A Practical Approach to Multivariate Time Series Anomaly Detection in Automotive Bus Systems Testing

A Practical Approach to Multivariate Time Series Anomaly Detection in Automotive Bus Systems Testing | 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 A Practical Approach to Multivariate Time Series Anomaly Detection in Automotive Bus Systems Testing Tobias Suciu, Jürgen Bock This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7778102/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 modern vehicles and their testing procedures generates vast amounts of multivariate time series data, making manual anomaly detection during automotive testing increasingly challenging. This article investigates the application of deep learning algorithms for automated anomaly detection in automotive bus data collected during dynamic driving scenarios. Three distinct architectures are implemented and compared: a CNN-based forecasting approach (DeepAnT), an LSTM-based model (LSTM-AD), and a Convolutional Autoencoder (CAE). Real-world driving data collected across various scenarios, ranging from normal operation to extreme maneuvers, is employed. Through evaluation across seven distinct test scenarios, findings reveal that while each architecture demonstrates specific strengths, their effectiveness varies significantly based on anomaly type and driving context. DeepAnT shows the most consistent performance across different scenarios, while LSTM-AD achieves superior detection capability for complex temporal patterns, particularly in scenarios involving coordinated changes across multiple features. The CAE excels at identifying pronounced deviations but shows limitations in detecting subtle anomalies. This study demonstrates that while deep learning models effectively detect anomalies in automotive time series data, their practical implementation requires careful consideration of specific use cases, emphasizing the critical role of data preprocessing and threshold calculation in ensuring reliable anomaly detection. Anomaly Detection Multivariate Time Series Automotive Systems Testing Unsupervised Anomaly Detection 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. 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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-7778102","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":539386314,"identity":"b4c7088b-462b-4fb1-b161-cda23745f875","order_by":0,"name":"Tobias Suciu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4UlEQVRIiWNgGAWjYJCCA0CcwMbAwPgAyODhI05LAlgLswFICxtx9gC1AEk2CRCboBb5GckPD/P+sMnjYz/+rPJrjp0MGwPzw0c38GgxuJFmcJgnIa2YjSfH7LbstmSgw9iMjXPwaZHIYQBqOZzYxpDDdltyGzNQCw+bND4t8jNgWvifPyuW3FZPWAvDDZgWiQQzxo/bDhPWYnDmmcHBOWlAv0i8MZZm3Hach42ZgF/k25Mff3hjY5Mn35/+8OPPbdX2/OzNDx/jdRgyYOYBk8QqBwHGH6SoHgWjYBSMghEDAIkNQ+wGzrvYAAAAAElFTkSuQmCC","orcid":"","institution":"Technische Hochschule Ingolstadt","correspondingAuthor":true,"prefix":"","firstName":"Tobias","middleName":"","lastName":"Suciu","suffix":""},{"id":539386315,"identity":"e1f20c19-323b-48b5-8e54-ab2ae53284c7","order_by":1,"name":"Jürgen Bock","email":"","orcid":"","institution":"Technische Hochschule Ingolstadt","correspondingAuthor":false,"prefix":"","firstName":"Jürgen","middleName":"","lastName":"Bock","suffix":""}],"badges":[],"createdAt":"2025-10-04 07:23:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7778102/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7778102/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95162926,"identity":"c4153913-1f9b-4a02-9f94-d95634ff120c","added_by":"auto","created_at":"2025-11-05 04:04:50","extension":"json","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4685,"visible":true,"origin":"","legend":"","description":"","filename":"eab71a69290f40ef98b7f17c6fa75b99.json","url":"https://assets-eu.researchsquare.com/files/rs-7778102/v1/c87f93e64a7e0e09c811bf40.json"},{"id":108718060,"identity":"b088666a-d9dc-4343-998c-722102847580","added_by":"auto","created_at":"2026-05-07 15:27:02","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":17477211,"visible":true,"origin":"","legend":"","description":"","filename":"MVTSADinautomotivetesting.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7778102/v1_covered_9352cdbe-95ba-4195-ac53-f66f498413b2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Practical Approach to Multivariate Time Series Anomaly Detection in Automotive Bus Systems Testing","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":"Anomaly Detection, Multivariate Time Series, Automotive Systems Testing, Unsupervised Anomaly Detection","lastPublishedDoi":"10.21203/rs.3.rs-7778102/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7778102/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The increasing complexity of modern vehicles and their testing procedures generates vast amounts of multivariate time series data, making manual anomaly detection during automotive testing increasingly challenging. 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