{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T21:18:36Z","timestamp":1775078316348,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T00:00:00Z","timestamp":1724803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Oceans and Fisheries","award":["220A580068"],"award-info":[{"award-number":["220A580068"]}]},{"DOI":"10.13039\/501100002649","name":"Yeungnam University","doi-asserted-by":"publisher","award":["220A580068"],"award-info":[{"award-number":["220A580068"]}],"id":[{"id":"10.13039\/501100002649","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Studies on autonomous driving have started to focus on snowy environments, and studies to acquire data and remove noise and pixels caused by snowfall in such environments are in progress. However, research to determine the necessary weather information for the control of unmanned platforms by sensing the degree of snowfall in real time has not yet been conducted. Therefore, in this study, we attempted to determine snowfall information for autonomous driving control in snowy weather conditions. To this end, snowfall data were acquired by LiDAR sensors in various snowy areas in South Korea, Sweden, and Denmark. Snow, which was extracted using a snow removal filter (the LIOR filter that we previously developed), was newly classified and defined based on the extracted number of snow particles, the actual snowfall total, and the weather forecast at the time. Finally, we developed an algorithm that extracts only snow in real time and then provides snowfall information to an autonomous driving system. This algorithm is expected to have a similar effect to that of actual controllers in promoting driving safety in real-time weather conditions.<\/jats:p>","DOI":"10.3390\/s24175587","type":"journal-article","created":{"date-parts":[[2024,8,28]],"date-time":"2024-08-28T11:54:10Z","timestamp":1724846050000},"page":"5587","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["LiDAR-Based Snowfall Level Classification for Safe Autonomous Driving in Terrestrial, Maritime, and Aerial Environments"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4033-7965","authenticated-orcid":false,"given":"Ji-il","family":"Park","sequence":"first","affiliation":[{"name":"National Defense AI Center, Agency for Defense Development (ADD), 160, Bugyuseong-daero 488beon-gil, Yuseong-gu, Daejeon 34060, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-9317-2568","authenticated-orcid":false,"given":"Seunghyeon","family":"Jo","sequence":"additional","affiliation":[{"name":"AUTOCRYPT GmbH, Salvatorplatz, 80333 M\u00fcnchen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9717-344X","authenticated-orcid":false,"given":"Hyung-Tae","family":"Seo","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, College of Creative Engineering, Kyonggi University, 154-42, Gwanggyosan-ro, Suwon 16227, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7516-3398","authenticated-orcid":false,"given":"Jihyuk","family":"Park","sequence":"additional","affiliation":[{"name":"Department of Automotive Engineering, College of Digital Convergence, Yeungnam University, 280 Daehak-ro, Gyeongsan 38541, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"031207","DOI":"10.1117\/1.OE.62.3.031207","article-title":"Winter adverse driving dataset for autonomy in inclement winter weather","volume":"62","author":"Kurup","year":"2023","journal-title":"Opt. Eng."},{"key":"ref_2","unstructured":"Hyundai (2024, August 01). Hyundai \u00d7 Motional-Bringing IONIQ 5 Robotaxis to the Streets from 2023. Available online: https:\/\/www.hyundai.com\/worldwide\/en\/brand\/robotaxis."},{"key":"ref_3","unstructured":"Waymo (2024, August 01). Safety Report and Whitepapers. Available online: https:\/\/waymo.com\/safety."},{"key":"ref_4","unstructured":"McEachern, S. (GM Authority, 2021). Cruise founder takes company\u2019s first driverless ride on SF Streets: Video, GM Authority."},{"key":"ref_5","unstructured":"SAE International (2018). SAE International Releases Updated Visual Chart for Its \u201cLevels of Driving Automation\u201d Standard for Self-Driving Vehicle, SAE International."},{"key":"ref_6","unstructured":"U.S. Department of Transportation Fed-Eral Highway Administration (2024, August 01). Available online: https:\/\/ops.fhwa.dot.gov\/weather\/weather_events\/snow_ice.htm."