AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres
Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vag...
Gespeichert in:
Veröffentlicht in: | Journal of navigation 2021-09, Vol.74 (5), p.985-1008 |
---|---|
Hauptverfasser: | , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 1008 |
---|---|
container_issue | 5 |
container_start_page | 985 |
container_title | Journal of navigation |
container_volume | 74 |
creator | Vestre, Arnstein Bakdi, Azzeddine Vanem, Erik Engelhardtsen, Øystein |
description | Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels. |
doi_str_mv | 10.1017/S0373463321000357 |
format | Article |
fullrecord | <record><control><sourceid>proquest_crist</sourceid><recordid>TN_cdi_cristin_nora_10852_92524</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><cupid>10_1017_S0373463321000357</cupid><sourcerecordid>2564416544</sourcerecordid><originalsourceid>FETCH-LOGICAL-c384t-6d3ea351ea1a9a2727013b514ee1badba6af9801cf1485ef6039f7eb63f5db143</originalsourceid><addsrcrecordid>eNp1kE1LAzEQhoMoWKs_wJMLnleTzcfuHov4BYKH6jnObiYlZZvUZFvov3eXVnoQD8PAvO_zMjOEXDN6xygr7-eUl1wozgtGKeWyPCETJlSdl2UlT8lklPNRPycXKS0HTyUqOSFfs9d53kBCk3mEmLeh61xywWcGehiFbIEeI_TjDLwZCrpdcikLNosIXXZEYBucAd9itgIfcLONmC7JmYUu4dWhT8nn0-PHw0v-9v78-jB7y1teiT5XhiNwyRAY1FCURUkZbyQTiKwB04ACW1eUtZYNe6NVlNe2xEZxK03DBJ-Sm31uG13qndc-RNCMVrLQdSGL0XG7d6xj-N5g6vUybOJwTdKFVEIwJcXoYr85IaWIVq-jW0HcDVl6fLX-8-qB4QcGVk10ZoHH6P-pH2_qf54</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2564416544</pqid></control><display><type>article</type><title>AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres</title><source>NORA - Norwegian Open Research Archives</source><source>Cambridge University Press Journals Complete</source><creator>Vestre, Arnstein ; Bakdi, Azzeddine ; Vanem, Erik ; Engelhardtsen, Øystein</creator><creatorcontrib>Vestre, Arnstein ; Bakdi, Azzeddine ; Vanem, Erik ; Engelhardtsen, Øystein</creatorcontrib><description>Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.</description><identifier>ISSN: 0373-4633</identifier><identifier>EISSN: 1469-7785</identifier><identifier>DOI: 10.1017/S0373463321000357</identifier><language>eng</language><publisher>Cambridge, UK: Cambridge University Press</publisher><subject>Algorithms ; Automation ; Avoidance behaviour ; Collision avoidance ; Economic analysis ; Economics ; Identification ; Identification systems ; Navigation ; Research methodology ; Risk assessment ; Ship accidents & safety ; Traffic control ; Traffic information</subject><ispartof>Journal of navigation, 2021-09, Vol.74 (5), p.985-1008</ispartof><rights>Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation</rights><rights>Copyright © The Author(s), 2021. Published by Cambridge University Press on behalf of The Royal Institute of Navigation. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>info:eu-repo/semantics/openAccess</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c384t-6d3ea351ea1a9a2727013b514ee1badba6af9801cf1485ef6039f7eb63f5db143</citedby><cites>FETCH-LOGICAL-c384t-6d3ea351ea1a9a2727013b514ee1badba6af9801cf1485ef6039f7eb63f5db143</cites><orcidid>0000-0001-6992-4234</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.cambridge.org/core/product/identifier/S0373463321000357/type/journal_article$$EHTML$$P50$$Gcambridge$$Hfree_for_read</linktohtml><link.rule.ids>164,230,314,776,780,881,26546,27903,27904,55606</link.rule.ids></links><search><creatorcontrib>Vestre, Arnstein</creatorcontrib><creatorcontrib>Bakdi, Azzeddine</creatorcontrib><creatorcontrib>Vanem, Erik</creatorcontrib><creatorcontrib>Engelhardtsen, Øystein</creatorcontrib><title>AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres</title><title>Journal of navigation</title><addtitle>J. Navigation</addtitle><description>Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.</description><subject>Algorithms</subject><subject>Automation</subject><subject>Avoidance behaviour</subject><subject>Collision avoidance</subject><subject>Economic analysis</subject><subject>Economics</subject><subject>Identification</subject><subject>Identification systems</subject><subject>Navigation</subject><subject>Research methodology</subject><subject>Risk assessment</subject><subject>Ship accidents & safety</subject><subject>Traffic control</subject><subject>Traffic information</subject><issn>0373-4633</issn><issn>1469-7785</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>IKXGN</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>3HK</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wJMLnleTzcfuHov4BYKH6jnObiYlZZvUZFvov3eXVnoQD8PAvO_zMjOEXDN6xygr7-eUl1wozgtGKeWyPCETJlSdl2UlT8lklPNRPycXKS0HTyUqOSFfs9d53kBCk3mEmLeh61xywWcGehiFbIEeI_TjDLwZCrpdcikLNosIXXZEYBucAd9itgIfcLONmC7JmYUu4dWhT8nn0-PHw0v-9v78-jB7y1teiT5XhiNwyRAY1FCURUkZbyQTiKwB04ACW1eUtZYNe6NVlNe2xEZxK03DBJ-Sm31uG13qndc-RNCMVrLQdSGL0XG7d6xj-N5g6vUybOJwTdKFVEIwJcXoYr85IaWIVq-jW0HcDVl6fLX-8-qB4QcGVk10ZoHH6P-pH2_qf54</recordid><startdate>20210901</startdate><enddate>20210901</enddate><creator>Vestre, Arnstein</creator><creator>Bakdi, Azzeddine</creator><creator>Vanem, Erik</creator><creator>Engelhardtsen, Øystein</creator><general>Cambridge