Auto Beam: Automatic Beam Adjuster For Enhanced Vehicle Safety Using Deep Learning

Auto Beam: Automatic Beam Adjuster For Enhanced Vehicle Safety Using Deep Learning | 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 Auto Beam: Automatic Beam Adjuster For Enhanced Vehicle Safety Using Deep Learning Aditya Kumar, Aryan Singh, Akanksha Malakar, Sarvagya Gupta, Sonali Vyas This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4522446/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 In today’s scenario, automobiles have been a huge part of our day-to-day life and also have a huge impact on the nation's economy. But with the increase in the number of automobiles, there is a rise in accidents too. The use of a high beam in front of a coming vehicle, creates a glare on the eyes of the driver which makes him/her partially blind for a few microseconds, which is enough for an accident to take place. This paper proposes an Automatic High to low beam adjuster that adjusts the beam according to the presence of the car in front of it. We are using deep learning and a masking approach independently for detection of a vehicle in front of the primary vehicle whose beam we would be adjusting. The AI model would then be connected with an Arduino by which we would be converting digital signals to electrical signals. The scope of the project is to be able to detect headlights of oncoming vehicles and adjust the beam of the vehicle (high to low and vice versa) without the driver’s intervention as it would be of great help to the people driving at night, aged people and people with vision problems like cataracts etc. It could bring a whole new dimension of traffic control and road safety by detecting the headlights of the vehicles using dynamic footage recorded by sensors in real time. This paper will also be of crucial importance in reducing the number of accidents therefore preventing mishaps, saving lives and preventing financial losses. The future scopes could be integrating Raspberry Pi and using it with an Arduino and a camera to develop and test the fully functional product which can then be installed in vehicles to control accidents. The camera that would be used to detect oncoming vehicles could also detect emergency factors like sudden accident-like situations and could take preventive measures to reduce the impact or the probability of an accident. OpenCV Masking Dilation Convolutional Neural Network (CNN) Arduino Digital signals Electrical Signals Vehicle 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-4522446","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":313615035,"identity":"e6efd8b3-68c0-4315-aacd-b7099ad1cf03","order_by":0,"name":"Aditya Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9UlEQVRIiWNgGAWjYFCCxAYgkcAgz97/8AGQxcNHhJbGBpAWw54zzAYgLWyEtSQwgrUw3PBhkwDxCWrhb09uf8xTk5bPOIP3WOXXHDsZNgbmh49u4NEiceZhYzPPsRzLdum+tNuy25KBDmMzNs7BZ82NRKAWtgoDxjkHzG5LbmMGauFhk8anRR6s5V+FAcONBLNiyW31hLUYgLTwtuUAteSYMX7cdpiwFkOgX2bO7UszMOw5lizNuO04DxszAb/IHU9/8OHNt2QDefbmgx9/bqu252dvfvgYr/eBgIkHymAGM5gJKAcBxh/ojFEwCkbBKBgFyAAAE9hMC8UQOkgAAAAASUVORK5CYII=","orcid":"","institution":"School of Computer Science, University Of Petroleum and Energy Studies (UPES), Dehradun.","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"","lastName":"Kumar","suffix":""},{"id":313615036,"identity":"820ebdb4-99a7-4e73-b8bf-25273b2e0d8b","order_by":1,"name":"Aryan Singh","email":"","orcid":"","institution":"School of Computer Science, University Of Petroleum and Energy Studies (UPES), Dehradun.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Aryan","middleName":"","lastName":"Singh","suffix":""},{"id":313615037,"identity":"c72841fe-48f6-42ce-a006-c707d4ec69a9","order_by":2,"name":"Akanksha Malakar","email":"","orcid":"","institution":"School of Computer Science, University Of Petroleum and Energy Studies (UPES), Dehradun.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Akanksha","middleName":"","lastName":"Malakar","suffix":""},{"id":313615038,"identity":"868ee07d-1155-45c0-87e8-c69b535c4789","order_by":3,"name":"Sarvagya Gupta","email":"","orcid":"","institution":"School of Computer Science, University Of Petroleum and Energy Studies (UPES), Dehradun.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sarvagya","middleName":"","lastName":"Gupta","suffix":""},{"id":313615039,"identity":"cffb4adb-0085-4c76-92eb-e9037347227e","order_by":4,"name":"Sonali Vyas","email":"","orcid":"","institution":"School of Computer Science, University Of Petroleum and Energy Studies (UPES), Dehradun.","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Sonali","middleName":"","lastName":"Vyas","suffix":""}],"badges":[],"createdAt":"2024-06-03 14:26:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4522446/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4522446/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59783165,"identity":"1a3eec7f-8479-4de1-8636-32c997ae495a","added_by":"auto","created_at":"2024-07-06 21:31:31","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":740588,"visible":true,"origin":"","legend":"","description":"","filename":"headlightfinalformatJIISFinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4522446/v1_covered_9a4cd48b-d2b5-498d-afd6-d61559e78b7a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Auto Beam: Automatic Beam Adjuster For Enhanced Vehicle Safety Using Deep Learning","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":"OpenCV, Masking, Dilation, Convolutional Neural Network (CNN), Arduino, Digital signals, Electrical Signals, Vehicle Safety","lastPublishedDoi":"10.21203/rs.3.rs-4522446/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4522446/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn today\u0026rsquo;s scenario, automobiles have been a huge part of our day-to-day life and also have a huge impact on the nation's economy. But with the increase in the number of automobiles, there is a rise in accidents too. The use of a high beam in front of a coming vehicle, creates a glare on the eyes of the driver which makes him/her partially blind for a few microseconds, which is enough for an accident to take place. This paper proposes an Automatic High to low beam adjuster that adjusts the beam according to the presence of the car in front of it. We are using deep learning and a masking approach independently for detection of a vehicle in front of the primary vehicle whose beam we would be adjusting. The AI model would then be connected with an Arduino by which we would be converting digital signals to electrical signals. The scope of the project is to be able to detect headlights of oncoming vehicles and adjust the beam of the vehicle (high to low and vice versa) without the driver\u0026rsquo;s intervention as it would be of great help to the people driving at night, aged people and people with vision problems like cataracts etc. It could bring a whole new dimension of traffic control and road safety by detecting the headlights of the vehicles using dynamic footage recorded by sensors in real time. This paper will also be of crucial importance in reducing the number of accidents therefore preventing mishaps, saving lives and preventing financial losses. The future scopes could be integrating Raspberry Pi and using it with an Arduino and a camera to develop and test the fully functional product which can then be installed in vehicles to control accidents. The camera that would be used to detect oncoming vehicles could also detect emergency factors like sudden accident-like situations and could take preventive measures to reduce the impact or the probability of an accident.\u003c/p\u003e","manuscriptTitle":"Auto Beam: Automatic Beam Adjuster For Enhanced Vehicle Safety Using Deep Learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 18:54:12","doi":"10.21203/rs.3.rs-4522446/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":"b9f1b97e-702a-4b48-9323-405ebbbba955","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-06T21:23:24+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-18 18:54:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4522446","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4522446","identity":"rs-4522446","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}