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1270","DOI":"10.1175\/1520-0477(1981)062<1270:WACNFL>2.0.CO;2","article-title":"Weather and climate needs for lidar observations from space and concepts for their realization","volume":"62","author":"Atlas","year":"1981","journal-title":"Bull. Am. Meteorol. Soc."},{"key":"ref_8","first-page":"484","article-title":"Detection of local weather events from multiwavelength lidar measurements during the EARLI09 campaign","volume":"56","author":"Belegante","year":"2011","journal-title":"Rom. J. Phys."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.5194\/gmd-16-2077-2023","article-title":"Evaluating wind profiles in a numerical weather prediction model with Doppler lidar","volume":"16","year":"2023","journal-title":"Geosci. Model Dev."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dannheim, C., Icking, C., M\u00e4der, M., and Sallis, P. (2014, January 27\u201329). Weather detection in vehicles by means of camera and LIDAR systems. Proceedings of the 2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks, Tetova, Macedonia.","DOI":"10.1109\/CICSyN.2014.47"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Vargas Rivero, J.R., Gerbich, T., Teiluf, V., Buschardt, B., and Chen, J. (2020). Weather classification using an automotive lidar sensor based on detections on asphalt and atmosphere. Sensors, 20.","DOI":"10.3390\/s20154306"},{"key":"ref_12","first-page":"577","article-title":"Evaluation of driver visibility from mobile lidar data and weather conditions","volume":"41","author":"Lorenzo","year":"2016","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_13","unstructured":"Tian, Y. (2021). Identification of Weather Conditions Related to Roadside LiDAR Data. [Master\u2019s Thesis, University of Nevada]."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Da Silva, M.P., Carneiro, D., Fernandes, J., and Texeira, L.F. (2023, January 18\u201323). MobileWeatherNet for LiDAR-Only Weather Estimation. Proceedings of the 2023 International Joint Conference on Neural Networks (IJCNN), Gold Coast, Australia.","DOI":"10.1109\/IJCNN54540.2023.10191333"},{"key":"ref_15","unstructured":"Robert, G., Hallowell, P., Michael, P., Matthews, P., and Pisano, A. (2005, January 18\u201319). Automated extraction of weather variables from camera imagery. Proceedings of the 2005 Mid-Continent Transportation Research Symposium, Ames, IA, USA."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1007\/s00138-005-0011-1","article-title":"Automatic fog detection and estimation of visibility distance through use of an onboard camera","volume":"17","author":"Hautiere","year":"2006","journal-title":"Mach. Vis. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ozcan, K., Sharma, A., Knickerbocker, S., Merickel, J., Hawkins, N., and Rizzo, M. (2020). Road weather condition estimation using fixed and mobile based cameras. Advances in Computer Vision: Proceedings of the 2019 Computer Vision Conference (CVC), Volume 1, Springer.","DOI":"10.1007\/978-3-030-17795-9_14"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1007\/s11263-011-0421-7","article-title":"Rain or snow detection in image sequences through use of a histogram of orientation of streaks","volume":"93","author":"Bossu","year":"2011","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","unstructured":"Yan, X., Luo, Y., and Zheng, X. (2009). Weather recognition based on images captured by vision system in vehicle. Advances in Neural Networks\u2014ISNN 2009: 6th International Symposium on Neural Networks, ISNN 2009 Wuhan, China, May 26\u201329, 2009 Proceedings, Part III, Springer."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"105521","DOI":"10.1016\/j.aap.2020.105521","article-title":"Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data","volume":"142","author":"Khan","year":"2020","journal-title":"Accid. Anal. Prev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1016\/j.ijtst.2021.06.003","article-title":"Weather and surface condition detection based on road-side webcams: Application of pre-trained convolutional neural network","volume":"11","author":"Khan","year":"2022","journal-title":"Int. J. Transp. Sci. Technol."