{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T13:38:38Z","timestamp":1773841118411,"version":"3.50.1"},"reference-count":28,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Dong-A University research fund"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Black ice on the road can be dangerous, as it renders the road slippery and is difficult to identify, owing to its transparency. Although studies on black ice detection using cameras, optical sensors, and infrared sensors have been conducted, these sensors have limitations, as they are affected by low light conditions and sunlight. To detect black ice regardless of low light conditions or sunlight, in this study, we incorporate a mmWave sensor that is consistent with varying light conditions. In the proposed method, a frequency modulated continuous wave is transmitted to the surface by the mmWave sensor, and the mmWave sensor backscattering is modulated by the surface medium and roughness. The proposed method also includes preprocessing to calculate the Range-FFT result of the mmWave sensor backscattering and a classification based on a 1-dimensional convolutional neural network to precisely detect the presence of black ice from the Range-FFT result. As a result of the indoor experiment, the proposed black ice detection method achieves an accuracy of 98.2% on dry, wet, and black ice surfaces. Additionally, under low light conditions and in an outdoor environment with sunlight, the proposed method achieves accuracies of 95.6% and 98.5%, respectively.<\/jats:p>","DOI":"10.3390\/rs14205252","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T00:34:30Z","timestamp":1666312470000},"page":"5252","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Black Ice Detection Method Based on 1-Dimensional CNN Using mmWave Sensor Backscattering"],"prefix":"10.3390","volume":"14","author":[{"given":"Jaewook","family":"Kim","sequence":"first","affiliation":[{"name":"Department of ICT Integrated Ocean-Front Smart City Engineering, Dong-A University, 37, Nakdong-Daero 550 beon-gil, Saha-gu, Busan 49315, Korea"}]},{"given":"Eunkyung","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Artificial Intelligence Software, Hanbat National University, 125, Dongseo-Daero, Yuseong-gu, Daejeon 34158, Korea"}]},{"given":"Dongwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Dong-A University, 37, Nakdong-Daero 550 beon-gil, Saha-gu, Busan 49315, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","unstructured":"(2022, June 13). Snow and Ice, Available online: https:\/\/ops.fhwa.dot.gov\/weather\/weather_events\/snow_ice.htm."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1109\/MITS.2017.2666587","article-title":"Prototype Decision Support System for Black Ice Detection and Road Closure Control","volume":"9","author":"Liu","year":"2017","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tabatabai, H., and Aljuboori, M. (2017). A Novel Concrete-based Sensor for Detection of Ice and Water on Roads and Bridges. Sensors, 17.","DOI":"10.3390\/s17122912"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lee, H., Kang, M., Song, J., and Hwang, K. (2020). The Detection of Black Ice Accidents for Preventative Automated Vehicles using Convolutional Neural Networks. Electronics, 9.","DOI":"10.3390\/electronics9122178"},{"key":"ref_5","unstructured":"Crawford, C.H., Daijavad, S., Gunnels, J.A., Nowicki, T., Swirszcz, G.M., and Xenidis, J. (2018). Method for Black Ice Detection and Prediction. (9,940,549), U.S. Patent."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jonsson, P. (2011, January 28\u201331). Remote Sensor for Winter Road Surface Status Detection. Proceedings of the IEEE Sensors, Limerick, Ireland.","DOI":"10.1109\/ICSENS.2011.6127089"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"7242","DOI":"10.1364\/AO.398772","article-title":"Method for Black Ice Detection on Roads using Tri-wavelength Backscattering Measurements","volume":"59","author":"Ma","year":"2020","journal-title":"Appl. Opt."},{"key":"ref_8","unstructured":"Nakanishi, Y., and Kushihi, Y. (2021). Black Ice and Standing Water Detection System. (16,647,046), U.S. Patent."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"41","DOI":"10.5331\/bgr.30.41","article-title":"Development of a Mobile Optical System to Detect Road-freezing Conditions","volume":"30","author":"Alimasi","year":"2012","journal-title":"Bull. Glaciol. Res."