Advancements in AI-Driven Navigation and Collision Avoidance Systems for Maritime Applications

Advancements in AI-Driven Navigation and Collision Avoidance Systems for Maritime Applications | 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 Research Article Advancements in AI-Driven Navigation and Collision Avoidance Systems for Maritime Applications Ali Elgohary This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8656537/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 The maritime industry is increasingly leveraging artificial intelligence (AI) to enhance navigation safety and mitigate collision risks at sea. This paper provides a comprehensive analysis of recent advancements in AI-driven navigation and collision avoidance systems for Maritime Autonomous Surface Ships (MASS) and conventional vessels. We extend prior work by introducing detailed mathematical modeling, numeric substitution, and MATLAB-based simulations. The study integrates collision risk assessment via Closest Point of Approach (CPA) and Time to CPA (𝑇𝐶𝑃𝐴), trajectory optimization with Model Predictive Control [MPC], Artificial Potential Fields (APF), and cybersecurity anomaly detection using Kalman filters. Results demonstrate that CPA analysis identified a critical near-collision (𝐷𝐶𝑃𝐴 = 8.84 m, 𝑇𝐶𝑃𝐴 = 11.22 s), MPC maintained safe separations > 16 m, APF successfully guided vessels to targets, and spoofed AIS signals were detected with > 95% accuracy. Figures generated from MATLAB simulations illustrate each methodology. This extended work contributes by merging theoretical models with practical simulations, highlighting both technological promise and cybersecurity challenges. 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-8656537","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577918112,"identity":"94d183fd-787a-4f63-a8b7-390595b72de7","order_by":0,"name":"Ali 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This paper provides a comprehensive analysis of recent advancements in AI-driven navigation and collision avoidance systems for Maritime Autonomous Surface Ships (MASS) and conventional vessels. We extend prior work by introducing detailed mathematical modeling, numeric substitution, and MATLAB-based simulations. The study integrates collision risk assessment via Closest Point of Approach (CPA) and Time to CPA (𝑇𝐶𝑃𝐴), trajectory optimization with Model Predictive Control [MPC], Artificial Potential Fields (APF), and cybersecurity anomaly detection using Kalman filters. Results demonstrate that CPA analysis identified a critical near-collision (𝐷𝐶𝑃𝐴 = 8.84 m, 𝑇𝐶𝑃𝐴 = 11.22 s), MPC maintained safe separations \u0026gt; 16 m, APF successfully guided vessels to targets, and spoofed AIS signals were detected with \u0026gt; 95% accuracy. Figures generated from MATLAB simulations illustrate each methodology. 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