Article(id=1151591713954427148, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1151591705854751239, articleNumber=1003-3033(2024)05-0238-09, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.05.0910, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1699113600000, receivedDateStr=2023-11-05, revisedDate=1708185600000, revisedDateStr=2024-02-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1752490460145, onlineDateStr=2025-07-14, pubDate=1716825600000, pubDateStr=2024-05-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752490460145, onlineIssueDateStr=2025-07-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752490460145, creator=13701087609, updateTime=1752490460145, updator=13701087609, issue=Issue{id=1151591705854751239, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='5', pageStart='1', pageEnd='251', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752490458214, creator=13701087609, updateTime=1757398693384, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1172178336315985942, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1151591705854751239, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1172178336315985943, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1151591705854751239, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=238, endPage=246, ext={EN=ArticleExt(id=1151591714340303118, articleId=1151591713954427148, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Emergency ship maneuvering method for crossing encounter situation under immediate danger threat, columnId=1149733268699918866, journalTitle=China Safety Science Journal(CSSJ), columnName=Emergency technology and management, runingTitle=null, highlight=null, articleAbstract=

To prevent collision accidents caused by ships failing to take timely and effective avoidance actions due to unclear situation awareness,an emergency ship maneuvering method aimed at addressing urgent dangerous threats was proposed. Based on the data from collision accident investigation reports,the ship encounter process was reconstructed. Considering the uncertainty and randomness of the ship collision risk,a bell-shaped grey cloud model was developed to obtain PCR in real-time and reveal its temporal evolution characteristics. The Kriging spatial interpolation algorithm was used to obtain the perception and graph representation of PCR between two ships. Furthermore,an avoidance decision-making assessment index was proposed to provide emergency maneuvering methods for steering collision avoidance. The validations of the collision accident in the crossing situation indicated that steering avoidance could effectively avoid a close-quarters situation if the give-way ship had taken action by the rudder in a crossing situation and turned starboard by 112.5° before the time of closest point of approach (TCPA) was 10 min. Furthermore,if the give-way ship did not take any action,the stand-on ship should turn to starboard by 45° before the TCPA was 4 min to effectively avoid immediate danger. This study proposes a fine-grained accident data-driven emergency ship-handling method that can be implemented in conjunction with traditional radar mapping and nautical experience ship-handling methods.

, correspAuthors=Shenping HU, 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, authorCompany=null, fund=null, authors=null, authorsList=Jianjun WU, Yan CHEN, Qinghua ZHU, Shenping HU), CN=ArticleExt(id=1151591741804606299, articleId=1151591713954427148, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=紧迫危险威胁下交叉相遇局面应急操船方法, columnId=1149733268855108116, journalTitle=中国安全科学学报, columnName=应急技术与管理, runingTitle=null, highlight=null, articleAbstract=

为预防船舶因会遇态势感知不明、未能及时采取有效避让行动而导致的碰撞事故,提出面向紧迫危险威胁的应急操船方法。首先基于碰撞事故调查报告数据,反演船舶会遇过程;其次考虑船舶碰撞危险度的不确定性和随机性,构建钟形灰云模型,获得实时碰撞风险概率(PCR),揭示时序下碰撞风险演化特征;然后引入克里金空间插值算法,实现会遇2船碰撞风险感知与图谱表达;最后提出避让决策评估指数和转向避碰应急操船方法。交叉相遇局面碰撞事故验证结果表明:交叉会遇局面下单纯转向避让,让路船对正横后来船在最近会遇时间(TCPA)10 min前向右转向112.5°可有效避免紧迫局面;若让路船无行动,直航船在TCPA 4 min前向右转向45°可有效避免紧迫危险。文中所提事故数据驱动的精细应急操船方法,可与传统的雷达标绘和航海经验的操船方法相结合实施。