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Bystrov, A., Hoare, E., Tran, T.Y., Clarke, N., Gashinova, M., and Cherniakov, M. (2018, January 12\u201314). Sensors for Automotive Remote Road Surface Classification. Proceedings of the IEEE International Conference on Vehicular Electronics and Safety (ICVES), Madrid, Spain.","DOI":"10.1109\/ICVES.2018.8519499"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Xiong, Y., From, P.J., and Isler, V. (2018, January 21\u201325). Design and Evaluation of a Novel Cable-driven Gripper with Perception Capabilities for Strawberry Picking Robots. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8460705"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TMTT.2011.2178427","article-title":"Millimeter-Wave Technology for Automotive Radar Sensors in the 77GHz Frequency Band","volume":"60","author":"Hasch","year":"2012","journal-title":"IEEE Trans. Microw. Theory Technol."},{"key":"ref_13","unstructured":"Cesar, I., and Sandeep, R. (2017). The Fundamentals of Millimeter Wave Sensors, Texas Instruments."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1109\/JMW.2020.3033616","article-title":"Automotive Radar\u2014From First Efforts to Future Systems","volume":"1","author":"Waldschmidt","year":"2021","journal-title":"IEEE J. Microw."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, J., Geng, X., and Wei, S. (2019, January 13\u201315). Airport Runway FOD Detection System based on 77GHz Millimeter Wave Radar Sensor. Proceedings of the IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA), Chengdu, China.","DOI":"10.1109\/ICTA48799.2019.9012911"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Hyun, E., Jin, Y.S., and Lee, J.H. (2016). A Pedestrian Detection Scheme using a Coherent Phase Difference Method Based on 2D Range-Doppler FMCW Radar. Sensors, 16.","DOI":"10.3390\/s16010124"},{"key":"ref_17","unstructured":"Tokihiko, A., and Seiichi, M. (2019, January 27\u201330). Object Tracking and Classification Using Millimeter-Wave Radar Based on LSTM. Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand."},{"key":"ref_18","unstructured":"Wu, Q., and Zhao, D. (2018, January 18\u201320). Dynamic Hand Gesture Recognition using FMCW Radar Sensor for Driving Assistance. Proceedings of the 10th International Conference on Wireless Communications and Signal Processing (WCSP), Hangzhou, China."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Lee, H.R., Park, J., and Suh, Y.J. (2020). Improving Classification Accuracy of Hand Gesture Recognition based on 60 GHz FMCW Radar with Deep Learning Domain Adaptation. Electronics, 9.","DOI":"10.3390\/electronics9122140"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"5119","DOI":"10.1109\/JSEN.2020.3036047","article-title":"RAMP-CNN: A Novel Neural Network for Enhanced Automotive Radar Object Recognition","volume":"21","author":"Xiangyu","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"19993","DOI":"10.1109\/JSEN.2021.3092583","article-title":"Target Classification by mmWave FMCW Radars Using Machine Learning on Range-Angle Images","volume":"21","author":"Siddharth","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_22","unstructured":"Merrill, I.S. (1990). Radar Handbook, Mcgraw-Hill."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sabery, S.M., Bystrov, A., Navarro-C\u00eda, M., Gardner, P., and Gashinova, M. (2021). Study of Low Terahertz Radar Signal Backscattering for Surface Identification. Sensors, 21.","DOI":"10.3390\/s21092954"},{"key":"ref_24","unstructured":"Richards, M.A., Scheer, J., and Holm, W.A. (2020). Principles of Modern Radar: Basic Principles, Scitech Publishing."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"851","DOI":"10.1109\/8.774140","article-title":"Low Grazing Incidence Millimeter-Wave Scattering Models and Measurements for Various Road Surface","volume":"47","author":"Li","year":"1999","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bystrov, A., Hoare, E., Tran, T.Y., Clarke, N., Gashinova, M., and Cherniakov, M. (2017). Automotive System for Remote Surface Classification. Sensors, 17.","DOI":"10.3390\/s17040745"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1679","DOI":"10.1109\/8.650080","article-title":"Modeling and Measurements of Scattering from Road Surfaces at Millimter-Wave Frequencies","volume":"45","author":"Sarabandi","year":"1997","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2020.107398","article-title":"1D Convolutional Neural Networks and Applications: A Survey","volume":"151","author":"Kiranyaz","year":"2021","journal-title":"Mech. Syst. 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