Road Terrain Recognition Based on Tire Noise for Autonomous Vehicle

Road Terrain Recognition Based on Tire Noise for Autonomous Vehicle | 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 Road Terrain Recognition Based on Tire Noise for Autonomous Vehicle Dongsheng Yang, Dongmin Zhang, Yi Yuan, Lizhi He, Zhaoyu Lei, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4610716/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 Choosing different driving modes according to different road terrains can effectively improve the driving safety, pass-ability and comfort. However, there still remains some challenge on accurate and robust road terrain recognition using deep learning in complex environment. In this paper, we proposed an end-to-end tire noise recognition residual network (TNResNet) together with a time-frequency attention module, which can be used to capture time-frequency information of tire noise signal for road terrains recognition. Five different roads including asphalt road, cement road, grass road, mud road and sand road were tested by our method, whose performance was compared with other machine learning and deep learning methods such as Decision Tree, K-Nearest Neighbors, Support Vector Machine, Long Short-Term Memory, Convolutional Neural Network, and Artificial Intelligence Model. Experimental results show that our proposed TNResNet has the best performance among the mentioned methods, and its validation classification accuracy reaches 99.48%. This method shows remarkable application value in automatic road terrain identification of autonomous vehicles. Road recognition Tire noise Deep learning Mel spectrogram Classification 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-4610716","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":320293897,"identity":"50026653-2faf-463a-81b5-0fdc2e212d71","order_by":0,"name":"Dongsheng Yang","email":"","orcid":"","institution":"Automotive Product Strategy \u0026 New Technology Research Institute, BYD Auto Industry Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Yang","suffix":""},{"id":320293901,"identity":"633d042a-8e4d-4efd-8a36-5e582c009889","order_by":1,"name":"Dongmin 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