Article(id=1308463017750978772, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250143, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1752422400000, revisedDateStr=2025-07-14, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891493498, onlineDateStr=2026-09-20, pubDate=1778774400000, pubDateStr=2026-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891493498, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891493498, creator=13701087609, updateTime=1789891493498, updator=13701087609, issue=Issue{id=1308463017016972142, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='5', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1778774400000', pubDateStr='2026-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891493324, creator='13701087609', updateTime=1789892866211, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468775385322487, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468775385322488, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=19, ext={EN=ArticleExt(id=1308463017914556629, articleId=1308463017750978772, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Data-Driven End-to-End Autonomous Driving Technology: Evolution, Current Status, and Future Challenges, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

This paper systematically reviews the developmental trajectory of autonomous driving systems from the 1920s to 2024, analyzing four key phases's characteristics—technological origins and infancy, theoretical foundations and exploration, practical breakthroughs, and rapid development with paradigm shifts. It focuses on the modular "perception-decision-control" driving, the end-to-end driving and the synergy and competition within the vehicle-road-cloud collaborative. Research indicates that the current autonomous driving landscape exhibits a "dual-track development pattern dominated by modular driving yet driven by end-to-end driving breakthroughs", where data closed-loops and vehicle-road-cloud integration serve as critical pathways to address long-tail scenarios and enhance system generalization. However, insufficient data quality, lack of algorithmic interpretability, surging computational demands and policy adaptation delays remain the main challenges. Predictions suggest that advancements in large model-powered cognitive reasoning, neuro-symbolic hybrid architectures, lightweight deployment, and deep vehicle-road-cloud collaboration will propel autonomous driving toward a safer, more efficient, and inclusive intelligent mobility ecosystem. This study provides a systematic reference for understanding the technological landscape, optimizing development strategies, and formulating industry policies.

, authors=Yao Wang, Wenjing Bai, Shikang Pei, Wenbo Zhao, Yueyun Xu, authorsList=Yao Wang, Wenjing Bai, Shikang Pei, Wenbo Zhao, Yueyun Xu, 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=1308463018162020566, articleId=1308463017750978772, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=数据驱动的端到端自动驾驶技术：演进历程、现状与未来挑战, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

系统梳理了自动驾驶技术自20世纪20年代至2024年的发展历程，分阶段剖析了技术起源与萌芽期、理论奠基与探索期、技术实践突破期、快速发展与范式转型期4个发展阶段及各阶段特点，并聚焦于感知-决策-控制模块化范式、端到端一体化范式、车路云一体化的协同与竞争。结果表明，当前自动驾驶技术呈现“模块化主导、端到端突破”的双轨发展态势，数据闭环与车路云一体化成为解决长尾场景与提升系统泛化能力的关键路径，然而，数据质量不足、算法可解释性欠缺、算力需求激增及政策适配滞后仍是主要挑战。由此可预测，大模型赋能的认知推理、神经符号混合架构、轻量化部署及车路云深度协同等技术将推动自动驾驶向安全、高效、普惠的智能出行生态转型。

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