Neural domains and game strategies in collision risk mapping in algorithms for safe control in multi-autonomous ship situations

Neural domains and game strategies in collision risk mapping in algorithms for safe control in multi-autonomous ship situations | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Neural domains and game strategies in collision risk mapping in algorithms for safe control in multi-autonomous ship situations J. Lisowski This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6283193/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As maritime transport technology has shifted towards the greater use of autonomous ships, the safety requirements for their movement are growing. A gap in the scope of the direct representation of the collision risk in the autonomous object control algorithm exists. In this study, intelligent methods for autonomous control engineering of maritime objects were developed to assess the risk of collision in multi-object situations. These methods included crisis management before collisions to determine the optimal and safe ship trajectories. The value of the collision risk was determined from the object domains generated by the neural network or its three appropriate mathematical models. The basis for these considerations was neural dynamic control and risk game control. Simulations of the control algorithms performed on examples of real navigation situations under different environmental conditions of object movement enabled the assessment of their effectiveness in safe control. The most effective algorithm in good traffic conditions was the neural dynamic control algorithm, whereas the risk game control algorithm enabled effective noncooperative and cooperative control in restricted traffic conditions of autonomous objects. The results from this study provide greater awareness that the use of intelligent control methods can improve the safety of maritime navigation. Physical sciences/Engineering Physical sciences/Mathematics and computing Intelligent control Game control Risk assessment Autonomous navigation safety Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6283193","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":440660898,"identity":"700fe02e-6638-470a-9998-e72544b979ca","order_by":0,"name":"J. Lisowski","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACxgbmhgMghgQz8wGIEIh6gFcLI0wLWwJCSwIBe8CUBAOPAXFamBsYGw/+qDhsL9nO803qxi8GOb4bCWwP8GkBOewwz5nDibOZebdJ5/YxGEveSGA3IKiFse12ghxYSw9D4gagLRKEtBz82XbbXo6Z5xlISz1RWg7wtt1mnM3Mwyad84MhwYCglmawX/4nzmxmM7bObZAwnHnmYTtevxi2Nx/++KMizV7i/OGHt3P+2MjzHU8+9uADPi3NKHa2SYBJPBoYGORRuX/AJBteLaNgFIyCUTDiAACDcFDLrPAtgQAAAABJRU5ErkJggg==","orcid":"","institution":"Gdynia Maritime University","correspondingAuthor":true,"prefix":"","firstName":"J.","middleName":"","lastName":"Lisowski","suffix":""}],"badges":[],"createdAt":"2025-03-22 11:08:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6283193/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6283193/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100364159,"identity":"436084e3-9af6-4dc4-b50b-cf1afcb90e9e","added_by":"auto","created_at":"2026-01-16 07:52:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":986112,"visible":true,"origin":"","legend":"","description":"","filename":"ScientificReportsv0.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6283193/v1_covered_7c08d63c-38e7-4b74-99ea-2d5ea338fe80.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Neural domains and game strategies in collision risk mapping in algorithms for safe control in multi-autonomous ship situations","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Intelligent control, Game control, Risk assessment, Autonomous navigation safety","lastPublishedDoi":"10.21203/rs.3.rs-6283193/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6283193/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs maritime transport technology has shifted towards the greater use of autonomous ships, the safety requirements for their movement are growing. A gap in the scope of the direct representation of the collision risk in the autonomous object control algorithm exists. In this study, intelligent methods for autonomous control engineering of maritime objects were developed to assess the risk of collision in multi-object situations. These methods included crisis management before collisions to determine the optimal and safe ship trajectories. The value of the collision risk was determined from the object domains generated by the neural network or its three appropriate mathematical models. The basis for these considerations was neural dynamic control and risk game control. Simulations of the control algorithms performed on examples of real navigation situations under different environmental conditions of object movement enabled the assessment of their effectiveness in safe control. The most effective algorithm in good traffic conditions was the neural dynamic control algorithm, whereas the risk game control algorithm enabled effective noncooperative and cooperative control in restricted traffic conditions of autonomous objects. The results from this study provide greater awareness that the use of intelligent control methods can improve the safety of maritime navigation.\u003c/p\u003e","manuscriptTitle":"Neural domains and game strategies in collision risk mapping in algorithms for safe control in multi-autonomous ship situations","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 20:19:10","doi":"10.21203/rs.3.rs-6283193/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"086be567-3741-4d0d-a25a-67aa6e512dca","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46916178,"name":"Physical sciences/Engineering"},{"id":46916179,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-12T13:55:00+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 20:19:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6283193","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6283193","identity":"rs-6283193","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}