{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T19:26:57Z","timestamp":1774121217487,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T00:00:00Z","timestamp":1638921600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The role of a service that is dedicated to road weather analysis is to issue forecasts and warnings to users regarding roadway conditions, thereby making it possible to anticipate dangerous traffic conditions, especially during the winter period. It is important to define pavement conditions at all times. In this paper, a new data acquisition approach is proposed that is based upon the analysis and combination of two sensors in real time by nanocomputer. The first sensor is a camera that records images and videos of the road network. The second sensor is a microphone that records the tire\u2013pavement interaction, to characterize each surface\u2019s condition. The two low-cost sensors were fed to different deep learning architectures that are specialized in surface state analysis; the results were combined using an evidential theory-based data fusion approach. This study is a proof of concept, to test an evidential approach for improving classification with deep learning, applied to only two sensors; however, one could very well add more sensors and make the nanocomputers communicate together, to analyze a larger urban environment.<\/jats:p>","DOI":"10.3390\/s21248218","type":"journal-article","created":{"date-parts":[[2021,12,8]],"date-time":"2021-12-08T23:30:00Z","timestamp":1639006200000},"page":"8218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Evidential Data Fusion for Characterization of Pavement Surface Conditions during Winter Using a Multi-Sensor Approach"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4781-2148","authenticated-orcid":false,"given":"Issiaka","family":"Diaby","sequence":"first","affiliation":[{"name":"Centre d\u2019Applications et de Recherches en T\u00e9l\u00e9d\u00e9tection (CARTEL), D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Qu\u00e9bec, QC J1K2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-7530","authenticated-orcid":false,"given":"Micka\u00ebl","family":"Germain","sequence":"additional","affiliation":[{"name":"Centre d\u2019Applications et de Recherches en T\u00e9l\u00e9d\u00e9tection (CARTEL), D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Qu\u00e9bec, QC J1K2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6992-5093","authenticated-orcid":false,"given":"Kalifa","family":"Go\u00efta","sequence":"additional","affiliation":[{"name":"Centre d\u2019Applications et de Recherches en T\u00e9l\u00e9d\u00e9tection (CARTEL), D\u00e9partement de G\u00e9omatique Appliqu\u00e9e, Universit\u00e9 de Sherbrooke, Qu\u00e9bec, QC J1K2R1, Canada"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/JSEN.2014.2364854","article-title":"Road surface status classification using spectral analysis of NIR camera images","volume":"15","author":"Jonsson","year":"2015","journal-title":"IEEE Sens. J."},{"key":"ref_2","unstructured":"(2021, November 12). U.S. Department of Transportation Federal Highway Administration, Available online: https:\/\/highways.dot.gov\/."},{"key":"ref_3","unstructured":"(2021, November 12). 2007\u20132016 10-Year Averages Analyzed by Booz Allen Hamilton, based on NHTSA Data in U.S. Department of Transportation Federal Highway Administration, Available online: https:\/\/ops.fhwa.dot.gov\/weather\/q1_roadimpact.htm."},{"key":"ref_4","unstructured":"Morin, E. (2010). Optimisation de la Gestion de L\u2019information M\u00e9t\u00e9o-Routi\u00e8re pour le Minist\u00e8re des Transports du Qu\u00e9bec\u2014Direction de l\u2019Estrie, M\u00e9moire de Ma\u00eetrise; Universit\u00e9 de Sherbrooke."},{"key":"ref_5","unstructured":"Khoderagha, N. (2019). Outil Innovant pour la Gestion des Routes, M\u00e9moire de Ma\u00eetrise, Universit\u00e9 du Qu\u00e9bec."},{"key":"ref_6","first-page":"297","article-title":"Application of GIS technology in pavement management systems","volume":"71","author":"Zagvozda","year":"2019","journal-title":"J. Croat. Assoc. Civ. Eng."},{"key":"ref_7","unstructured":"Tarleton, J. (2021, November 12). Critical conditions: The weather plays a vital role in road safety and traffic management applications. Intertraffic World Annual Showcase 2015. Traffic Manag., Available online: https:\/\/www.vaisala.com\/sites\/default\/files\/documents\/WEA-RDS-G-TTI_Intertraffic%20World_2014_final.pdf."},{"key":"ref_8","unstructured":"(2021, November 12). Teconer. Available online: https:\/\/www.teconer.fi\/en\/."},{"key":"ref_9","unstructured":"(2021, November 12). High Sierra. Available online: https:\/\/hsierra.com\/product\/remote-road-surface-condition-sensor-series-5433\/."},{"key":"ref_10","unstructured":"(2021, November 12). Lufft\/OTT Hydromet. Available online: https:\/\/www.otthydromet.com\/en\/Ott\/p-lufft-starwis-umb-non-invasive-road-condition-sensor-5-m-distance\/8711.U55."},{"key":"ref_11","unstructured":"(2021, November 12). Campbell Scientific. Available online: https:\/\/www.campbellsci.ca\/ccfc."