Article(id=1309640003660116642, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250749, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1765209600000, revisedDateStr=2025-12-09, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172108807, onlineDateStr=2026-09-23, pubDate=1787500800000, pubDateStr=2026-08-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172108807, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172108807, creator=13701087609, updateTime=1790172108807, updator=13701087609, issue=Issue{id=1309639998379491369, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='8', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1787500800000', pubDateStr='2026-08-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172107547, creator='13701087609', updateTime=1790232702427, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309894151819330406, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309894151819330407, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639998379491369, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=19, endPage=29, ext={EN=ArticleExt(id=1309640003865637539, articleId=1309640003660116642, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=The Comparison of Traffic Violations Between Autonomous Driving and Human Driving Based on Real Driving Data: Feature Extraction and Mechanism Analysis, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To explore the traffic violation characteristics of Autonomous Vehicles (AV) in real-world traffic environments, the adaptability of existing traffic regulations, and the differences compared to Human-Driven Vehicles (HV), this study utilizes the SinD artificial driving dataset, highD artificial driving dataset, and Argoverse2 autonomous driving dataset. 6 types of violation cases are analyzed, including speed limit violations, safe following distance violations, lane changes, running red lights, left-turn blocking of straight-moving vehicles, and continuous lane changes. The study employs Pearson correlation coefficients and linear regression models to analyze speed limit violations, evaluates the safety of following distance and lane changes using headway time as an indicator, and uses data proportion analysis to assess red light violations and left-turn blocking violations that impede straight-moving traffic. Additionally, continuous lane change characteristics are studied by examining lane change frequency, stability, and speed variability. The results show that under high-speed conditions, AVs are more prone to persistent speed limit violations but exhibit more stable speeds. The risk of safe following distance violations is higher for AVs compared to HVs, while the risk of lane change violations is lower for AVs. Red light violations are more significantly influenced by weather conditions for AVs, and once left-turn blocking violations occur, they present more severe risks. In short-distance, low-speed scenarios, AVs tend to change lanes more frequently with poorer stability. The study confirms that AVs have not yet fully complied with traffic regulations, with higher rates of speed limit and safe following distance violations. Significant differences exist between AVs and HVs across all six types of violations. The applicability of current traffic regulations to AVs has limitations. Based on this, future research can further explore 4 areas: the construction of specialized AV violation datasets, the improvement of specific traffic regulations for AVs, upgrades to autonomous driving algorithms, and the establishment of compliance evaluation mechanisms. The findings provide theoretical support and practical reference for the compliant integration of AVs into the existing traffic system.

, authors=Huimin Liu, Runmin Wang, Yu Zhu, Jingjun Cheng, Ju Li, authorsList=Huimin Liu, Runmin Wang, Yu Zhu, Jingjun Cheng, Ju Li, authorCompany=null, correspAuthors=Runmin Wang, 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=1309640004117295780, articleId=1309640003660116642, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于真实驾驶数据的自动驾驶与人类驾驶违规行为对比——特征挖掘与机制解析, columnId=0, journalTitle=汽车技术, columnName=, runingTitle=null, highlight=null, articleAbstract=

为探究真实交通环境中自动驾驶汽车(AV)的违规行为特征、现有交规适配性及其与人工驾驶汽车(HV)的差异,基于SinD人工驾驶数据集、highD人工驾驶数据集和Argoverse2自动驾驶数据集,提取AV与HV的限速、安全车距、变道、信号灯闯行、左转妨碍直行、连续变道6类违规案例数据展开分析。利用皮尔逊相关系数与线性回归模型剖析限速违规,以车头时距评估安全车距与变道的安全性,通过数据占比分析信号灯闯行与左转妨碍直行违规,并结合变道频率、变道频率稳定性及速度波动性指标分析连续变道特征。结果表明:在高速状态下,AV易形成持续性限速违规,但速度更稳定;AV的安全车距违规风险高于HV,变道违规风险则低于HV;AV的信号灯闯行行为受天气影响更显著,左转妨碍直行违规一旦发生则风险更严重;短距离、低速场景下AV变道更频繁且稳定性较差。研究证实,AV尚未实现对交规的完全合规遵循,其限速与安全车距违规发生率较高,且AV与HV在6类违规行为上均存在显著差异;现有交规对AV的适用性存在局限。同时提出可从AV违规专项数据集构建、专属交规完善、自动驾驶算法升级及合规评价准入机制建立4方面深化研究,为AV合规融入现有交通体系提供理论支撑与实践参考。

