{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T12:13:54Z","timestamp":1777119234138,"version":"3.51.4"},"reference-count":46,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,2]],"date-time":"2022-04-02T00:00:00Z","timestamp":1648857600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Flanders Innovation &amp; Entrepreneurship (VLAIO)","award":["HBC.2017.0999"],"award-info":[{"award-number":["HBC.2017.0999"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Road weather conditions such as ice, snow, or heavy rain can have a significant impact on driver safety. In this paper, we present an approach to continuously monitor the road conditions in real time by equipping a fleet of vehicles with sensors. Based on the observed conditions, a physical road weather model is used to forecast the conditions for the following hours. This can be used to deliver timely warnings to drivers about potentially dangerous road conditions. To optimally process the large data volumes, we show how artificial intelligence is used to (1) calibrate the sensor measurements and (2) to retrieve relevant weather information from camera images. The output of the road weather model is compared to forecasts at road weather station locations to validate the approach.<\/jats:p>","DOI":"10.3390\/s22072732","type":"journal-article","created":{"date-parts":[[2022,4,3]],"date-time":"2022-04-03T06:04:01Z","timestamp":1648965841000},"page":"2732","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Leveraging Artificial Intelligence and Fleet Sensor Data towards a Higher Resolution Road Weather Model"],"prefix":"10.3390","volume":"22","author":[{"given":"Toon","family":"Bogaerts","sequence":"first","affiliation":[{"name":"IDLab-Faculty of Applied Engineering, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium"}]},{"given":"Sylvain","family":"Watelet","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute of Belgium, Ringlaan 3, 1180 Brussels, Belgium"}]},{"given":"Niko","family":"De Bruyne","sequence":"additional","affiliation":[{"name":"Verhaert Masters in Innovation, Hogenakkerhoekstraat 21, 9150 Kruibeke, Belgium"}]},{"given":"Chris","family":"Thoen","sequence":"additional","affiliation":[{"name":"Verhaert Masters in Innovation, Hogenakkerhoekstraat 21, 9150 Kruibeke, Belgium"}]},{"given":"Tom","family":"Coopman","sequence":"additional","affiliation":[{"name":"Inuits-Open Source Innovators, Essensteenweg 31, 2930 Brasschaat, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4873-0710","authenticated-orcid":false,"given":"Joris","family":"Van den Bergh","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute of Belgium, Ringlaan 3, 1180 Brussels, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3671-6900","authenticated-orcid":false,"given":"Maarten","family":"Reyniers","sequence":"additional","affiliation":[{"name":"Royal Meteorological Institute of Belgium, Ringlaan 3, 1180 Brussels, Belgium"}]},{"given":"Dirck","family":"Seynaeve","sequence":"additional","affiliation":[{"name":"Verhaert Masters in Innovation, Hogenakkerhoekstraat 21, 9150 Kruibeke, Belgium"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6704-6201","authenticated-orcid":false,"given":"Wim","family":"Casteels","sequence":"additional","affiliation":[{"name":"IDLab-Faculty of Applied Engineering, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium"},{"name":"Royal Meteorological Institute of Belgium, Ringlaan 3, 1180 Brussels, Belgium"}]},{"given":"Steven","family":"Latr\u00e9","sequence":"additional","affiliation":[{"name":"IDLab-Faculty of Applied Engineering, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium"}]},{"given":"Peter","family":"Hellinckx","sequence":"additional","affiliation":[{"name":"IDLab-Faculty of Applied Engineering, University of Antwerp-IMEC, Sint-Pietersvliet 7, 2000 Antwerp, Belgium"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.aap.2018.10.014","article-title":"Accident risk of road and weather conditions on different road types","volume":"122","author":"Malin","year":"2019","journal-title":"Accid. Anal. Prev."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Roh, C.G., Kim, J., and Im, I. (2020). Analysis of Impact of Rain Conditions on ADAS. Sensors, 20.","DOI":"10.3390\/s20236720"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Dixit, V.V., Chand, S., and Nair, D.J. (2016). Autonomous vehicles: Disengagements, accidents and reaction times. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0168054"},{"key":"ref_4","unstructured":"Parmenter, B., and Thornes, J.E. (1986). The Use of a Computer Model to Predict the Formation of Ice on Road Surfaces, Transport and Road Research Laboratory. Research Report."},{"key":"ref_5","first-page":"421","article-title":"Modelling of road surface temperature from a geographical parameter database. Part 2: Numerical","volume":"8","author":"Chapman","year":"2001","journal-title":"Meteorol. Appl. A J. Forecast. Pract. Appl. Train. Tech. Model."