University Press</general><scope>IKXGN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7SP</scope><scope>7TN</scope><scope>7XB</scope><scope>88I</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>H8D</scope><scope>H96</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>KR7</scope><scope>L.G</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M2P</scope><scope>M7S</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>Q9U</scope><scope>3HK</scope><orcidid>https://orcid.org/0000-0001-6992-4234</orcidid></search><sort><creationdate>20210901</creationdate><title>AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres</title><author>Vestre, Arnstein ; Bakdi, Azzeddine ; Vanem, Erik ; Engelhardtsen, Øystein</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c384t-6d3ea351ea1a9a2727013b514ee1badba6af9801cf1485ef6039f7eb63f5db143</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Automation</topic><topic>Avoidance behaviour</topic><topic>Collision avoidance</topic><topic>Economic analysis</topic><topic>Economics</topic><topic>Identification</topic><topic>Identification systems</topic><topic>Navigation</topic><topic>Research methodology</topic><topic>Risk assessment</topic><topic>Ship accidents & safety</topic><topic>Traffic control</topic><topic>Traffic information</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Vestre, Arnstein</creatorcontrib><creatorcontrib>Bakdi, Azzeddine</creatorcontrib><creatorcontrib>Vanem, Erik</creatorcontrib><creatorcontrib>Engelhardtsen, Øystein</creatorcontrib><collection>Cambridge Journals Open Access</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Oceanic Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection (ProQuest)</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ProQuest Central Basic</collection><collection>NORA - Norwegian Open Research Archives</collection><jtitle>Journal of navigation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Vestre, Arnstein</au><au>Bakdi, Azzeddine</au><au>Vanem, Erik</au><au>Engelhardtsen, Øystein</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres</atitle><jtitle>Journal of navigation</jtitle><addtitle>J. Navigation</addtitle><date>2021-09-01</date><risdate>2021</risdate><volume>74</volume><issue>5</issue><spage>985</spage><epage>1008</epage><pages>985-1008</pages><issn>0373-4633</issn><eissn>1469-7785</eissn><abstract>Economic and technological development has increased the amount, density and complexity of maritime traffic, which has resulted in new challenges. One challenge is conforming to the distinct evasion manoeuvres required by vessels entering into near-collision situations (NCSs). Existing rules are vague and do not precisely dictate which, when and how collision avoidance manoeuvres (CAMs) should be executed. The automatic identification system (AIS) is widely used for vessel monitoring and traffic control. This paper presents an efficient, scalable method for processing large-scale raw AIS data using the closest point of approach (CPA) framework. NCSs are identified to create a database of historical traffic data. Important features describing CAMs are defined, estimated and analysed. Applications on a high-quality real-world data set show promising results for a subset of the identified situations. Future applications may play a significant role in the maritime regulatory framework, navigation protocol compliance evaluation, risk assessment, automatic collision avoidance, and algorithm design and testing for autonomous vessels.</abstract><cop>Cambridge, UK</cop><pub>Cambridge University Press</pub><doi>10.1017/S0373463321000357</doi><tpages>24</tpages><orcidid>https://orcid.org/0000-0001-6992-4234</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0373-4633 |
ispartof | Journal of navigation, 2021-09, Vol.74 (5), p.985-1008 |
issn | 0373-4633 1469-7785 |
language | eng |
recordid | cdi_cristin_nora_10852_92524 |
source | NORA - Norwegian Open Research Archives; Cambridge University Press Journals Complete |
subjects | Algorithms Automation Avoidance behaviour Collision avoidance Economic analysis Economics Identification Identification systems Navigation Research methodology Risk assessment Ship accidents & safety Traffic control Traffic information |
title | AIS-based near-collision database generation and analysis of real collision avoidance manoeuvres |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-24T01%3A40%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_crist&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=AIS-based%20near-collision%20database%20generation%20and%20analysis%20of%20real%20collision%20avoidance%20manoeuvres&rft.jtitle=Journal%20of%20navigation&rft.au=Vestre,%20Arnstein&rft.date=2021-09-01&rft.volume=74&rft.issue=5&rft.spage=985&rft.epage=1008&rft.pages=985-1008&rft.issn=0373-4633&rft.eissn=1469-7785&rft_id=info:doi/10.1017/S0373463321000357&rft_dat=%3Cproquest_crist%3E2564416544%3C/proquest_crist%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2564416544&rft_id=info:pmid/&rft_cupid=10_1017_S0373463321000357&rfr_iscdi=true |