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王润民(1989—),高级工程师,主要研究方向为自动驾驶测试评价技术,。
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基于真实驾驶数据的自动驾驶与人类驾驶违规行为对比——特征挖掘与机制解析
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刘绘敏 , 王润民 , 朱宇 , 承靖钧 , 李聚
汽车技术 | 2026,(8): 19-29
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汽车技术 | 2026 , (8) : 19 -29
基于真实驾驶数据的自动驾驶与人类驾驶违规行为对比——特征挖掘与机制解析
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刘绘敏, 王润民 , 朱宇, 承靖钧, 李聚
作者信息
  • 长安大学,西安 710018
通讯作者:
王润民(1989—),高级工程师,主要研究方向为自动驾驶测试评价技术,。
The Comparison of Traffic Violations Between Autonomous Driving and Human Driving Based on Real Driving Data: Feature Extraction and Mechanism Analysis
Huimin Liu, Runmin Wang , Yu Zhu, Jingjun Cheng, Ju Li
Affiliations
  • Chang'an University, Xi'an 710018
出版时间: 2026-08-24 doi: 10.19620/j.cnki.1000-3703.20250749
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为探究真实交通环境中自动驾驶汽车(AV)的违规行为特征、现有交规适配性及其与人工驾驶汽车(HV)的差异,基于SinD人工驾驶数据集、highD人工驾驶数据集和Argoverse2自动驾驶数据集,提取AV与HV的限速、安全车距、变道、信号灯闯行、左转妨碍直行、连续变道6类违规案例数据展开分析。利用皮尔逊相关系数与线性回归模型剖析限速违规,以车头时距评估安全车距与变道的安全性,通过数据占比分析信号灯闯行与左转妨碍直行违规,并结合变道频率、变道频率稳定性及速度波动性指标分析连续变道特征。结果表明:在高速状态下,AV易形成持续性限速违规,但速度更稳定;AV的安全车距违规风险高于HV,变道违规风险则低于HV;AV的信号灯闯行行为受天气影响更显著,左转妨碍直行违规一旦发生则风险更严重;短距离、低速场景下AV变道更频繁且稳定性较差。研究证实,AV尚未实现对交规的完全合规遵循,其限速与安全车距违规发生率较高,且AV与HV在6类违规行为上均存在显著差异;现有交规对AV的适用性存在局限。同时提出可从AV违规专项数据集构建、专属交规完善、自动驾驶算法升级及合规评价准入机制建立4方面深化研究,为AV合规融入现有交通体系提供理论支撑与实践参考。

自动驾驶  /  人工驾驶  /  违规行为  /  真实驾驶数据集  /  合规

To explore the traffic violation characteristics of Autonomous Vehicles (AV) in real-world traffic environments, the adaptability of existing traffic regulations, and the differences compared to Human-Driven Vehicles (HV), this study utilizes the SinD artificial driving dataset, highD artificial driving dataset, and Argoverse2 autonomous driving dataset. 6 types of violation cases are analyzed, including speed limit violations, safe following distance violations, lane changes, running red lights, left-turn blocking of straight-moving vehicles, and continuous lane changes. The study employs Pearson correlation coefficients and linear regression models to analyze speed limit violations, evaluates the safety of following distance and lane changes using headway time as an indicator, and uses data proportion analysis to assess red light violations and left-turn blocking violations that impede straight-moving traffic. Additionally, continuous lane change characteristics are studied by examining lane change frequency, stability, and speed variability. The results show that under high-speed conditions, AVs are more prone to persistent speed limit violations but exhibit more stable speeds. The risk of safe following distance violations is higher for AVs compared to HVs, while the risk of lane change violations is lower for AVs. Red light violations are more significantly influenced by weather conditions for AVs, and once left-turn blocking violations occur, they present more severe risks. In short-distance, low-speed scenarios, AVs tend to change lanes more frequently with poorer stability. The study confirms that AVs have not yet fully complied with traffic regulations, with higher rates of speed limit and safe following distance violations. Significant differences exist between AVs and HVs across all six types of violations. The applicability of current traffic regulations to AVs has limitations. Based on this, future research can further explore 4 areas: the construction of specialized AV violation datasets, the improvement of specific traffic regulations for AVs, upgrades to autonomous driving algorithms, and the establishment of compliance evaluation mechanisms. The findings provide theoretical support and practical reference for the compliant integration of AVs into the existing traffic system.

Autonomous Driving (AV)  /  Human-Driven Driving (HV)  /  Traffic violations  /  Real-world driving datasets  /  Compliance of traffic regulations
刘绘敏, 王润民, 朱宇, 承靖钧, 李聚. 基于真实驾驶数据的自动驾驶与人类驾驶违规行为对比——特征挖掘与机制解析. 汽车技术, 2026 , (8) : 19 -29 . DOI: 10.19620/j.cnki.1000-3703.20250749
Huimin Liu, Runmin Wang, Yu Zhu, Jingjun Cheng, Ju Li. The Comparison of Traffic Violations Between Autonomous Driving and Human Driving Based on Real Driving Data: Feature Extraction and Mechanism Analysis[J]. Automobile Technology, 2026 , (8) : 19 -29 . DOI: 10.19620/j.cnki.1000-3703.20250749
  • 国家重点研发计划项目(2024YFB2505705)
  • 国家自然科学基金重点项目(52232015)
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doi: 10.19620/j.cnki.1000-3703.20250749
  • 首发时间:2026-09-23
  • 出版时间:2026-08-24
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  • 修回日期:2025-12-09
基金
国家重点研发计划项目(2024YFB2505705)
国家自然科学基金重点项目(52232015)
作者信息
    长安大学,西安 710018

通讯作者:

王润民(1989—),高级工程师,主要研究方向为自动驾驶测试评价技术,。
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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