, correspAuthors=胡甚平, authorNote=null, correspAuthorsNote=
**胡甚平(1974—),男,湖北通城人,博士,教授,主要从事载运工具运用工程与安全工程的研究。E-mail:
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吴建军 (1984—),男,河南项城人,博士,副教授,主要从事船舶营运安全风险及控制、海洋运输安全韧性等方面的研究。E-mail:

胡甚平 教授

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Study on collision avoidance in busy waterways by using AIS data[J]. Ocean Engineering, 2010, 37(5/6):483-490., articleTitle=Study on collision avoidance in busy waterways by using AIS data, refAbstract=null), Reference(id=1172490878179488500, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1151591713954427148, doi=null, pmid=null, pmcid=null, year=2015, volume=68, issue=2, pageStart=291, pageEnd=307, url=null, language=null, rfNumber=[2], rfOrder=1, authorNames=LAZAROWSKA A, journalName=The Journal of Navigation, refType=null, unstructuredReference=LAZAROWSKA A. Ship's trajectory planning for collision avoidance at sea based on ant colony optimization[J]. 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紧迫危险威胁下交叉相遇局面应急操船方法
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吴建军 1, 2 , 陈炎 1 , 朱清华 1 , 胡甚平 1, 2, **
中国安全科学学报 | 应急技术与管理 2024,34(5): 238-246
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中国安全科学学报 | 应急技术与管理 2024, 34(5): 238-246
紧迫危险威胁下交叉相遇局面应急操船方法
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吴建军1, 2 , 陈炎1, 朱清华1, 胡甚平1, 2, **
作者信息
  • 1 上海海事大学 商船学院,上海 201306
  • 2 船舶运输控制系统国家工程研究中心,上海 200135
  • 吴建军 (1984—),男,河南项城人,博士,副教授,主要从事船舶营运安全风险及控制、海洋运输安全韧性等方面的研究。E-mail:

    胡甚平 教授

通讯作者:

**胡甚平(1974—),男,湖北通城人,博士,教授,主要从事载运工具运用工程与安全工程的研究。E-mail:
Emergency ship maneuvering method for crossing encounter situation under immediate danger threat
Jianjun WU1, 2 , Yan CHEN1, Qinghua ZHU1, Shenping HU1, 2, **
Affiliations
  • 1 Merchant Marine College,Shanghai Maritime University,Shanghai 201306,China
  • 2 National Engineering Research Center for Ship Transportation Control System,Shanghai 200135,China
出版时间: 2024-05-28 doi: 10.16265/j.cnki.issn1003-3033.2024.05.0910
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为预防船舶因会遇态势感知不明、未能及时采取有效避让行动而导致的碰撞事故,提出面向紧迫危险威胁的应急操船方法。首先基于碰撞事故调查报告数据,反演船舶会遇过程;其次考虑船舶碰撞危险度的不确定性和随机性,构建钟形灰云模型,获得实时碰撞风险概率(PCR),揭示时序下碰撞风险演化特征;然后引入克里金空间插值算法,实现会遇2船碰撞风险感知与图谱表达;最后提出避让决策评估指数和转向避碰应急操船方法。交叉相遇局面碰撞事故验证结果表明:交叉会遇局面下单纯转向避让,让路船对正横后来船在最近会遇时间(TCPA)10 min前向右转向112.5°可有效避免紧迫局面;若让路船无行动,直航船在TCPA 4 min前向右转向45°可有效避免紧迫危险。文中所提事故数据驱动的精细应急操船方法,可与传统的雷达标绘和航海经验的操船方法相结合实施。

紧迫危险  /  交叉相遇局面  /  应急操船  /  灰云模型  /  碰撞风险概率(PCR)