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2026","DOI":"10.1175\/1520-0450(2001)040<2026:MANMFR>2.0.CO;2","article-title":"METRo: A new model for road-condition forecasting in Canada","volume":"40","author":"Crevier","year":"2001","journal-title":"J. Appl. Meteorol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"544","DOI":"10.1002\/met.1486","article-title":"RoadSurf: A modelling system for predicting road weather and road surface conditions","volume":"22","author":"Kangas","year":"2015","journal-title":"Meteorol. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1002\/met.1729","article-title":"On statistical nowcasting of road surface temperature","volume":"26","author":"Yin","year":"2019","journal-title":"Meteorol. Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"e1955","DOI":"10.1002\/met.1955","article-title":"RoadSurf-Pedestrian: A sidewalk condition model to predict risk for wintertime slipping injuries","volume":"27","author":"Hippi","year":"2020","journal-title":"Meteorol. Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1109\/TITS.2014.2371455","article-title":"Potential of intelligent transportation systems in mitigating adverse weather impacts on road mobility: A review","volume":"16","author":"Dey","year":"2014","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","first-page":"281","article-title":"Vehicular networking road weather information system tailored for arctic winter conditions","volume":"12","author":"Sukuvaara","year":"2020","journal-title":"Int. J. Commun. Netw. Inf. Secur."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1175\/WAF-D-18-0167.1","article-title":"Verification of road surface temperature forecasts assimilating data from mobile sensors","volume":"34","author":"Karsisto","year":"2019","journal-title":"Weather Forecast."},{"key":"ref_13","unstructured":"Minge, E., Gallagher, M., Hanson, Z., and Hvizdos, K. (2019). Mobile Technologies for Assessment of Winter Road Conditions, Department of Transportation, Clear Roads Pooled Fund. Technical Report."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Stepanova, D., Sukuvaara, T., and Karsisto, V. (2020, January 25\u201328). Intelligent Transport Systems\u2013Road weather information and forecast system for vehicles. Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium.","DOI":"10.1109\/VTC2020-Spring48590.2020.9129368"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hellweg, M., Acevedo-Valencia, J.W., Paschalidi, Z., Nachtigall, J., Kratzsch, T., and Stiller, C. (2020, January 25\u201328). Using floating car data for more precise road weather forecasts. Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium.","DOI":"10.1109\/VTC2020-Spring48590.2020.9129401"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1175\/BAMS-D-12-00044.1","article-title":"Realizing the potential of vehicle-based observations","volume":"94","author":"Mahoney","year":"2013","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_17","unstructured":"mAutomotive, G (2021, May 26). Connected Car Forecast: Global Connected Car Market to Grow Threefold within Five Years. Available online: https:\/\/www.gsma.com\/iot\/wp-content\/uploads\/2013\/06\/cl_ma_forecast_06_13.pdf."},{"key":"ref_18","unstructured":"Markets (2021, May 26). Connected Car Market. Available online: https:\/\/www.marketsandmarkets.com\/Market-Reports\/connected-car-market-102580117.html."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e921","DOI":"10.1002\/asl.921","article-title":"Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4\u20135 December 2018","volume":"20","author":"Hintz","year":"2019","journal-title":"Atmos. Sci. Lett."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bell, Z., Dance, S.L., and Waller, J.A. (2021). Exploring the characteristics of a vehicle-based temperature dataset for convection-permitting numerical weather prediction. arXiv.","DOI":"10.1002\/met.2058"},{"key":"ref_21","unstructured":"SARWS, C.N. (2021, May 26). Real-Time Location-Aware Road Weather Services Composed from Multi-Modal Data. Available online: https:\/\/www.celticnext.eu\/project-sarws\/."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Mercelis, S., Watelet, S., Casteels, W., Bogaerts, T., Van den Bergh, J., Reyniers, M., and Hellinckx, P. (2020, January 25\u201328). Towards detection of road weather conditions using large-scale vehicle fleets. Proceedings of the 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), Antwerp, Belgium.","DOI":"10.1109\/VTC2020-Spring48590.2020.9128484"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ibrahim, M.R., Haworth, J., and Cheng, T. (2019). WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8120549"},{"key":"ref_24","first-page":"3567","article-title":"Weather recognition based on 3C-CNN","volume":"14","author":"Tan","year":"2020","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Palvanov, A., and Cho, Y.I. (2019). Visnet: Deep convolutional neural networks for forecasting atmospheric visibility. Sensors, 19.","DOI":"10.3390\/s19061343"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.neucom.2018.09.048","article-title":"A CNN\u2013RNN architecture for multi-label weather recognition","volume":"322","author":"Zhao","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_27","unstructured":"Guerra, J.C.V., Khanam, Z., Ehsan, S., Stolkin, R., and McDonald-Maier, K. (2018, January 6\u20139). Weather Classification: A new multi-class dataset, data augmentation approach and comprehensive evaluations of Convolutional Neural Networks. Proceedings of the 2018 NASA\/ESA Conference on Adaptive Hardware and Systems (AHS), Edinburgh, UK."