Article(id=1309640086657000370, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250272, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1749744000000, revisedDateStr=2025-06-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172128595, onlineDateStr=2026-09-23, pubDate=1776960000000, pubDateStr=2026-04-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172128595, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172128595, creator=13701087609, updateTime=1790172128595, updator=13701087609, issue=Issue{id=1309640086191432626, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='4', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1776960000000', pubDateStr='2026-04-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172128484, creator='13701087609', updateTime=1790232864374, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894831258825016, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894831258825017, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309640086191432626, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=10, endPage=16, ext={EN=ArticleExt(id=1309640086828966835, articleId=1309640086657000370, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Closed-Loop Autonomous Driving Based on LLM-DDPG Collaborative Decision and Control, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To address the limited generalization ability of rule-based methods in long-tail scenarios, as well as the overfitting issue and poor interpretability of data-driven models in autonomous driving, this paper proposes an autonomous driving framework, based on a Large Language Model (LLM) and Deep Deterministic Policy Gradient (DDPG). The framework integrates commonsense knowledge with environmental information to generate interpretable driving decisions, while the DDPG algorithm is employed to achieve low-level continuous control. In the memory module, a hybrid retrieval mechanism that combines numerical-feature similarity and semantic-feature similarity is designed to match similar historical cases in real time, thereby supporting dynamic decision-making. Experimental results show that compared with Rule-Driving and DQN-Driving, the proposed method achieves relative improvements of approximately 33% and 14% in success rate, respectively, in heterogeneous test Scenario A. In heterogeneous test Scenario B, the relative improvements further increase to approximately 70% and 42%, demonstrating stronger cross-scenario generalization capability and environmental adaptability.

, authors=Tongying Guo, Shuo Fan, Yan Zheng, authorsList=Tongying Guo, Shuo Fan, Yan Zheng, 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=1309640087034487732, articleId=1309640086657000370, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于LLM-DDPG协同决控实现闭环自动驾驶, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对自动驾驶领域中规则驱动方法在长尾场景下泛化能力有限，数据驱动模型存在数据过拟合、决策过程缺乏可解释性等问题，提出一种基于大语言模型（LLM）和深度确定性策略梯度（DDPG）驱动的自动驾驶框架。通过整合常识与环境数据，生成可解释性决策；利用DDPG算法实现底层控制；在存储模块中，设计数值+语义特征相似性检索机制，为当前决策任务实时匹配相似历史案例辅助动态决策。试验结果表明：相较于Rule-Driving和DQN-Driving，提出的方法在异构测试场景A中的成功率分别提升约33%和14%；在异构测试场景B中的成功率分别提升约70%和42%，表现出更强的跨场景泛化能力和环境适应能力。

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