To prevent collision accidents caused by ships failing to take timely and effective avoidance actions due to unclear situation awareness,an emergency ship maneuvering method aimed at addressing urgent dangerous threats was proposed. Based on the data from collision accident investigation reports,the ship encounter process was reconstructed. Considering the uncertainty and randomness of the ship collision risk,a bell-shaped grey cloud model was developed to obtain PCR in real-time and reveal its temporal evolution characteristics. The Kriging spatial interpolation algorithm was used to obtain the perception and graph representation of PCR between two ships. Furthermore,an avoidance decision-making assessment index was proposed to provide emergency maneuvering methods for steering collision avoidance. The validations of the collision accident in the crossing situation indicated that steering avoidance could effectively avoid a close-quarters situation if the give-way ship had taken action by the rudder in a crossing situation and turned starboard by 112.5° before the time of closest point of approach (TCPA) was 10 min. Furthermore,if the give-way ship did not take any action,the stand-on ship should turn to starboard by 45° before the TCPA was 4 min to effectively avoid immediate danger. This study proposes a fine-grained accident data-driven emergency ship-handling method that can be implemented in conjunction with traditional radar mapping and nautical experience ship-handling methods.

immediate danger  /  crossing situation  /  emergency ship maneuvering  /  gray-cloud model  /  potential collision risk (PCR)
吴建军, 陈炎, 朱清华, 胡甚平. 紧迫危险威胁下交叉相遇局面应急操船方法. 中国安全科学学报, 2024 , 34 (5) : 238 -246 . DOI: 10.16265/j.cnki.issn1003-3033.2024.05.0910
Jianjun WU, Yan CHEN, Qinghua ZHU, Shenping HU. Emergency ship maneuvering method for crossing encounter situation under immediate danger threat[J]. China Safety Science Journal(CSSJ), 2024 , 34 (5) : 238 -246 . DOI: 10.16265/j.cnki.issn1003-3033.2024.05.0910
船舶碰撞事故是海上交通运输事故的主要类型之一。船舶避让行动过晚或避让幅度过小导致的碰撞事故时常发生。《1972年国际海上避碰规则》(简称《避碰规则》)对会遇过程中2船的避让行动给出指导性意见,但未明确量化行动细则。相对一段时期内,基于雷达标绘手段的避碰方式为航海实务提供有力支撑,但其量化避让幅度时仅能确保船舶在某种距离上驶过,未考虑在会遇过程中的碰撞风险程度。因此,需要一种考虑船舶碰撞危险的应急操船方法来辅助船舶避碰。
在现有风险评估研究方面,MOU Junming等[1]采用最近会遇距离(Distance Closest Point of Approach, DCPA)、最近会遇时间(Time Closest Point of Approach, TCPA)和航向夹角,建立实时碰撞风险评估模型。LAZAROWSKA[2]和ZHEN Rong[3]等针对多船避碰决策与碰撞风险评估问题,分别从船基视角和交通管制视角给出建议。在船舶避碰研究方面,马杰[4]和RONG Hao[5]等通过会遇船舶自动识别系统信息时空分析,识别船舶在会遇过程中的避让时机和避让行为。ZHU Qinghua等[6]采用雷达视窗方法,对船舶会遇态势及避碰行动进行时空分析。HE Yixiong等[7]基于船舶领域和船舶操纵分离法模型量化分析会遇阶段,提出转向避让建议。马杰等[8]通过结合速度障碍和人工势场,构建了适用于受限水域内避碰决策模型。WANG Huanxin等[9]认为,碰撞事故的严重程度与船舶类型、船员资质、船龄等因素密切相关。WENG Jinxian等[10]研究表明:船舶碰撞频率与交通特性和环境条件相关,其中,船舶接近角、相对速度对避碰能力有显著影响。付珊珊等[11]研究发现,不安全航速、疏忽瞭望、未履行航行义务等因素是事故的关键共性因素。受限于环境信息和自身经验差异,驾驶员对避让时机和幅度把握不准将加剧船舶碰撞风险,必须精细化研究不同环境情况下的避让行动。王欣等[12]利用船舶操纵分离法模型和自适应控制算法,仿真分析开阔水域下近距离船舶会遇情形。DU Lei等[13]利用会遇船舶速度障碍评估在不同会遇阶段下让路船运动状态,为直航船采取避让提供参考。
当前,基于船舶会遇风险的避碰决策研究已从会遇过程的动态评估走向算法智能决策;同时,船舶实际运行仍处于辅助驾驶员操船决策阶段。但对于辅助应急操船问题还缺少必要关注。由于驾驶员自身经验差异,特别是在面临2船应各自采取最有利于避碰行动的紧迫危险阶段,驾驶员对不同方位的来船采取截然不同的避碰措施。有必要探究碰撞风险动态演化进而量化在未及时了解来船动态状态下采取应急的转向措施,以及指导船员为避免碰撞局面采取的操船行动。因此,笔者拟针对不同会遇案例下船舶风险感知差异和大数据下某会遇态势的整体风险感知,引入钟形灰云模型和克里金空间插值,刻画极坐标系下的船舶碰撞风险动态变化过程,探究紧迫危险威胁下的风险感知图谱,从而量化交叉相遇局面2船安全避让时机和幅度,以期为快速量化决策,形成有效的应急操船方法提供新思路。
为探究紧迫危险威胁下的应急操船方法,需研究2船从远距离至非常接近的过程中的风险方位趋势,船舶碰撞事故的形成过程是典型的整个会遇过程风险的集中体现。为充分辨识事故发生过程,采用海事管理机构针对水上交通事故发布的权威性、结论性调查文本,即船舶碰撞事故调查报告中的数据作为支撑。事故调查报告包含事故概况、船舶情况、事故经过、海况及通航环境和事故责任分析等,可从中提取出AIS信息再现船舶碰撞前2船会遇过程。
为详细表达船舶的航向航速的连续动态变化情况,需要进行插值处理。结合航海实际,提出处理事故调查报告中航向航速信息的两点三次Hermite插值方法,求解公式如下:

H ( x ) = y 0 1 - 2 × x - x 0 x 0 - x 1 x - x 1 x 0 - x 1 2 +

y 1 1 - 2 × x - x 1 x 1 - x 0 2 + y ' 0 x - x 0 x - x 1 x 0 - x 1 2 +

y ' 1 ( x - x 1 ) x - x 0 x 1 - x 0 2
式中:(x0y0)和(x1y1)分别为插值起点和终点的坐标;y0'和y1'分别为起点和终点处的导数。
事故调查报告显示的碰撞前经纬度信息有限,航迹拟合方法的效果与实际误差较大。以碰撞事故海域的经纬度为原点,根据碰撞前任意时刻的航速、航向信息推算船舶航迹,求取公式如下:
X i = X 0 + ( T 0 - T i ) · V ¯ · s i n θ Y i = Y 0 + ( T 0 - T i ) · V ¯ · c o s θ
式中:(X0Y0)为T0时刻的船位;(XiYi)为推算出的Ti时刻的航迹坐标; V -T0Ti间的平均航速;θ为会遇中船舶真航向的反方向。
在船舶避碰的研究中,感知船舶碰撞危险(Risk of Collision, ROC)常用DCPA和TCPA,公式如下:
V r = V o 2 + V t 2 - 2 · | V o · V t | · c o s ( θ t - θ o ) D C P A = | Y t - X t · t a n θ r | t a n 2 θ r + 1 T C P A = ( X t 2 + Y t 2 ) - D C P A 2 V r
式中:Vrθr分别为2船相对运动速度和方向;Voθo分别为本船速度和方向;Vtθt分别为目标船速度和方向;(XtYt)为笛卡尔坐标系下目标船相对于本船的横纵坐标。
船舶碰撞危险度(Collision Risk Index, CRI)为发生碰撞可能性大小的度量,是避碰决策重要的依据和评判标准。
引入负指数函数获得CRI,公式[3]如下:

C R I = a · e x p ( α · | D C P A | ) +

b · e x p β · | T C P A | )
式中: α β为调整系数,取决于海上业务应用的具体特征;ab为权重系数。
航海实践中,雷达上显示的数据是四维结构(θr,DCPA,TCPA)。涵盖2船经纬度、航向、航速等信息的DCPA和TCPA可降维成CRI,因此,船员风险感知模型简化为三维结构(θr,CRI)。
CRI的权重系数是由海事专家意见估计得到,存在不确定性;DCPA和TCPA为基于碰撞事故调查报告的情景反演得到的某一时刻避碰参数,呈现出随机性;因此,碰撞前某时刻CRI同时具有不确定性和随机性特征。进一步将碰撞危险度转化成碰撞风险概率(Potential Collision Risk, PCR),形成面向碰撞风险感知的三维结构模型(θr,PCR) [6]
为解决CRI的随机性和模糊性,构建具有广义钟形隶属度的钟形灰云模型。灰云模型的正向云发生器能实现定性概念到定量描述的转换,逆向云发生器可实现由定量描述到定性概念的转换。
步骤如下:根据已知隶属云中相当数量的云滴分布,通过逆向云发生器确定数字特征,从而构成正向云发生器,产生具有确定度的云滴D(xiμ(xi)),进而将CRI推理成PCR[14]
钟形灰云模型:GL7(ExLxRxqlqrEnHe) 。参数定义为:期望值Ex、云边界值[LxRx],期望左右确定值[qlqr],熵En、超熵He
满足如下关系:
E n = ( R x - L x ) 6 H e = E n c μ ( x i ) = e x p - ( x i - E x ) 2 2 · ( E ' n ) 2 P C R i = μ ( x i ) · C R I i
式中:c为给定的常数,取c=7,En'为以En为期望值和He为标准差的正态随机数。
为感知同一方位区域来船的整体风险,针对PCR模型结构的三维空间性(θr,PCR),引入克里金插值算法。已知若干离散点(xiyi)的某一属性观测值zi=z(xiyi)的条件下,估计空间上任意一点(xy)的属性值。空间任意一点处的值z=z(xy)由区域平均值E(z)和随机偏差R(xy)组成,偏差的方差为常数VAR[R(xy)]=σ2
表达式如下[15]:
z ^ 0 = i = 1 n w i z i
式中: z ^ 0为点(x0y0)处的估计值,即 z ^ 0 = z ( x 0 y 0 );wi为权重系数,利用所有已知点加权求和估计未知点;zi为已知点(xiyi)的观测值。wi要求满足点(x0y0)处的估计值 z ^ 0与真实值z0之差最小,也希望估计值 z ^ 0与真实值z0之差的方差最小,即
E ( z ^ 0 - z 0 ) = 0 m i n w i ( V A R ( z ^ 0 - z 0 ) )
通常,2船海上互见情况下,若距离小于6n mile,且DCPA小于安全会遇距离时,认为碰撞危险正在形成;若不采取任何行动,至相距3n mile时,认为紧迫局面正在形成;若继续接近到1n mile,则认为紧迫危险正在形成。
