Intelligent Detection of Alcohol-Impaired Driving via Variational Autoencoders and SHAP-based Interpretation

Intelligent Detection of Alcohol-Impaired Driving via Variational Autoencoders and SHAP-based Interpretation | 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 Intelligent Detection of Alcohol-Impaired Driving via Variational Autoencoders and SHAP-based Interpretation Fouzi Harrou, Abdelkader Dairi, Abdelhakim Dorbane, Ying Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7884491/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Drunk driving poses a serious risk to road safety. This study introduces a semi-supervised approach to detect intoxicated drivers by combining Variational Autoencoders (VAEs) for feature extraction with a One-Class Support Vector Machine (OCSVM) for anomaly detection. The method is trained only on data from sober drivers, making it suitable for limited-label scenarios. Model predictions are explained using SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-Agnostic Explanations), which consistently identify alcohol concentration as the most influential feature. The proposed framework is evaluated using a publicly available dataset from IEEE Dataport, which includes sensor readings from alcohol gas sensors, facial temperature data, and pupil measurements captured by a Raspberry Pi camera. The VAE-OCSVM model achieves strong results, with an F1-score of 98%, outperforming standard clustering-based methods as well as classical semi-supervised statistical monitoring approaches, including Principal Component Analysis (PCA)-based Hotelling \((T^2)\) , PCA-based Squared Prediction Error (SPE), and Independent Component Analysis (ICA)-based SPE. Bootstrap-based confidence intervals are computed to assess the robustness of performance metrics. Drunk driving detection semi-supervised learning anomaly detection variational autoencoders and Multisensor data fusion Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 06 Apr, 2026 Submission checks completed at journal 02 Apr, 2026 First submitted to journal 01 Apr, 2026 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-7884491","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618078546,"identity":"93a5bb5f-15bc-451b-9f9a-cd169ebd901c","order_by":0,"name":"Fouzi Harrou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABD0lEQVRIie3Qv0oDMRzA8d9xkFsirieH9hVyFPwD1bxKwkGn2wQRHBo4uKkPELByr3DiC1wJxOXofINDJyeFuBVcjG2hiqZ2dMh3+IUf5EMgAD7fPywSdjBoVptZn4C3ENx8IYH8PNEuBNYkxDuRaNw3c3jqndwqnQzaC15VIpi/lkD3hYPglhAGz+lkNhwmeZfxWkOY3pXAZfM7oXFOGAMVSIyPk9yEvEaAkr1y8yc/XrGksYQuyakZ8aqE6N0S2nOTVFjClwQ6xYUGFFoS1C6C9SUwojKJUXY2bh/7tebFwWQW83sXiYqHt8W1Opc4nHYLfXNYFWpqXq4G9MhBVpFvWyDsiLfd9/l8Pt8ffQDBJFi2nrrFCgAAAABJRU5ErkJggg==","orcid":"","institution":"King Abdullah University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Fouzi","middleName":"","lastName":"Harrou","suffix":""},{"id":618078547,"identity":"90239c47-e3ee-44f4-bc89-890e3f2c51f2","order_by":1,"name":"Abdelkader Dairi","email":"","orcid":"","institution":"Université des Sciences et de la Technologie d'Oran Mohamed Boudiaf","correspondingAuthor":false,"prefix":"","firstName":"Abdelkader","middleName":"","lastName":"Dairi","suffix":""},{"id":618078548,"identity":"826805a3-5a50-4930-8162-c22f72b9e9bb","order_by":2,"name":"Abdelhakim Dorbane","email":"","orcid":"","institution":"Université de ain Témouchent","correspondingAuthor":false,"prefix":"","firstName":"Abdelhakim","middleName":"","lastName":"Dorbane","suffix":""},{"id":618078549,"identity":"dd790d1d-cec3-47f9-aef1-cd9b58df8c66","order_by":3,"name":"Ying Sun","email":"","orcid":"","institution":"King Abdullah University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-10-17 08:38:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7884491/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7884491/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106726075,"identity":"fa81164d-8c65-4371-962d-c9f68797f2dc","added_by":"auto","created_at":"2026-04-12 18:35:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1156756,"visible":true,"origin":"","legend":"","description":"","filename":"CleanTexSourceRevisedManuscript412026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7884491/v1_covered_da04e1a0-4b96-4b44-8b44-75f9139b37a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intelligent Detection of Alcohol-Impaired Driving via Variational Autoencoders and SHAP-based Interpretation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Drunk driving detection, semi-supervised learning, anomaly detection, variational autoencoders, and Multisensor data fusion","lastPublishedDoi":"10.21203/rs.3.rs-7884491/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7884491/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDrunk driving poses a serious risk to road safety. 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