{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:19:33Z","timestamp":1760149173153,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2023,7,10]],"date-time":"2023-07-10T00:00:00Z","timestamp":1688947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["5210120159","SF2149"],"award-info":[{"award-number":["5210120159","SF2149"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Talent Introduction Research Foundation of Changsha University","award":["5210120159","SF2149"],"award-info":[{"award-number":["5210120159","SF2149"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>This article introduces a long-range sensing system based on millimeter-wave radar, which is used to detect the roadside boundaries and track trains for trains. Due to the high speed and long braking distance of trains, existing commercial vehicle sensing solutions cannot meet their needs for long-range target detection. To address this challenge, this study proposes a long-range perception system for detecting road boundaries and trains based on millimeter-wave radar. The system uses high-resolution, long-range millimeter-wave radar customized for the strong scattering environment of rail transit. First, we established a multipath scattering theory in complex scenes such as track tunnels and fences and used the azimuth scattering characteristics to eliminate false detections. A set of accurate calculation methods of the train\u2019s ego-velocity is proposed, which divides the radar detection point clouds into static target point clouds and dynamic target point clouds based on the ego-velocity of the train. We then used the road boundary curvature, global geometric parallel information, and multi-frame information fusion to extract and fit the boundary in the static target point stably. Finally, we performed clustering and shape estimation on the radar track information to identify the train and judge the collision risk based on the position and speed of the detected train and the extracted boundary information. The paper makes a significant contribution by establishing a multipath scattering theory for complex scenes of rail transit to eliminate radar false detection and proposing a train speed estimation strategy and a road boundary feature point extraction method that adapt to the rail environment. As well as building a perception system and installing it on the train for verification, the main line test results showed that the system can reliably detect the road boundary more than 400 m ahead of the train and can stably detect and track the train.<\/jats:p>","DOI":"10.3390\/rs15143473","type":"journal-article","created":{"date-parts":[[2023,7,11]],"date-time":"2023-07-11T01:42:58Z","timestamp":1689039778000},"page":"3473","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Long-Range Perception System for Road Boundaries and Objects Detection in Trains"],"prefix":"10.3390","volume":"15","author":[{"given":"Wenbo","family":"Pan","sequence":"first","affiliation":[{"name":"Changsha University, 98 Hongshan Road, Changsha 410022, China"},{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}]},{"given":"Xianghua","family":"Fan","sequence":"additional","affiliation":[{"name":"Changsha University, 98 Hongshan Road, Changsha 410022, China"}]},{"given":"Hongbo","family":"Li","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}]},{"given":"Kai","family":"He","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., 169 Shidai Road, Zhuzhou 412001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.trc.2018.02.012","article-title":"Autonomous vehicle perception: The technology of today and tomorrow","volume":"89","author":"Gruyer","year":"2018","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_2","first-page":"50","article-title":"Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, H., Pan, W., Hu, Y., Li, C., Yuan, X., and Long, T. (2022). A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks. Sensors, 22.","DOI":"10.3390\/s22165989"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1589","DOI":"10.1049\/iet-rsn.2017.0126","article-title":"Human\u2013vehicle classification using feature-based SVM in 77-GHz automotive FMCW radar","volume":"11","author":"Lee","year":"2017","journal-title":"IET Radar Sonar Navig."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cho, H., Choi, S., Cho, Y., and Kim, J. (2020, January 6\u20139). Deep complex-valued network for ego-velocity estimation with millimeter-wave radar. Proceedings of the 2020 IEEE SENSORS, Shanghai, China.","DOI":"10.1109\/SENSORS47125.2020.9278729"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wagner, T., Feger, R., and Stelzer, A. (2015, January 8\u201310). Modification of DBSCAN and application to range\/Doppler\/DoA measurements for pedestrian recognition with an automotive radar system. Proceedings of the 2015 European Radar Conference (EuRAD), Paris, France.","DOI":"10.1109\/EuRAD.2015.7346289"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lim, S., Lee, S., and Kim, S. (2018, January 20\u201322). Clustering of detected targets using DBSCAN in automotive radar systems. Proceedings of the 2018 19th International Radar Symposium (IRS), Bonn, Germany.","DOI":"10.23919\/IRS.2018.8448228"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Schlichenmaier, J., Selvaraj, N., Stolz, M., and Waldschmidt, C. (2017, January 19\u201321). Template matching for radar-based orientation and position estimation in automotive scenarios. Proceedings of the 2017 IEEE MTT-S International Conference on Microwaves for Intelligent Mobility (ICMIM), Nagoya, Japan.","DOI":"10.1109\/ICMIM.2017.7918865"},{"key":"ref_9","first-page":"1963450","article-title":"Detection and Tracking of Road Barrier Based on Radar and Vision Sensor Fusion","volume":"10","author":"Kim","year":"2016","journal-title":"J. Sens."