随着船舶日益大型化和高速化,单纯考虑DCPA对会遇阶段划分的误差偏大,需要融入船舶尺度和速度提高量化估算的合理性。鉴于事故案例下碰撞前时间与TCPA的一致性,采用碰撞前约20、10、4 min分别作为2船会遇过程划分的3个分界点[16]。因此,将船舶会遇过程划分为4个阶段(图1)。
1) 让路船避碰行动。《避碰规则》中第15条规定,当2艘机动船交叉相遇致有构成碰撞危险时,有他船在本船右舷的船舶应给他船让路,如当时环境许可,还应避免横越他船的前方。当2船小角度交叉相遇局面构成碰撞危险时,在直航船保向保速情况下,让路船采取向左转向的避让行动往往并不利于协调避让,条件允许下向右转向是避让右前方来船的最有效行动。当2船在大角度交叉相遇局面且构成碰撞危险时,条件允许下尽早采取大幅度右转进行避让,必要时左转并行或旋回一周避让来船。
2) 直航船避碰行动。《避碰规则》中第17条规定,当2船中的一船应给另一船让路时,另一船应保持航向航速,即在交叉相遇局面下形成碰撞危险时,直航船负有保持航向航速的义务。当直航船发觉让路船显然没有按照规则采取适当行动时,可独自采取操纵行动。交叉相遇局面下让路船未按照规则采取适当行动时,直航船对左舷的船舶采取向右转向的避让行动。
1) 转向操纵评估指数。定义: K C ' r为对避让决策有效性的度量,表示船舶采取转向角度ΔC后,目标船的相对运动方向斜率。其表达式如下:
K C ' r = S t · c o s C t - S 0 · c o s ( C 0 + Δ C ) S t · s i n C t - S 0 · s i n ( C 0 + Δ C )
式中:ΔC为转向幅度;S0St分别为让路船和直航船速度;C0Ct分别为让路船和直航船真航向。
2) 让路船避让行动模型。小角度交叉相遇局面下让路船避让行动的几何效果如图2所示。图2中,Cg为让航船真航向,Cs为直路船真航向,Cr为来船相对运动方向,Cg'为让路船转向后真航向,Cr'为转向后来船相对运动方向。显然,在小角度交叉相遇中让路船向右转向幅度ΔC越大,斜率 K C ' r越小,DCPA越大,避让效果越明显。
3) 直航船避让行动模型。直航船对左舷的船舶采取向右转向的避让行动的几何效果如图3所示。图3中,θ为相对运动线与直航船船首向夹角,Cs'为直路船转向后真航向。模型显示,①随着ΔC逐渐增大, K C ' r呈现出先减小后增大的趋势;②当ΔC为90°- θ时,直航船转向后相对运动线Cr'的距离最远,即DCPA达到最大值;③当ΔC大于90°- θ时, K C ' r逐渐增大,DCPA逐渐减小。
基于海事主管机关发布的88起交叉相遇局面碰撞事故案例(构成碰撞危险时有他船位于一船右舷6~ 112.5°),还原碰撞前30 min 2船会遇过程。
根据会遇过程的4阶段划分规则,碰撞前20、10、4 min对应的PCR分别为0.2、0.5、0.8左右。对碰撞前时刻t下的TCPA进行聚类分析,也得到相同的PCR。将TCPA与碰撞前时刻t等效应用于2船交叉相遇局面的风险推理,得到风险概率值。上述关键时刻对应的灰云模型如下:

M - 20 = G L 7 ( 0.2554,0.1310,0.4389,0.2368 0.3331,0.0513,0.0073 ) ; M - 10 = G L 7 ( 0.5091,0.3826,0.6850,0.4865 0.5810,0.0504,0.0072 ) ; M - 4 = G L 7 ( 0.7964,0.7111,0.9386,0.7893 0.8604,0.0379,0.0054

让路船雷达视窗下显示的对直航船风险感知分布如图4a图4c所示。
结果显示,①TCPA为20 min时,直航船处于让路船右舷6~109°区间,距离0.78~9.79 n mile,PCR最大约为0.25;②会遇2船相互驶近至TCPA为10 min时,紧迫局面正在形成,直航船位于让路船右舷1~106°,距离0.26~4.59 n mile,PCR最大约为0.51;③会遇2船驶近至TCPA为4 min时,紧迫危险正在形成,直航船位于让路船左舷9~右舷115°,距离0.05~1.43 n mile,PCR最大约为0.80。
直航船雷达视窗下显示的对让路船风险感知分布如图4d图4f所示。结果显示,①致有构成碰撞危险时,让路船约位于直航船左舷3~左舷102°;②会遇2船相互驶近至TCPA为10 min时,让路船位于直航船右舷9~左舷97°;③会遇2船驶近至TCPA为4 min时,让路船位于直航船右舷10~左舷105°。
采用克里金空间插值方法得到连续的风险感知图谱,以及对案例目标船的整体风险感知值。船舶风险感知图谱如图5所示。
1) 小角度交叉相遇局面。对于小角度交叉相遇局面,提取位于让路船右舷6~67.5°的直航船,采用空间插值法得到让路船3个时间点的风险感知分布,如图5a图5c。结果显示:①TCPA为20 min时,让路船整体风险感知值约为0.25,直航船同等风险值方位约在让路船左舷11~右舷77°;②TCPA为10 min时,让路船整体风险感知值为0.51,直航船同等风险值方位约在让路船左舷4~右舷73°;③TCPA为4 min时,让路船整体风险感知值为0.80,与直航船最近距离约0.11n mile,风险等值方位约为右舷78°。
2) 弱交叉相遇局面。在右舷6~67.5°内存在毗邻对遇局面的弱交叉情形(他船位于本船右舷6~30°),让路船不必要进行较大幅度改变航向。针对弱交叉相遇情形进行2船数据提取,采用空间插值法得到让路船3个时间点的风险感知分布,如图5d图5f所示。结果显示:①TCPA为20 min时,让路船整体风险感知值约为0.25,直航船同等风险值方位在左舷7~右舷39°;②TCPA为10 min时,让路船整体风险感知值约为0.50,同等风险值方位约在让路船左舷9~右舷41°;③TCPA为4 min时,让路船整体风险感知值约为0.80,与直航船最近距离约0.47n mile,风险等值方位约为右舷36°。
3) 大角度交叉相遇局面。对于大角度交叉相遇局面,提取位于让路船右舷67.5~112.5°的直航船,采用空间插值法得到让路船3个时间点的风险感知分布,如图5g图5i所示。结果显示:①TCPA为20 min时,让路船整体风险感知值约为0.25,直航船同等风险方位约在右舷115°;②TCPA为10 min时,让路船整体风险感知值为0.50,直航船同等风险方位约为右舷112°;③TCPA为4 min时,让路船整体风险感知值为0.79,风险等值方位约为右舷113°。
采用空间插值法得到直航船3个时间节点的风险感知图,如图5j图5l所示。结果显示:①TCPA为20 min时,与直航船风险等值的让路船活动方位约为左舷107~右舷12°,平均风险方位为左舷47°;②TCPA为10 min时,与直航船风险等值的让路船活动方位为左舷112°~右舷11°,平均风险方位约为左舷51°;③TCPA为4 min时,风险等值方位为左舷115~右舷10°,平均风险方位约为左舷52°。
由碰撞事故案例集的风险感知图谱可知:大多数直航船与让路船的相对方位无明显变化,与事故调查揭示的事故直接原因是让路船在碰撞危险和紧迫局面阶段未采取大幅度转向行动的结论一致。因此,船舶应根据来船相对方位及实时风险,及时采取转向避让行动。结合《避碰规则》和会遇阶段划分,提出船舶避让的决策准则和行动方案。
对于让路船,应考虑:①当2船从远距离相互驶近至TCPA为20 min时,存在碰撞危险,让路船避让责任形成,应尽早且大幅度地采取转向措施,以避免2船进入紧迫局面阶段;②若让路船在碰撞危险阶段未采取明显的避让措施,将进入紧迫局面阶段:即使让路船采取明显的避让措施,2船也无法在安全距离外驶过让清。