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jonsson, P. (2011, January 19\u201321). Classification of road conditions: From camera images and weather data. Proceedings of the 2011 IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) Proceedings, Ottawa, ON, Canada.","DOI":"10.1109\/CIMSA.2011.6059917"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Sirirattanapol, C., Nagai, M., Witayangkurn, A., Pravinvongvuth, S., and Ekpanyapong, M. (2019). Bangkok CCTV image through a road environment extraction system using multi-label convolutional neural network classification. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030128"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cordes, K., Reinders, C., Hindricks, P., Lammers, J., Rosenhahn, B., and Broszio, H. (2022, January 18\u201324). Roadsaw: A large-scale dataset for camera-based road surface and wetness estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00490"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cordes, K., and Broszio, H. (2023, January 2\u20133). Camera-Based Road Snow Coverage Estimation. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCVW60793.2023.00433"},{"key":"ref_26","first-page":"100766","article-title":"Snow coverage estimation using camera data for automated driving applications","volume":"18","author":"Goberville","year":"2023","journal-title":"Transp. Res. Interdiscip. Perspect."},{"key":"ref_27","unstructured":"Udacity (2024, August 01). Udacity\u2019s Self Driving Nano Degree Program. Available online: https:\/\/www.udacity.com\/course\/self-driving-car-engineer-nanodegree\u2013nd0013."},{"key":"ref_28","unstructured":"Ford Media Center (2016). Ford Conducts Industry-First Snow Tests of Autonomous Vehicles\u2013Further Accelerating Development Program, Ford Media Center."},{"key":"ref_29","unstructured":"Torc (2018). Torc Self-Driving Car Dashes through Snow, Torc."},{"key":"ref_30","unstructured":"Sensible4 (2024, August 01). Unique Technology for All-Weather Self-Driving Vehicles. Available online: https:\/\/sensible4.fi\/technology\/."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Liu, G. (2020). Radar snowfall measurement. Satellite Precipitation Measurement: Volume 1, Springer.","DOI":"10.1007\/978-3-030-24568-9_16"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"160202","DOI":"10.1109\/ACCESS.2020.3020266","article-title":"Fast and accurate desnowing algorithm for LiDAR point clouds","volume":"8","author":"Park","year":"2020","journal-title":"IEEE Access"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/LRA.2020.2972865","article-title":"Cnn-based lidar point cloud de-noising in adverse weather","volume":"5","author":"Heinzler","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Charron, N., Phillips, S., and Waslander, S.L. (2018, January 9\u201311). De-noising of lidar point clouds corrupted by snowfall. Proceedings of the 2018 15th Conference on Computer and Robot Vision (CRV), Toronto, ON, Canada.","DOI":"10.1109\/CRV.2018.00043"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1109\/JSEN.2021.3133873","article-title":"DIOR: A hardware-assisted weather denoising solution for LiDAR point clouds","volume":"22","author":"Roriz","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_36","unstructured":"CompuWeather (2024, August 01). The Important Difference between Wet Snow and Dry Snow. Available online: https:\/\/compuweather.com\/the-important-difference-between-wet-snow-and-dry-snow\/."},{"key":"ref_37","unstructured":"Oceanic, N., and Adminitration, A. (2024, August 01). What Is the Difference between a Winter Storm Watch, Warning, and Advisory?, Available online: https:\/\/www.weather.gov\/ilx\/wwa_social."},{"key":"ref_38","unstructured":"Administration, K.M. (2024, August 01). Criteria for Advisory\/Warning Information, Available online: https:\/\/web.kma.go.kr\/eng\/weather\/forecast\/standardwarninginfo.jsp."},{"key":"ref_39","unstructured":"Lane, M. (2024, August 01). Severe Weather Planning Guidance for HSE Services. Available online: https:\/\/www.hse.ie\/eng\/services\/list\/3\/emergencymanangement\/severe-weather\/severe-weather-planning-guidance-for-hse-services-2024.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5587\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:44:35Z","timestamp":1760111075000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/17\/5587"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,28]]},"references-count":39,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,9]]}},"alternative-id":["s24175587"],"URL":"https:\/\/doi.org\/10.3390\/s24175587","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,28]]}}}