},{"key":"ref_12","unstructured":"Chagnon, F. (2008). Caract\u00e9risation des \u00c9tats de Surface par T\u00e9l\u00e9d\u00e9tection Infrarouge Thermique Multispectrale: Contribution \u00e0 L\u2019\u00e9tude des Conditions de Viabilit\u00e9 Hivernale. [Ph.D. Thesis, Universit\u00e9 de Sherbrooke]."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5746","DOI":"10.1080\/01431161.2012.671554","article-title":"Classification of road surface status using a 94 GHz dual-channel polarimetric radiometer","volume":"33","author":"Song","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.sna.2019.111540","article-title":"Road surface condition detection utilizing resonance frequency and optical technologies","volume":"297","author":"Gui","year":"2019","journal-title":"Sens. Actuators A Phys."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.neucom.2021.03.066","article-title":"An evidential classifier based on Dempster-Shafer theory and deep learning","volume":"450","author":"Tong","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Den\u0153ux, T., Lef\u00e8vre, E., Liu, Z., and Pichon, F. (2021). Fusion of Evidential CNN Classifiers for Image Classification. Belief Functions: Theory and Applications. BELIEF, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-88601-1"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Shafer, G. (1976). A mathematical theory of evidence, In Foundations of Probability Theory, Statistical Inference, and Statistical Theories of Science, Springer.","DOI":"10.1007\/978-94-010-1436-6_11"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1111\/j.2517-6161.1968.tb00722.x","article-title":"A generalization of Bayesian inference","volume":"30","author":"Dempster","year":"1968","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1016\/j.patcog.2009.05.018","article-title":"Real-time traffic sign recognition from video by class-specific discriminative features","volume":"43","author":"Ruta","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.compeleceng.2015.01.002","article-title":"A lane detection approach based on intelligent vision","volume":"42","author":"Yi","year":"2015","journal-title":"Comput. Electr. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.autcon.2017.11.010","article-title":"Evaluation of pavement surface drainage using an automated image acquisition and processing system","volume":"86","author":"Mataei","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1080\/15472450.2021.1944860","article-title":"Multilevel weather detection based on images: A machine learning approach with histogram of oriented gradient and local binary pattern-based features","volume":"25","author":"Khan","year":"2021","journal-title":"J. Intell. Transp. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1541\/ieejias.129.761","article-title":"Detection of road surface conditions using tire noise from vehicles","volume":"129","author":"Kongrattanaprasert","year":"2009","journal-title":"IEEJ Trans. Ind. Appl."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.apacoust.2016.08.027","article-title":"Investigating generation mechanisms of tyre\/road noise by speed exponent analysis","volume":"115","author":"Winroth","year":"2017","journal-title":"Appl. Acoust."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1016\/j.measurement.2019.06.034","article-title":"Identification and mapping of asphalt surface deterioration by tyre-pavement interaction noise measurement","volume":"146","author":"Sigcha","year":"2019","journal-title":"Measurement"},{"key":"ref_26","unstructured":"Bochra, G., and Safia, S. (2019). Les R\u00e9seaux de Neurones Convolutionels (CNN) Pour la Classification des Images Associ\u00e9es Aux Places de Stationnement d\u2019un Parc de V\u00e9hicule, M\u00e9moire de Ma\u00eetrise, Djlali Bounaama."},{"key":"ref_27","unstructured":"Simonnet, E. (2019). R\u00e9seaux de Neurones Profonds Appliqu\u00e9s \u00e0 la Compr\u00e9hension de la Parole. [Ph.D. Thesis, Le Mans Universit\u00e9]."},{"key":"ref_28","unstructured":"Pan, G., Fu, L., Yu, R., and Muresan, M. (2019). Winter Road Surface Condition Recognition Using a Pre-Trained Deep Convolutional Neural Network, IEEE."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abdic, I., Fridman, L., Brown, D.E., Angell, W., Reimer, B., Marchi, E., and Schuller, B. (2016, January 5\u20138). Detecting road surface wetness from audio: A deep learning approach. Proceedings of the 23rd International Conference on Pattern Recognition (ICPR 2016), Cancun, Mexico.","DOI":"10.1109\/ICPR.2016.7900169"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"White, F.E. (1991). Data Fusion Lexicon: Fort Belvoir, Defense Technical Information Center.","DOI":"10.21236\/ADA529661"},{"key":"ref_31","unstructured":"Bloch, I., and Maitre, H. (2020, February 10). \u00c9cole Nationale Sup\u00e9rieure des T\u00e9l\u00e9communications-CNRS UMR. Les M\u00e9thodes de Raisonnement dans les Images. Available online: https:\/\/perso.telecom-paristech.fr\/bloch\/VOIR\/poly_voir.pdf."