Ai-integrated Autonomous Commercial Aircraft Taxiing System

Ai-integrated Autonomous Commercial Aircraft Taxiing System | 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 Ai-integrated Autonomous Commercial Aircraft Taxiing System Eashan Mathur, Apeksha Dongre, Pooja Gupta This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8425857/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 increasing complexity of airport operations and rising global air traffic demand more efficient, safe, and environmentally sustainable ground movement of aircraft. This research proposes an AI-integrated autonomous taxiing guidance system designed to assist pilots in navigating from gate to runway and vice versa, minimizing reliance on conventional Air Traffic Control (ATC) instructions. Unlike fully autonomous systems, this solution retains human involvement by focusing on real-time route optimization, obstacle detection, and pilot guidance, thereby improving operational efficiency while maintaining pilot oversight. The system leverages a hybrid of technologies, including computer vision, LIDAR,RADAR, and machine learning-based predictive analytics, to recommend optimal taxi routes while detecting and responding to obstacles in real time. Augmented reality (AR) interfaces and 3D mapping enhance situational awareness, especially in low-visibility conditions, while automation of routine communication reduces the cognitive load on both pilots and ATC. The system is designed for integration into existing airport infrastructure, making it scalable across a wide range of airport sizes and configurations. Simulation results demonstrate significant improvements in taxiing efficiency, substantial reductions in fuel consumption, and notable decreases in emission levels, contributing toward sustainable airport operations. This hybrid approach preserves human control while leveraging AI for smarter, safer, and more sustainable decision-making, setting the stage for the future of intelligent ground operations in aviation. AI-guided taxiing aircraft ground operations route optimization obstacle detection LIDAR RADAR human-in-the-loop environmental sustainability 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-8425857","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573126161,"identity":"bc79cef2-7635-4570-8b1c-2b8874fae19b","order_by":0,"name":"Eashan Mathur","email":"","orcid":"","institution":"Manipal University jaipur","correspondingAuthor":false,"prefix":"","firstName":"Eashan","middleName":"","lastName":"Mathur","suffix":""},{"id":573126162,"identity":"903db186-f4f6-4b35-a5f2-b80af48ccd95","order_by":1,"name":"Apeksha Dongre","email":"","orcid":"","institution":"Manipal University jaipur","correspondingAuthor":false,"prefix":"","firstName":"Apeksha","middleName":"","lastName":"Dongre","suffix":""},{"id":573126163,"identity":"afd30081-669c-4d83-910a-41c5031d8c94","order_by":2,"name":"Pooja Gupta","email":"data:image/png;base64,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","orcid":"","institution":"Manipal University jaipur","correspondingAuthor":true,"prefix":"","firstName":"Pooja","middleName":"","lastName":"Gupta","suffix":""}],"badges":[],"createdAt":"2025-12-22 14:16:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8425857/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8425857/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100199934,"identity":"4d6382dd-b605-47aa-96e0-e693d059d48c","added_by":"auto","created_at":"2026-01-14 04:32:47","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":5401,"visible":true,"origin":"","legend":"","description":"","filename":"9354764311834452991639a7c49cc1fc.json","url":"https://assets-eu.researchsquare.com/files/rs-8425857/v1/3969691d24dadd16d3c65dc1.json"},{"id":104315491,"identity":"db354e0f-3312-4e6b-8c57-e5e354aaaac4","added_by":"auto","created_at":"2026-03-10 11:57:58","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":517812,"visible":true,"origin":"","legend":"","description":"","filename":"AIINTEGRATEDAUTONOMOUSCOMMERCIALAIRCRAFTTAXIINGSYSTEMDiscoverAi1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8425857/v1_covered_d68330b7-952c-48b9-be3f-275ea8f4342b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAi-integrated Autonomous Commercial Aircraft Taxiing System\u003c/p\u003e","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":"AI-guided taxiing, aircraft ground operations, route optimization, obstacle detection, LIDAR, RADAR, human-in-the-loop, environmental sustainability","lastPublishedDoi":"10.21203/rs.3.rs-8425857/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8425857/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The increasing complexity of airport operations and rising global air traffic demand more efficient, safe, and environmentally sustainable ground movement of aircraft. This research proposes an AI-integrated autonomous taxiing guidance system designed to assist pilots in navigating from gate to runway and vice versa, minimizing reliance on conventional Air Traffic Control (ATC) instructions. Unlike fully autonomous systems, this solution retains human involvement by focusing on real-time route optimization, obstacle detection, and pilot guidance, thereby improving operational efficiency while maintaining pilot oversight. The system leverages a hybrid of technologies, including computer vision, LIDAR,RADAR, and machine learning-based predictive analytics, to recommend optimal taxi routes while detecting and responding to obstacles in real time. Augmented reality (AR) interfaces and 3D mapping enhance situational awareness, especially in low-visibility conditions, while automation of routine communication reduces the cognitive load on both pilots and ATC. 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