{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T14:15:54Z","timestamp":1780409754464,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T00:00:00Z","timestamp":1592352000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Qilu Young Scholar Program of Shandong University","award":["201999000171"],"award-info":[{"award-number":["201999000171"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.<\/jats:p>","DOI":"10.3390\/s20123433","type":"journal-article","created":{"date-parts":[[2020,6,17]],"date-time":"2020-06-17T13:11:32Z","timestamp":1592399492000},"page":"3433","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Vehicle Detection under Adverse Weather from Roadside LiDAR Data"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2844-6082","authenticated-orcid":false,"given":"Jianqing","family":"Wu","sequence":"first","affiliation":[{"name":"School of Qilu Transportation, Shandong University, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1314-4540","authenticated-orcid":false,"given":"Hao","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Tian","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rendong","family":"Pi","sequence":"additional","affiliation":[{"name":"School of Qilu Transportation, Shandong University, Jinan 250061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Yue","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, University of Nevada, Reno, NV 89557, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chang, J.C., Findley, D.J., Cunningham, C.M., and Tsai, M.K. (2014). Considerations for Effective Lidar Deployment by Transportation Agencies. Transp. Res. Record, 1\u20138.","DOI":"10.3141\/2440-01"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.trc.2013.11.014","article-title":"Automated parking surveys from a LIDAR equipped vehicle","volume":"39","author":"Thornton","year":"2014","journal-title":"Transp. Res. Part C-Emerg. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4652","DOI":"10.3390\/rs5094652","article-title":"Synthesis of Transportation Applications of Mobile LIDAR","volume":"5","author":"Williams","year":"2013","journal-title":"Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Lv, B., Xu, H., Wu, J.Q., Tian, Y., Tian, S., and Feng, S.Y. (2019). Revolution and rotation-based method for roadside LiDAR data integration. Opt. Laser Technol., 119.","DOI":"10.1016\/j.optlastec.2019.105571"},{"key":"ref_5","first-page":"32","article-title":"An Automatic Procedure for Vehicle Tracking with a Roadside LiDAR Sensor","volume":"88","author":"Wu","year":"2018","journal-title":"ITE J.-Inst. Transp. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"65944","DOI":"10.1109\/ACCESS.2019.2916718","article-title":"Deer Crossing Road Detection With Roadside LiDAR Sensor","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"79895","DOI":"10.1109\/ACCESS.2019.2923421","article-title":"LiDAR-Enhanced Connected Infrastructures Sensing and Broadcasting High-Resolution Traffic Information Serving Smart Cities","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"238","DOI":"10.1016\/j.aap.2018.09.001","article-title":"A novel method of vehicle-pedestrian near-crash identification with roadside LiDAR data","volume":"121","author":"Wu","year":"2018","journal-title":"Accid. Anal. Prev."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yue, R., Xu, H., Wu, J.Q., Sun, R.J., and Yuan, C.W. (2019). Data Registration with Ground Points for Roadside LiDAR Sensors. Remote Sens., 11.","DOI":"10.3390\/rs11111354"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1049\/iet-its.2018.5258","article-title":"Trajectory tracking and prediction of pedestrian\u2019s crossing intention using roadside LiDAR","volume":"13","author":"Zhao","year":"2019","journal-title":"IET Intell. Transp. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1177\/0361198118775839","article-title":"3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data","volume":"2672","author":"Sun","year":"2018","journal-title":"Transp. Res. Record"},{"key":"ref_12","unstructured":"Tarko, A. (2018, January 16\u201318). Application of the Lomax distribution to estimate the conditional probability of crash. Proceedings of the 18th International Conference Road Safety on Five Continents (RS5C 2018), Jeju Island, Korea."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Lee, H., and Coifman, B. (2012). Side-Fire Lidar-Based Vehicle Classification. Transp. Res. Record, 173\u2013183.","DOI":"10.3141\/2308-19"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhang, Z.Y., Zheng, J.Y., Wang, X., and Fan, X.L. (2018, January 25\u201327). Background Filtering and Vehicle Detection with Roadside Lidar Based on Point Association. Proceedings of the 37th Chinese Control Conference (CCC), Wuhan, China.","DOI":"10.23919\/ChiCC.2018.8484040"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wu, J.Y., Xu, H., Zheng, J.Y., and IEEE (2017, January 16\u201319). Automatic Background Filtering and Lane Identification with Roadside LiDAR Data. Proceedings of the 20th IEEE International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317723"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1177\/0361198118775841","article-title":"Automatic Background Filtering Method for Roadside LiDAR Data","volume":"2672","author":"Wu","year":"2018","journal-title":"Transp. Res. Record"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"76779","DOI":"10.1109\/ACCESS.2019.2919624","article-title":"Raster-Based Background Filtering for Roadside LiDAR Data","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"key":"ref_18","unstructured":"Zhang, J., Xiao, W., Coifman, B., and Mills, J. (2019, January 10\u201314). Image-based Vehicle Tracking From Roadside Lidar Data. Proceedings of the ISPRS Geospatial Week, Enschede, The Netherlands."