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.engappai.2015.04.012","article-title":"Evidential framework for data fusion in a multi-sensor surveillance system","volume":"43","author":"Reynaud","year":"2015","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bezerra, E.D.C., Teles, A.S., Coutinho, L.R., and da Silva e Silva, F.J. (2021). Dempster-Shafer Theory for modeling and treating uncertainty in IoT applications based on complex event processing. Sensors, 21.","DOI":"10.3390\/s21051863"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Irschik, D., and Stork, W. (2014, January 16\u201317). Road surface classification for extended floating car data. Proceedings of the International Conference on Vehicular Electronics and Safety, Hyderabad, India.","DOI":"10.1109\/ICVES.2014.7063728"},{"key":"ref_35","first-page":"1","article-title":"Road surface state recognition based on SVM optimization and image segmentation processing","volume":"2017","author":"Zhao","year":"2017","journal-title":"J. Adv. Transp."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1177\/0278364920979368","article-title":"Canadian Adverse Driving Conditions Dataset","volume":"40","author":"Pitropov","year":"2021","journal-title":"Int. J. Robot. Res."},{"key":"ref_37","unstructured":"(2021, May 11). Center for Data Innovation. Available online: https:\/\/datainnovation.org\/2018\/08\/detecting-weather-conditions-on-the-road\/."},{"key":"ref_38","unstructured":"(2020, January 20). Association des Pi\u00e9tons et Cyclistes Pont Jacques-Cartier. Available online: flickr.com\/photos\/pontjacquescartier."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1541\/ieejias.130.920","article-title":"Detection of road surface states from tire noise using Neural Network Analysis","volume":"130","author":"Kongrattanaprasert","year":"2010","journal-title":"IEEJ Trans. Ind. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.measurement.2018.06.056","article-title":"A state-of-the-art review of measurement techniques on tire\u2013pavement interaction noise","volume":"28","author":"Li","year":"2018","journal-title":"Measurement"},{"key":"ref_41","unstructured":"Jay, P. (2021, May 11). Understanding and Implementing Architectures of ResNet and ResNeXt for State-of-the-Art Image Classification: From Microsoft to Facebook, Part 1. Available online: https:\/\/medium.com\/@14prakash\/understanding-and-implementing-architectures-of-resnet-and-resnext-for-state-of-the-art-image-cf51669e1624."},{"key":"ref_42","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_43","unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Dai, W., Dai, C., Qu, S., Li, J., and Das, S. (2017, January 5\u20139). Very deep convolutional neural networks for raw waveforms. Proceedings of the 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, USA.","DOI":"10.1109\/ICASSP.2017.7952190"},{"key":"ref_45","unstructured":"Herring, W. (2021, March 22). Audio Classifier Tutorial. Available online: https:\/\/colab.research.google.com\/github\/pytorch\/tutorials\/blob\/gh-pages\/_downloads\/audio_classifier_tutorial.ipynb."},{"key":"ref_46","unstructured":"Abbas, N. (2009). D\u00e9veloppement de Mod\u00e8les de Fusion et de Classification Contextuelle D\u2019images Satellitaires par la Th\u00e9orie de L\u2019\u00e9vidence et la Th\u00e9orie du Raisonnement Plausible et Paradoxal. [Ph.D. Thesis, Universit\u00e9 Des Sciences Et De La Technologie Houari Boumediene]."},{"key":"ref_47","unstructured":"Germain, M. (2006). Classification Multisource par la Fusion \u00c9videntielle avec une Nouvelle Approche Statistique Floue. [Ph.D. Thesis, Universit\u00e9 de Sherbrooke]."},{"key":"ref_48","unstructured":"Smarandache, F., and Dezert, J. (2009). An Introduction to DSmT, American Research Press. Applications and Advances of DSmT for Information Fusion."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"7138","DOI":"10.1109\/TII.2020.2976812","article-title":"DSmT-based fusion strategy for human activity recognition in body sensor networks","volume":"16","author":"Dong","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1080\/19479832.2013.793216","article-title":"Intelligent fire-detection model using statistical color models data fusion with Dezert\u2013Smarandache method","volume":"4","author":"Walia","year":"2013","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Smets, P. (2000, January 10\u201313). Data Fusion in the Transferable Belief Model. Proceedings of the Third International Conference on Information Fusion, Paris, France.","DOI":"10.1109\/IFIC.2000.862713"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8218\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:43:37Z","timestamp":1760168617000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,8]]},"references-count":51,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248218"],"URL":"https:\/\/doi.org\/10.3390\/s21248218","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,8]]}}}