对于直航船,应考虑:①当2船从远距离相互驶近至TCPA约为20 min时,直航船维持航行状态的责任与让路船主动避让责任同时形成。直航船应尽可能地维持航速和航向,给让路船提供避让措施参考。②当TCPA接近10 min,即将进入紧迫局面,直航船若发觉让路船显然没有遵照《避碰规则》采取适当行动时,可独自采取操纵行动以避免碰撞:按照《避碰规则》:若当时环境许可,直航船不应对在本船左舷的船舶采取向左转向的规定,采取向右转向。在交叉相遇局面的碰撞危险和紧迫局面阶段,《避碰规则》对让路船和直航船的避让行动负有定性要求,而紧迫危险阶段,会遇2船可根据实际情况采取违背规则的避让行动。
以形成紧迫局面前完成避让措施且维持整体碰撞风险感知为目标,结合克里金插值结果和《避碰规则》中让路船和直航船的避让决策原则,在未能及时明确来船动态情况下,提出避让条件允许时兼顾转向时机和幅度的避让行动方案。
1) 让路船行动方案。①在TCPA为10 min前,小角度交叉局面的让路船对右舷6~ 30°和30~ 67.5°的直航船尽可能分别向右转向至少41和73°,使DCPA分别平均上升1.13和0.52n mile;大角度交叉局面的让路船对右舷67.5~ 112.5°的直航船尽可能向右转向至少112°,使DCPA平均上升约0.29n mile,必要时向左并行或旋回一周避让,以避免进入紧迫局面阶段。②在TCPA为4 min前,小角度交叉局面的让路船对右舷6~ 30°的直航船和右舷30~ 67.5°的直航船分别向右转向至少36和78°,使DCPA分别平均上升0.26和0.17n mile;大角度交叉局面的让路船对右舷67.5~ 112.5°的直航船向右转向至少113°,使DCPA平均上升约0.10n mile,以避免2船进入紧迫危险阶段。否则,2船可不再遵守《避碰规则》行动规定,根据当时情况采取最有利于避碰的行动。
2) 直航船行动方案。①在TCPA为10 min前,若发觉让路船并没有采取明显避让行动时,直航船可向右转向至少39°,使DCPA平均上升约0.68n mile,可避免2船进入紧迫局面阶段,必要时配合加速增大避让效果。②在TCPA为4 min前,若发觉让路船仍未采取明显避让行动时,直航船向右转向至少38°,使DCPA平均上升约0.20n mile,则最有可能避免碰撞发生,必要时配合加速增大避让效果。
利用避让决策评估指数对船舶避碰行动方案进行效果分析,很明显,小角度交叉相遇中让路船向右转向幅度越大,DCPA越大,避让效果越明显。
直航船若发觉让路船无明显避让行动,可采取独自向右转向,随着转向幅度逐渐增大,避让效果呈先减小后增大的趋势;当转向幅度等于相对运动线舷角的余角时,DCPA达到最大,避让效果最佳。
1) 在船舶交叉会遇情况下,若船舶仅采取转向方式避让来船,让路船对正横后来船在TCPA 10 min前向右转向112.5°可有效避免紧迫局面;若让路船无行动,直航船在TCPA 4 min前向右转向45°可有效避免紧迫危险。
2) 船舶交叉相遇局面下,考虑船舶碰撞事故数据驱动的精细应急操船方法,能对船舶近距离避让,特别是紧迫危险威胁下的避让有参考,从而能补充以航海经验为主导和雷达标绘为指南的传统避碰行动。
3) 根据简单的来船方位信息进行大数据视角下的应急转向量化探索,整体应急操船的转向行动量化分为2个阶段,当前阶段是应急转向的第1阶段量化,在此基础上第2阶段的实际操船决策优化过程及其方法将在后续工作中拓展并深入研究。
  • 2023年度教育部人文社会科学研究规划基金资助(23YJAZH157)
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2024年第34卷第5期
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doi: 10.16265/j.cnki.issn1003-3033.2024.05.0910
  • 接收时间:2023-11-05
  • 首发时间:2025-07-14
  • 出版时间:2024-05-28
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  • 收稿日期:2023-11-05
  • 修回日期:2024-02-18
基金
2023年度教育部人文社会科学研究规划基金资助(23YJAZH157)
作者信息
    1 上海海事大学 商船学院,上海 201306
    2 船舶运输控制系统国家工程研究中心,上海 200135

通讯作者:

**胡甚平(1974—),男,湖北通城人,博士,教授,主要从事载运工具运用工程与安全工程的研究。E-mail:
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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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