[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127400-en":3,"doc-seo-127400-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127400,962085571259,"Theodora","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Automotive Radar Range Spectrum-Based Road Surface Classification - By Using Machine Learning - paper","Road surface awareness is essential for safe automated driving in both on- and off-road scenarios. This study presents an approach that classifies multiple road surface types using automotive radar range spectra together with machine learning and AI models. Training and testing datasets are constructed by combining range data from different surfaces. A Random Forest classifier identifies four surface categories, and results are validated using radar data from two mounting positions. Under dry conditions, the forward-looking setup reaches 84.5% generalization error, and wet versus dry asphalt differentiation achieves 88.7%.","OPUS-HSO  \nRepositorium der Hochschule Offenburg  \n[https://opus.hs-offenburg.de](https://opus.hs-offenburg.de)  \nAutomotive Radar Range Spectrum-Based Road Surface Classification by Using Machine Learning  \nHima Dominic, Marius Patzer, Marlene Harter  \nZitiervorschlag im APA Stil:  \nDominic, H., Patzer, M., & Harter, M. (2025) . Automotive Radar Range Spectrum-Based Road Surface Classification by Using Machine Learning. Sensors, 25(22), 1–12 . [https://doi.org/10](https://doi.org/10)  \n[.3390/s25226911](.3390/s25226911)  \nAbstract  \nThe awareness of different road surface types is crucial for the safe operation of automated vehicles in on-and off-road modes. This paper focuses on the classification of different road surface types using automotive radar and Machine Learning (ML) Artificial Intelligence (AI) models. This analysis is based on the range spectrum of the backscattered radar signals from different road surfaces. The dataset for training and testing is formed by combining the range data from various road surface types. A Random Forest (RF) classifier is built for the identification and classification of four different road surface types. The proposed method is compared using range data obtained from two different mounting positions of the radar. Under dry conditions, a generalization error of 84.5% in the forward-looking position of the radar is achieved. The proposed classifier is also able to distinguish between wet and dry asphalt surfaces with a generalization error of 88.7% .  \nNutzungsbedingungen  \nDieses Dokument wird unter diesen Bedinungen zur Verfügung gestellt:  \nCreative Commons-CC BY-Namensnennung 4.0 International  \nFür weitere Informationen siehe:  \n[https://creativecommons.org/licenses/by/4.0/deed.de](https://creativecommons.org/licenses/by/4.0/deed.de)  \nKontakt  \nHochschule Offenburg | Bibliothek Badstraße 24  \n77652 Offenburg  \nTelefon: (0781) 205-240  \n[E-Mail: bibliothek@hs-offenburg.de](E-Mail: bibliothek@hs-offenburg.de)[ ](E-Mail: bibliothek@hs-offenburg.de)[www.hs-offenburg.de/bibliothek](www.hs-offenburg.de/bibliothek)  \nArticle  \nAutomotive Radar Range Spectrum-Based Road Surface Classification by Using Machine Learning  \nHima Dominic *, Marius Patzer  and Marlene Harter   \nAcademic Editor: Guang-CaiSun  \nReceived: 5 September 2025  \nRevised: 3 November 2025  \nAccepted: 7 November 2025  \nPublished: 12 November 2025  \nCitation: Dominic, H.; Patzer, M.; Harter, M. Automotive Radar Range Spectrum-Based Road Surface Classification by Using Machine Learning. Sensors 2025, 25, 6911 . [https://doi.org/10.3390/s25226911](https://doi.org/10.3390/s25226911)  \n[Copyright:](Copyright:) © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://creativecommons.org/](https://creativecommons.org/)[ ](https://creativecommons.org/)[licenses/by/4.0/](licenses/by/4.0/)) .  \nInstitute for Unmanned Aerial Systems, Offenburg University, Badstrasse 24, 77652 Offenburg, Germany; [marius.patzer@hs-offenburg.de](marius.patzer@hs-offenburg.de) (M.P.); [marlene.harter@hs-offenburg.de](marlene.harter@hs-offenburg.de) (M.H.)  \n* Correspondence: hima.dominic@hs-offenburg.de  \nAbstract  \nThe awareness of different road surface types is crucial for the safe operation of automated vehicles in on-and off-road modes. This paper focuses on the classification of different road surface types using automotive radar and Machine Learning (ML) Artificial Intelligence (AI) models. This analysis is based on the range spectrum of the backscattered radar signals from different road surfaces. The dataset for training and testing is formed by combining the range data from various road surface types. A Random Forest (RF) classifier is built for the identification and classification of four different road surface types. The proposed method is compared using range data obtained from two different moun","cbCaimBFVneT4Rqj","https://ap.wps.com/l/cbCaimBFVneT4Rqj","pdf",4210851,1,13,"English","en",105,"# Introduction\n## Road surface classification problem and related sensing technologies\n## Radar-based approach and motivation","[{\"question\":\"How is road surface information extracted in this approach?\",\"answer\":\"The method analyzes the range spectrum of backscattered automotive radar signals from different road surfaces and builds datasets from those range data for training and testing.\"},{\"question\":\"Which machine learning model is used for classification?\",\"answer\":\"A Random Forest (RF) classifier is used to identify and classify four different road surface types.\"},{\"question\":\"How is the model evaluated, and what results are reported?\",\"answer\":\"The method compares radar range data collected from two mounting positions. Under dry conditions, it reports 84.5% generalization error in the forward-looking position, and it distinguishes wet versus dry asphalt with 88.7% generalization error.\"}]","Automotive Radar Range Spectrum-Based Road Surface Classification - By Using Machine Learning - paper | PDF",1785938686,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"automotive-radar-range-spectrum-based-road-surface-classification-by-using-machine-learning-paper","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/automotive-radar-range-spectrum-based-road-surface-classification-by-using-machine-learning-paper/127400/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"How is road surface information extracted in this approach?","Question",{"text":76,"@type":77},"The method analyzes the range spectrum of backscattered automotive radar signals from different road surfaces and builds datasets from those range data for training and testing.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning model is used for classification?",{"text":81,"@type":77},"A Random Forest (RF) classifier is used to identify and classify four different road surface types.",{"name":83,"@type":74,"acceptedAnswer":84},"How is the model evaluated, and what results are reported?",{"text":85,"@type":77},"The method compares radar range data collected from two mounting positions. 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