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1109\/MIC.2019.2935800","article-title":"Architectural Considerations for Privacy on the Edge","volume":"23","author":"Tsigkanos","year":"2019","journal-title":"IEEE Internet Comput."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, H., Barriga, L., Vahidi, A., and Raza, S. (2019, January 4\u20137). Machine Learning for Security at the IoT Edge-A Feasibility Study. Proceedings of the 2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems Workshops (MASSW), Monterey, CA, USA.","DOI":"10.1109\/MASSW.2019.00009"},{"key":"ref_30","unstructured":"Krishnasamy, E., Varrette, S., and Mucciardi, M. (2022, February 17). Edge Computing: An Overview of Framework and Applications. Available online: https:\/\/prace-ri.eu\/wp-content\/uploads\/Edge-Computing-An-Overview-of-Framework-and-Applications.pdf."},{"key":"ref_31","unstructured":"Gonz\u00e1lez, J.P. (2022, February 17). Machine Learning Edge Devices: Benchmark Report. Available online: https:\/\/tryolabs.com\/blog\/machine-learning-on-edge-devices-benchmark-report."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"V\u00e9stias, M.P. (2019). A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing. Algorithms, 12.","DOI":"10.3390\/a12080154"},{"key":"ref_33","unstructured":"Basler (2021, December 07). Basler pulse puA1920-30uc-Area Scan Camera. Available online: https:\/\/www.baslerweb.com\/en\/products\/cameras\/area-scan-cameras\/pulse\/pua1920-30uc\/."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). Identity Mappings in Deep Residual Networks. Computer Vision\u2013ECCV 2016, Springer International Publishing.","DOI":"10.1007\/978-3-319-46454-1"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_36","unstructured":"MLCommons (2022, February 17). Inference: Edge Benchmark v0.7 Results. Available online: https:\/\/mlcommons.org\/en\/inference-edge-07\/."},{"key":"ref_37","unstructured":"Google (2022, February 17). Coral Edge TPU Performance Benchmarks. Available online: https:\/\/coral.ai\/docs\/edgetpu\/benchmarks\/."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_39","unstructured":"Rawashdeh Research Group (2021, December 07). Adverse Weather Dataset. Available online: http:\/\/sar-lab.net\/adverse-weather-dataset\/."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1021\/acssensors.9b01455","article-title":"Low cost sensor networks: How do we know the data are reliable?","volume":"4","author":"Williams","year":"2019","journal-title":"ACS Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3191750","article-title":"Calibrating low-cost sensors by a two-phase learning approach for urban air quality measurement","volume":"2","author":"Lin","year":"2018","journal-title":"Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Lov\u00e9n, L., Karsisto, V., J\u00e4rvinen, H., Sillanp\u00e4\u00e4, M.J., Lepp\u00e4nen, T., Peltonen, E., Pirttikangas, S., and Riekki, J. (2019). Mobile road weather sensor calibration by sensor fusion and linear mixed models. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0211702"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Bogaerts, T., Watelet, S., Thoen, C., Coopman, T., Van den Bergh, J., Reyniers, M., Seynaeve, D., Casteels, W., Latr\u00e9, S., and Hellinckx, P. (2022, January 8\u201311). Enhancement of road weather services using vehicle sensor data. Proceedings of the 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA.","DOI":"10.1109\/CCNC49033.2022.9700658"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"991","DOI":"10.1175\/WAF-D-16-0158.1","article-title":"Comparing the performance of two road weather models in the Netherlands","volume":"32","author":"Karsisto","year":"2017","journal-title":"Weather Forecast."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1175\/2010WAF2222451.1","article-title":"The Integrated Nowcasting through Comprehensive Analysis (INCA) system and its validation over the Eastern Alpine region","volume":"26","author":"Haiden","year":"2011","journal-title":"Weather Forecast."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"E1126","DOI":"10.1175\/BAMS-D-20-0051.1","article-title":"Engaging schools to explore meteorological observational gaps","volume":"102","author":"Caluwaerts","year":"2021","journal-title":"Bull. Am. Meteorol. Soc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2732\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:50Z","timestamp":1760136530000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/7\/2732"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,2]]},"references-count":46,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["s22072732"],"URL":"https:\/\/doi.org\/10.3390\/s22072732","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,2]]}}}