Article(id=1282029992330641458, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.07.0122, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739462400000, receivedDateStr=2025-02-14, revisedDate=1747238400000, revisedDateStr=2025-05-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589369137, onlineDateStr=2026-07-09, pubDate=1753632000000, pubDateStr=2025-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589369137, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589369137, creator=13701087609, updateTime=1783589369137, updator=13701087609, issue=Issue{id=1282029960869167928, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='7', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1753632000000', pubDateStr='2025-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589361636, creator='13701087609', updateTime=1783589710614, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031424651903975, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031424656098280, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=209, endPage=217, ext={EN=ArticleExt(id=1282029993224028211, articleId=1282029992330641458, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Lane change and obstacle avoidance trajectory planning method for intelligent vehicles using case-based reasoning mechanism, columnId=1149733270084042840, journalTitle=China Safety Science Journal, columnName=Public safety, runingTitle=null, highlight=null, articleAbstract=

To solve the problem of difficulty in balancing trajectory optimization and real-time performance in lane change and obstacle avoidance trajectory planning for intelligent vehicles, a CBR mechanism based intelligent vehicle lane changing and obstacle avoidance trajectory planning method was proposed. The optimal trajectories in typical scenarios were obtained by offline optimization of the fifth-degree polynomial trajectory parameters, forming an optimal case library consisting of the state variables and decision variables. The optimal trajectory correction rule in the new case scenario was explored, and a trajectory key parameter update function was established. Thus, existing case trajectories were directly called in similar case scenarios, and the lane changing and obstacle avoidance trajectories were updated according to state variables such as vehicle speed and position in new case scenarios, thus the case library was updated. The comparative test results show that the trajectory performance of proposed method is basically consistent with that of the fifth-degree polynomial online optimization method, and is significantly better than that of A* and Rapidly-exploring Random Trees-star(RRT*) methods. Specifically, the maximum curvature of the trajectories from the proposed method and the fifth-degree polynomial online optimization method are both 0.001 2 m-1, and the maximum lateral acceleration of the trajectories from the two methods are both 1.245 7 m/s2 in the similar case scenario. While in the new case scenario, the maximum curvature of the trajectories from the two methods are both 0.001 4 m-1, and the maximum lateral acceleration is 1.194 9 and 1.1365 m/s2, respectively. Meanwhile, compared with the three comparative methods of the fifth-degree polynomial online optimization, A* and RRT*, the proposed method has the smallest planning time. The planning time in the new case scenario and the similar case is only 0.001 1 and 0.000 1 s, respectively.

, authors=Xibo FANG, Yigao NING**, Xuan ZHAO, Meng ZHOU, authorsList=Xibo FANG, Yigao NING, Xuan ZHAO, Meng ZHOU, authorCompany=null, correspAuthors=Yigao NING, 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=1282029994264215606, articleId=1282029992330641458, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=案例推理机制下智能汽车换道避障轨迹规划方法, columnId=1149733271510106222, journalTitle=中国安全科学学报, columnName=公共安全, runingTitle=null, highlight=null, articleAbstract=

为解决智能汽车换道避障轨迹规划难以兼顾性能优化与实时性的问题,提出基于案例推理(CBR)机制的智能汽车换道避障轨迹规划方法。利用五次多项式轨迹参数离线优化得到典型场景下的最优轨迹,形成状态量与决策量相对应的最优案例库,探明新案例场景下最优轨迹修正规律,建立轨迹关键参数更新函数,从而在相似案例场景下直接调用现有案例轨迹,在新案例场景下根据车速、位置等状态量在线更新换道避障轨迹,并进行案例库更新。对比测试结果表明:所提出方法与五次多项式在线优化方法的轨迹性能基本一致,且明显优于A*和快速随机探索树星(RRT*)方法。其中,所提出方法与五次多项式在线优化方法在相似案例场景下,所得轨迹最大曲率均为0.001 2 m-1,最大侧向加速度均为1.245 7 m/s2;在新案例场景下,两者轨迹最大曲率均为0.001 4 m-1,而最大侧向加速度分别为1.194 9和1.136 5 m/s2。同时,所提出方法与五次多项式在线优化、A*和RRT*这3种对比方法相比,具有最小的规划耗时,新案例场景下的规划耗时为0.001 1 s,而相似案例场景下的规划耗时仅为0.000 1 s。

, authors=房熙博, 宁一高讲师**, 赵轩教授, 周猛, authorsList=房熙博, 宁一高讲师, 赵轩教授, 周猛, authorCompany=null, correspAuthors=宁一高讲师, authorNote=

房熙博(2000—),男,甘肃庆阳人,硕士研究生,主要研究方向为智能客车动力学及其运动控制、无人驾驶智能系统。E-mail:

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** 宁一高(1990—),男,陕西凤翔人,博士,讲师,主要从事车辆动力学与控制、智能车辆方面的研究。E-mail:
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房熙博(2000—),男,甘肃庆阳人,硕士研究生,主要研究方向为智能客车动力学及其运动控制、无人驾驶智能系统。E-mail:

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房熙博(2000—),男,甘肃庆阳人,硕士研究生,主要研究方向为智能客车动力学及其运动控制、无人驾驶智能系统。E-mail:

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案例推理机制下智能汽车换道避障轨迹规划方法
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房熙博 , 宁一高讲师 ** , 赵轩教授 , 周猛
中国安全科学学报 | 公共安全 2025,35(7): 209-217
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中国安全科学学报 |公共安全 2025 , 35 (7) : 209 -217
案例推理机制下智能汽车换道避障轨迹规划方法
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房熙博 , 宁一高讲师** , 赵轩教授, 周猛
作者信息
  • 长安大学 汽车学院,陕西 西安 710018
通讯作者:
** 宁一高(1990—),男,陕西凤翔人,博士,讲师,主要从事车辆动力学与控制、智能车辆方面的研究。E-mail:
作者简介:

房熙博(2000—),男,甘肃庆阳人,硕士研究生,主要研究方向为智能客车动力学及其运动控制、无人驾驶智能系统。E-mail:

Lane change and obstacle avoidance trajectory planning method for intelligent vehicles using case-based reasoning mechanism
Xibo FANG , Yigao NING** , Xuan ZHAO, Meng ZHOU
Affiliations
  • School of Automobile, Chang'an University, Xi'an Shaanxi 710018, China
出版时间: 2025-07-28 doi: 10.16265/j.cnki.issn1003-3033.2025.07.0122
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为解决智能汽车换道避障轨迹规划难以兼顾性能优化与实时性的问题,提出基于案例推理(CBR)机制的智能汽车换道避障轨迹规划方法。利用五次多项式轨迹参数离线优化得到典型场景下的最优轨迹,形成状态量与决策量相对应的最优案例库,探明新案例场景下最优轨迹修正规律,建立轨迹关键参数更新函数,从而在相似案例场景下直接调用现有案例轨迹,在新案例场景下根据车速、位置等状态量在线更新换道避障轨迹,并进行案例库更新。对比测试结果表明:所提出方法与五次多项式在线优化方法的轨迹性能基本一致,且明显优于A*和快速随机探索树星(RRT*)方法。其中,所提出方法与五次多项式在线优化方法在相似案例场景下,所得轨迹最大曲率均为0.001 2 m-1,最大侧向加速度均为1.245 7 m/s2;在新案例场景下,两者轨迹最大曲率均为0.001 4 m-1,而最大侧向加速度分别为1.194 9和1.136 5 m/s2。同时,所提出方法与五次多项式在线优化、A*和RRT*这3种对比方法相比,具有最小的规划耗时,新案例场景下的规划耗时为0.001 1 s,而相似案例场景下的规划耗时仅为0.000 1 s。

案例推理(CBR)  /  智能汽车  /  换道避障  /  轨迹规划  /  区别度

To solve the problem of difficulty in balancing trajectory optimization and real-time performance in lane change and obstacle avoidance trajectory planning for intelligent vehicles, a CBR mechanism based intelligent vehicle lane changing and obstacle avoidance trajectory planning method was proposed. The optimal trajectories in typical scenarios were obtained by offline optimization of the fifth-degree polynomial trajectory parameters, forming an optimal case library consisting of the state variables and decision variables. The optimal trajectory correction rule in the new case scenario was explored, and a trajectory key parameter update function was established. Thus, existing case trajectories were directly called in similar case scenarios, and the lane changing and obstacle avoidance trajectories were updated according to state variables such as vehicle speed and position in new case scenarios, thus the case library was updated. The comparative test results show that the trajectory performance of proposed method is basically consistent with that of the fifth-degree polynomial online optimization method, and is significantly better than that of A* and Rapidly-exploring Random Trees-star(RRT*) methods. Specifically, the maximum curvature of the trajectories from the proposed method and the fifth-degree polynomial online optimization method are both 0.001 2 m-1, and the maximum lateral acceleration of the trajectories from the two methods are both 1.245 7 m/s2 in the similar case scenario. While in the new case scenario, the maximum curvature of the trajectories from the two methods are both 0.001 4 m-1, and the maximum lateral acceleration is 1.194 9 and 1.1365 m/s2, respectively. Meanwhile, compared with the three comparative methods of the fifth-degree polynomial online optimization, A* and RRT*, the proposed method has the smallest planning time. The planning time in the new case scenario and the similar case is only 0.001 1 and 0.000 1 s, respectively.

case-based reasoning(CBR)  /  intelligent vehicles  /  lane change and obstacle avoidance  /  trajectory planning  /  difference degree
房熙博, 宁一高讲师, 赵轩教授, 周猛. 案例推理机制下智能汽车换道避障轨迹规划方法. 中国安全科学学报, 2025 , 35 (7) : 209 -217 . DOI: 10.16265/j.cnki.issn1003-3033.2025.07.0122
Xibo FANG, Yigao NING, Xuan ZHAO, Meng ZHOU. Lane change and obstacle avoidance trajectory planning method for intelligent vehicles using case-based reasoning mechanism[J]. China Safety Science Journal, 2025 , 35 (7) : 209 -217 . DOI: 10.16265/j.cnki.issn1003-3033.2025.07.0122
  • 国家自然科学基金资助(52402492; 52372375)
  • 中国博士后科学基金资助(2023M730358)
  • 陕西省自然科学基础研究计划项目(2024JC-YBQN-0564)
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2025年第35卷第7期
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doi: 10.16265/j.cnki.issn1003-3033.2025.07.0122
  • 接收时间:2025-02-14
  • 首发时间:2026-07-09
  • 出版时间:2025-07-28
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  • 收稿日期:2025-02-14
  • 修回日期:2025-05-15
基金
国家自然科学基金资助(52402492; 52372375)
中国博士后科学基金资助(2023M730358)
陕西省自然科学基础研究计划项目(2024JC-YBQN-0564)
作者信息
    长安大学 汽车学院,陕西 西安 710018

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** 宁一高(1990—),男,陕西凤翔人,博士,讲师,主要从事车辆动力学与控制、智能车辆方面的研究。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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