},{"key":"ref_10","unstructured":"(2022, November 22). India Company Invented Collision Avoidance Radar System to Make Trains Travel Safer. Available online: http:\/\/www.chinanews.com\/shipin\/2010\/12-21\/news30667.html."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MAES.2017.150104","article-title":"Collision Avoidance Radar System for the Bullet Train: Implementation and First Results","volume":"32","author":"Liu","year":"2017","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1078","DOI":"10.1049\/iet-rsn.2018.0026","article-title":"Recent evolution of automotive imaging radar and its information content","volume":"12","author":"Brisken","year":"2018","journal-title":"IET Radar Sonar Navig."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Li, G., Sit, Y.L., Manchala, S., Kettner, T., Ossowska, A., Krupinski, K., Sturm, C., and Lubbert, U. (2019, January 25\u201327). Novel 4D 79 GHz Radar Concept for Object Detection and Active Safety Applications. Proceedings of the 2019 12th German Microwave Conference (GeMiC), Stuttgart, Germany.","DOI":"10.23919\/GEMIC.2019.8698172"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Stolz, M., Wolf, M., Meinl, F., Kunert, M., and Menzel, W. (2018, January 26\u201328). A New Antenna Array and Signal Processing Concept for an Automotive 4D Radar. Proceedings of the 2018 15th European Radar Conference (EuRAD), Madrid, Spain.","DOI":"10.23919\/EuRAD.2018.8546603"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Chen, X., Ma, H., Wan, J., Li, B., and Xia, T. (2017, January 21\u201326). Multi-view 3D Object Detection Network for Autonomous Driving. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.691"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s41095-021-0229-5","article-title":"PCT: Point cloud transformer","volume":"7","author":"Guo","year":"2021","journal-title":"Comput. Vis. Media"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bai, J., Zheng, L., Li, S., Tan, B., Chen, S., and Huang, L. (2021). Radar transformer: An object classification network based on 4D MMW imaging radar. Sensors, 21.","DOI":"10.3390\/s21113854"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Abdu, F.J., Zhang, Y., Fu, M., Li, Y., and Deng, Z. (2021). Application of Deep Learning on Millimeter-Wave Radar Signals: A Review. Sensors, 21.","DOI":"10.3390\/s21061951"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Brodeski, D., Bilik, I., and Giryes, I. (2019, January 22\u201326). Deep radar detector. Proceedings of the 2019 IEEE Radar Conference (RadarConf), Boston, MA, USA.","DOI":"10.1109\/RADAR.2019.8835792"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1109\/MSP.2015.2504328","article-title":"High-Accuracy Localization for Assisted Living: 5G systems will turn multipath channels from foe to friend","volume":"33","author":"Witrisal","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Guo, X.P., Du, J.S., Gao, J., and Wang, W. (2018, January 18\u201320). Pedestrian Detection Based on Fusion of Millimeter Wave Radar and Vision. Proceedings of the 2018 International Conference on Intelligence and Pattern Recognition, Beijing, China.","DOI":"10.1145\/3268866.3268868"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kellner, D., Klappstein, J., and Dietmayer, K. (2012, January 3\u20137). Grid-based DBSCAN for clustering extended objects in radar data. Proceedings of the Intelligent Vehicles Symposium, Madrid, Spain.","DOI":"10.1109\/IVS.2012.6232167"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Etinger, A., Litvak, B., and Pinhasi, Y. (2017). Multi Ray Model for Near-Ground Millimeter Wave Radar. Sensors, 17.","DOI":"10.3390\/s17091983"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"945","DOI":"10.1109\/TAP.2013.2291008","article-title":"A low-RCS and high-gain partially reflecting surface antenna","volume":"62","author":"Pan","year":"2013","journal-title":"IEEE Trans. Antennas Propag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y., Wei, Y., Wang, Y., Lin, Y., Shen, W., and Jiang, W. (2023). False Detections Revising Algorithm for Millimeter Wave Radar SLAM in Tunnel. Remote Sens., 15.","DOI":"10.3390\/rs15010277"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.eswa.2021.115563","article-title":"A Gaussian Process model for UAV localization using millimetre wave radar","volume":"185","author":"Paredes","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_27","unstructured":"(2023, January 02). Design Guide: TIDEP-01012\u2014Imaging Radar Using Cascaded mmWave Sensor Reference Design (REV. A). Available online: http:\/\/www.ti.com\/lit\/ug\/tiduen5a\/tiduen5a.pdf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3473\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:09:50Z","timestamp":1760126990000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/14\/3473"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,10]]},"references-count":27,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["rs15143473"],"URL":"https:\/\/doi.org\/10.3390\/rs15143473","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,7,10]]}}}