Article(id=1309639603410268854, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639589137052221, articleNumber=null, orderNo=null, doi=10.19822/j.cnki.1671-6329.20250156, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172013379, onlineDateStr=2026-09-23, pubDate=1770220800000, pubDateStr=2026-02-05, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172013379, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172013379, creator=13701087609, updateTime=1790172013379, updator=13701087609, issue=Issue{id=1309639589137052221, tenantId=1146029695717560320, journalId=1189645257101713411, year='2026', volume='', issue='2', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1770220800000', pubDateStr='2026-02-05', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172009977, creator='13701087609', updateTime=1790233873475, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309899063529669032, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639589137052221, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309899063529669033, tenantId=1146029695717560320, journalId=1189645257101713411, issueId=1309639589137052221, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=32, endPage=36, ext={EN=ArticleExt(id=1309639603645149879, articleId=1309639603410268854, tenantId=1146029695717560320, journalId=1189645257101713411, language=EN, title=Adaptive Cruise Control System Test Scenario Generation Method Based on Reinforcement Learning Algorithm, columnId=1309639589728449086, journalTitle=Automotive Digest, columnName=Special Topic on Autonomous Driving Technology, runingTitle=null, highlight=null, articleAbstract=

In order to improve the testing efficiency and comprehensiveness of the Adaptive Cruise Control (ACC) system, this paper proposes a test scenario generation method based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, typical driving scenarios are designed, and a test scenario model is constructed, which includes scenario initialization, the definition of state and control variables, the agent-environment interaction mechanism, and a reward function. Then, an agent is designed based on a reinforcement learning algorithm, and generate challenging test scenarios. a reward function integrating exposure rate, adversarial factors, and unreasonable collision penalties is developed, guiding the agent to generate scenarios that are not only highly risky but also consistent with real traffic rules. Finally, simulation experiments on left/right cut-in and cut-out scenarios are conducted to validate the effectiveness of the proposed method. The results demonstrate that this approach can automatically generate critical hazardous scenarios for ACC, improve test coverage and specificity, and provide significant value for ensuring system safety testing.

, authors=Deyin Zhao1, Yan Gao1, Yilin Qin1, Peixing Zhang2, Jiaming Zhang1, authorsList=Deyin Zhao, Yan Gao, Yilin Qin, Peixing Zhang, Jiaming Zhang, 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=1309639603905196728, articleId=1309639603410268854, tenantId=1146029695717560320, journalId=1189645257101713411, language=CN, title=基于强化学习算法的自适应巡航控制系统测试场景生成方法, columnId=1309639589879444032, journalTitle=汽车文摘, columnName=自动驾驶技术专题, runingTitle=null, highlight=null, articleAbstract=

为了提升自适应巡航控制系统的测试效率和完善性,提出了一种基于深度确定性策略梯度算法的测试场景生成方法。首先,设计了典型工况场景,构建了包含场景初始化、状态变量与控制变量定义、智能体交互机制及奖励函数的测试场景模型。然后,基于强化学习算法设计智能体,生成具有挑战性的测试场景,设计了综合曝光率、对抗性因素和不合理碰撞处罚的奖励函数,引导智能体生成既具有较高危险性又符合真实交通规则的场景。最后,通过左/右侧切入、切出等场景的仿真试验,验证了所提方法的有效性。结果表明,该方法能够自动生成ACC的关键危险场景,提高测试覆盖度和针对性,对实现系统的安全测试具有重要价值。

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基于强化学习算法的自适应巡航控制系统测试场景生成方法
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赵德银 1 , 高艳 1 , 秦熠琳 1 , 张培兴 2 , 张家铭 1
汽车文摘 | 自动驾驶技术专题 2026,(2): 32-36
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汽车文摘 |自动驾驶技术专题 2026 , (2) : 32 -36
基于强化学习算法的自适应巡航控制系统测试场景生成方法
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赵德银1, 高艳1, 秦熠琳1, 张培兴2, 张家铭1
作者信息
  • 1.中国第一汽车股份有限公司研发总院,长春 130013
  • 2.吉林大学汽车工程学院,长春 130025
Adaptive Cruise Control System Test Scenario Generation Method Based on Reinforcement Learning Algorithm
Deyin Zhao1, Yan Gao1, Yilin Qin1, Peixing Zhang2, Jiaming Zhang1
Affiliations
  • 1.Global R&D Center, China FAW Corporation Limited, Changchun 130013
  • 2.College of Automotive Engineering, Jilin University, Changchun 130025
出版时间: 2026-02-05 doi: 10.19822/j.cnki.1671-6329.20250156
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为了提升自适应巡航控制系统的测试效率和完善性,提出了一种基于深度确定性策略梯度算法的测试场景生成方法。首先,设计了典型工况场景,构建了包含场景初始化、状态变量与控制变量定义、智能体交互机制及奖励函数的测试场景模型。然后,基于强化学习算法设计智能体,生成具有挑战性的测试场景,设计了综合曝光率、对抗性因素和不合理碰撞处罚的奖励函数,引导智能体生成既具有较高危险性又符合真实交通规则的场景。最后,通过左/右侧切入、切出等场景的仿真试验,验证了所提方法的有效性。结果表明,该方法能够自动生成ACC的关键危险场景,提高测试覆盖度和针对性,对实现系统的安全测试具有重要价值。

自适应巡航控制系统  /  测试场景生成  /  强化学习  /  DDPG算法

In order to improve the testing efficiency and comprehensiveness of the Adaptive Cruise Control (ACC) system, this paper proposes a test scenario generation method based on the Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, typical driving scenarios are designed, and a test scenario model is constructed, which includes scenario initialization, the definition of state and control variables, the agent-environment interaction mechanism, and a reward function. Then, an agent is designed based on a reinforcement learning algorithm, and generate challenging test scenarios. a reward function integrating exposure rate, adversarial factors, and unreasonable collision penalties is developed, guiding the agent to generate scenarios that are not only highly risky but also consistent with real traffic rules. Finally, simulation experiments on left/right cut-in and cut-out scenarios are conducted to validate the effectiveness of the proposed method. The results demonstrate that this approach can automatically generate critical hazardous scenarios for ACC, improve test coverage and specificity, and provide significant value for ensuring system safety testing.

Adaptive cruise control system  /  Test scenario generation  /  Reinforcement learning  /  DDPG algorithm
赵德银, 高艳, 秦熠琳, 张培兴, 张家铭. 基于强化学习算法的自适应巡航控制系统测试场景生成方法. 汽车文摘, 2026 , (2) : 32 -36 . DOI: 10.19822/j.cnki.1671-6329.20250156
Deyin Zhao, Yan Gao, Yilin Qin, Peixing Zhang, Jiaming Zhang. Adaptive Cruise Control System Test Scenario Generation Method Based on Reinforcement Learning Algorithm[J]. Automotive Digest, 2026 , (2) : 32 -36 . DOI: 10.19822/j.cnki.1671-6329.20250156

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doi: 10.19822/j.cnki.1671-6329.20250156
  • 首发时间:2026-09-23
  • 出版时间:2026-02-05
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    1.中国第一汽车股份有限公司研发总院,长春 130013
    2.吉林大学汽车工程学院,长春 130025
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