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.trc.2019.01.007","article-title":"Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors","volume":"100","author":"Zhao","year":"2019","journal-title":"Transp. Res. Part C-Emerg. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Wu, J., Xu, H., Zheng, J., and Zhao, J. (2020). Automatic vehicle detection with roadside LiDAR data under rainy and snowy conditions. IEEE Intell. Transp. Syst. Mag.","DOI":"10.1109\/MITS.2019.2926362"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1504\/IJSNET.2019.097558","article-title":"A portable roadside vehicle detection system based on multi-sensing fusion","volume":"29","author":"Zheng","year":"2019","journal-title":"Int. J. Sens. Netw."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"183","DOI":"10.2478\/s11772-014-0190-2","article-title":"Comparison of 905 nm and 1550 nm semiconductor laser rangefinders\u2019 performance deterioration due to adverse environmental conditions","volume":"22","author":"Wojtanowski","year":"2014","journal-title":"Opto-Electron. Rev."},{"key":"ref_23","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, Canada.","DOI":"10.1109\/CRV.2018.00043"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jokela, M., Kutila, M., and Pyykonen, P. (2019). Testing and Validation of Automotive Point-Cloud Sensors in Adverse Weather Conditions. Appl. Sci. -Basel, 9.","DOI":"10.3390\/app9112341"},{"key":"ref_25","unstructured":"Kutila, M., Pyykonen, P., Holzhuter, H., Colomb, M., Duthon, P., and IEEE (2006, January 4\u20137). Automotive LiDAR performance verification in fog and rain. Proceedings of the 21st IEEE International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Mannan, F., Gruber, T., Ritter, W., Dietmayer, K., and Heide, F. (2019). Seeing through fog without seeing fog: Deep sensor fusion in the absence of labeled training data. arXiv.","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Gruber, T., and Ritter, W. (2018, January 26\u201330). A benchmark for lidar sensors in fog: Is detection breaking down?. Proceedings of the 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China.","DOI":"10.1109\/IVS.2018.8500543"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Heinzler, R., Schindler, P., Seekircher, J., Ritter, W., and Stork, W. (2019, January 9\u201312). Weather Influence and Classification with Automotive Lidar Sensors. Proceedings of the 2019 IEEE Intelligent Vehicles Symposium (IV 2019), Paris, France.","DOI":"10.1109\/IVS.2019.8814205"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1002\/rob.21701","article-title":"When the Dust Settles: The Four Behaviors of LiDAR in the Presence of Fine Airborne Particulates","volume":"34","author":"Phillips","year":"2017","journal-title":"J. Field Robot."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1007\/s42421-019-00005-9","article-title":"Transfer Learning Using Deep Neural Networks for Classification of Truck Body Types Based on Side-Fire Lidar Data","volume":"1","author":"Nezafat","year":"2019","journal-title":"J. Big Data Anal. Transp."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1109\/MITS.2018.2876559","article-title":"Automatic Lane Identification Using the Roadside LiDAR Sensors","volume":"12","author":"Wu","year":"2020","journal-title":"IEEE Intell. Transp. Syst. Mag."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1177\/0361198119844457","article-title":"Vehicle Detection and Tracking in Complex Traffic Circumstances with Roadside LiDAR","volume":"2673","author":"Zhang","year":"2019","journal-title":"Transp. Res. Record"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1177\/0361198119843857","article-title":"Automatic Vehicle Classification using Roadside LiDAR Data","volume":"2673","author":"Wu","year":"2019","journal-title":"Transp. Res. Record"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wu, J., Xu, H., Yue, R., Tian, Z., Tian, Y., and Tian, Y. (2019). An automatic skateboarder detection method with roadside LiDAR data. J. Transp. Saf. Secur., 1\u201320.","DOI":"10.3390\/s20123433"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1177\/0361198119843869","article-title":"Automatic Ground Points Identification Method for Roadside LiDAR Data","volume":"2673","author":"Wu","year":"2019","journal-title":"Transp. Res. Record"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1109\/LGRS.2008.2004470","article-title":"Three-Dimensional LiDAR Data Classifying to Extract Road Point in Urban Area","volume":"5","author":"Choi","year":"2008","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.optlastec.2019.02.039","article-title":"Automatic ground points filtering of roadside LiDAR data using a channel-based filtering algorithm","volume":"115","author":"Wu","year":"2019","journal-title":"Opt. Laser Technol."},{"key":"ref_38","unstructured":"Toth, C.K., and Shan, J. (2008). Building extraction from LiDAR point clouds based on clustering techniques. Topographic Laser Ranging and Scanning: Principles and Processing, CRC Press."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/01431160902882561","article-title":"Adaptive clustering of airborne LiDAR data to segment individual tree crowns in managed pine forests","volume":"31","author":"Lee","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1374","DOI":"10.1109\/TGRS.2014.2338915","article-title":"Semiautomated Extraction of Street Light Poles from Mobile LiDAR Point-Clouds","volume":"53","author":"Yu","year":"2015","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3433\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:40:09Z","timestamp":1760175609000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/12\/3433"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,6,17]]},"references-count":40,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2020,6]]}},"alternative-id":["s20123433"],"URL":"https:\/\/doi.org\/10.3390\/s20123433","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,6,17]]}}}