assistance Systems (ADAS), and the safety risks associated with multi-sensor coordination in complex electromagnetic environments, this paper proposes a vehicle-based EMS testing method for ADAS systems. By using point cloud digital signal injection technology to simulate the characteristics of LiDAR point cloud scenes, the paper constructs high-precision test scenarios using PreScan and Simulink, and employs a pass-band filtering algorithm to reduce noise in the point cloud data. The test results show that the pass-through filtering algorithm reduces the number of point clouds by 22 851 while preserving target features, thereby significantly reducing the computational load on the equipment. At the same time, the sensor hardware-in-the-loop platform successfully activated all sensors during functional activation testing under Adaptive Cruise Control (ACC) conditions in a semi-anechoic chamber. The speed and relative distance recognition accuracy of the millimeter-wave radar, camera, and LiDAR all met the expected requirements, validating the effectiveness and reliability of the proposed solution.

, authors=Junli Chen¹, Yongjian Zhu², Yu Zhang², Jin Jia², Yanming Qin¹, authorsList=Junli Chen, Yongjian Zhu, Yu Zhang, Jin Jia, Yanming Qin, authorCompany=null, correspAuthors=null, 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=1309640049042482055, articleId=1309640048488833925, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于激光雷达的ADAS系统的电磁抗扰度测试方法试验, columnId=1309640043359207549, journalTitle=汽车技术, columnName=第二十八届中国科协年会学术论文选登, runingTitle=null, highlight=null, articleAbstract=

针对当前高级驾驶辅助系统（ADAS）的电磁抗干扰度（EMS）测试缺乏分布式视觉与激光雷达协同测试方案，且复杂电磁环境下多传感器协同存安全隐患的问题，提出一种基于车辆的ADAS EMS测试方法。通过设计仿真场景点云数字信号注入技术模拟激光雷达点云场景特征，基于PreScan与Simulink构建高精度测试工况，并采用直通滤波算法实现点云降噪。试验结果表明：直通滤波算法在保留目标特征的前提下，使点云数量减少22 851个，显著降低设备算力负荷。同时，搭建的传感器硬件在环平台在半点波暗室的自适应巡航控制（ACC）工况功能激活测试中，成功激活各传感器，毫米波雷达、摄像头和激光雷达的速度和相对距离识别精度均达到预期要求，验证了提出方案的有效性和可靠性。

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