{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:35:07Z","timestamp":1783384507508,"version":"3.54.6"},"reference-count":35,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T00:00:00Z","timestamp":1660176000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["(5210120159)"],"award-info":[{"award-number":["(5210120159)"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>There exist many difficulties in environmental perception in transportation at open-pit mines, such as unpaved roads, dusty environments, and high requirements for the detection and tracking stability of small irregular obstacles. In order to solve the above problems, a new multi-target detection and tracking method is proposed based on the fusion of Lidar and millimeter-wave radar. It advances a secondary segmentation algorithm suitable for open-pit mine production scenarios to improve the detection distance and accuracy of small irregular obstacles on unpaved roads. In addition, the paper also proposes an adaptive heterogeneous multi-source fusion strategy of filtering dust, which can significantly improve the detection and tracking ability of the perception system for various targets in the dust environment by adaptively adjusting the confidence of the output target. Finally, the test results in the open-pit mine show that the method can stably detect obstacles with a size of 30\u201340 cm at 60 m in front of the mining truck, and effectively filter out false alarms of concentration dust, which proves the reliability of the method.<\/jats:p>","DOI":"10.3390\/s22165989","type":"journal-article","created":{"date-parts":[[2022,8,11]],"date-time":"2022-08-11T21:15:05Z","timestamp":1660252505000},"page":"5989","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A Detection and Tracking Method Based on Heterogeneous Multi-Sensor Fusion for Unmanned Mining Trucks"],"prefix":"10.3390","volume":"22","author":[{"given":"Haitao","family":"Liu","sequence":"first","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5527-2914","authenticated-orcid":false,"given":"Wenbo","family":"Pan","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunqing","family":"Hu","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Li","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiwen","family":"Yuan","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Long","sequence":"additional","affiliation":[{"name":"CRRC Zhuzhou Institute Co., Ltd., Zhuzhou 412001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,11]]},"reference":[{"key":"ref_1","first-page":"72","article-title":"Unmanned technology for mining trucks","volume":"10","author":"Yuan","year":"2013","journal-title":"Min. Equip."},{"key":"ref_2","first-page":"3","article-title":"The Big Picture: An Overview Approach to Surface Mining","volume":"24","author":"Widdififield","year":"2016","journal-title":"Min. Eng."},{"key":"ref_3","first-page":"465","article-title":"Two-stage static\/dynamic environment modeling using voxel representation","volume":"Volume 417","author":"Asvadi","year":"2016","journal-title":"Robot 2015: Second Iberian Robotics Conference"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1016\/j.robot.2016.06.007","article-title":"3D Lidar-Based Static and Moving Obstacle Detection in Driving Environments: An Approach Based on Voxels and Multi-Region Ground Planes","volume":"83","author":"Asvadi","year":"2016","journal-title":"Robot. Auton. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.robot.2017.11.014","article-title":"Unsupervised Obstacle Detection in Driving Environments Using Deep-Learning-Based Stereovision","volume":"100","author":"Dairi","year":"2018","journal-title":"Robot. Auton. Syst."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/TITS.2009.2018319","article-title":"Obstacle Detection and Tracking for the Urban Challenge","volume":"10","author":"Darms","year":"2009","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_7","first-page":"43","article-title":"Research and application of millimeter wave radar collision avoidance system for mining truck","volume":"46","author":"Wei","year":"2015","journal-title":"Saf. Coal Mines"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhou, T., Yang, M., Jiang, K., Wong, H., and Yang, D. (2020). MMW Radar-Based Technologies in Autonomous Driving: A Review. Sensors, 20.","DOI":"10.3390\/s20247283"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3548","DOI":"10.3390\/s21103548","article-title":"A New Challenge: Detection of Small-Scale Falling Rocks on Transportation Roads in Open-Pit Mines","volume":"21","author":"Lin","year":"2021","journal-title":"Sensors"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"123757","DOI":"10.1109\/ACCESS.2019.2928603","article-title":"A Target Detection Model Based on Improved Tiny-Yolov3 Under the Environment of Mining Truck","volume":"7","author":"Xiao","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lu, S., Luo, Z., Gao, F., Liu, M., Chang, K., and Piao, C. (2021). A Fast and Robust Lane Detection Method Based on Semantic Segmentation and Optical Flow Estimation. Sensors, 21.","DOI":"10.3390\/s21020400"},{"key":"ref_12","unstructured":"Brabandere, B.D., Neven, D., and Gool, L.V. (2017, January 21\u201326). Semantic instance segmentation for autonomous driving. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Eindhoven, The Netherlands."},{"key":"ref_13","unstructured":"Liu, P., King, I., Lyu, M.R., and Xu, J. (February, January 27). DDFlow: Learning optical flow with unlabeled data distillation. Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs","volume":"40","author":"Chen","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.knosys.2017.07.032","article-title":"Deep convolutional neural networks for thermal infrared object tracking","volume":"134","author":"Liu","year":"2017","journal-title":"Knowl. -Based Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/TMM.2020.3008028","article-title":"Learning deep multi-level similarity for thermal infrared object tracking","volume":"23","author":"Liu","year":"2020","journal-title":"IEEE Trans. Multimed."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Q., Yuan, D., Fan, N., Gao, P., Li, X., and He, Z. (2022). Learning dual-level deep representation for thermal infrared tracking. IEEE Trans. Multimed.","DOI":"10.1109\/TMM.2022.3140929"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Minemura, K., Liau, H.F., Monrroy, A., and Kato, S. (2018, January 21\u201323). Lmnet: Real-Time Multiclass Object Detection on Cpu Using 3D Lidar. Proceedings of the 2018 3rd Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Singapore.","DOI":"10.1109\/ACIRS.2018.8467245"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Beltran, J., Guindel, C., Moreno, F.M., Cruzado, D., Garcia, F., and de la Escalera, A. (2018, January 4\u20137). Birdnet: A 3D Object Detection Framework from Lidar Information. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569311"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Shi, S., Wang, X., and Li, H. (2019, January 16\u201320). PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00086"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rummelhard, L., Paigwar, A., Negre, A., and Laugier, C. (2017, January 11\u201314). Ground Estimation and Point Cloud Segmentation using SpatioTemporal Conditional Random Field. Proceedings of the 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, CA, USA.","DOI":"10.1109\/IVS.2017.7995861"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1002\/rob.20255","article-title":"Autonomous driving in urban environments: Boss and the urban challenge","volume":"25","author":"Urmson","year":"2008","journal-title":"J. Field Robot."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Narksri, P., Takeuchi, E., Ninomiya, Y., Morales, Y., Akai, N., and Kawaguchi, N. (2018, January 4\u20137). A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569534"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1007\/s10846-013-9889-4","article-title":"Gaussian-Process-Based Real-Time Ground Segmentation for Autonomous Land Vehicles","volume":"76","author":"Chen","year":"2014","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, M., Stolz, M., Feng, Z., Kunert, M., Henze, R., and K\u00fc\u00e7\u00fckay, F. (2018, January 12\u201314). An adaptive 3D grid-based clustering algorithm for automotive high resolution radar sensor. Proceedings of the IEEE International Conference on Vehicular Electronics and Safety (ICVES), Madrid, Spain.","DOI":"10.1109\/ICVES.2018.8519483"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Scheel, A., Knill, C., Reuter, S., and Dietmayer, K. (2016, January 19\u201322). Multi-sensor multi-object tracking of vehicles using high-resolution radars. Proceedings of the IEEE Intelligent Vehicles Symposium (IV), Gothenburg, Sweden.","DOI":"10.1109\/IVS.2016.7535442"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Dickmann, J., Klappstein, J., Hahn, M., Appenrodt, N., Bloecher, H.L., Werber, K., and Sailer, A. (2016, January 1\u20136). Automotive radar the key technology for autonomous driving: From detection and ranging to environmental understanding. Proceedings of the IEEE Radar Conference (RadarConf), Philadelphia, PA, USA.","DOI":"10.1109\/RADAR.2016.7485214"},{"key":"ref_28","first-page":"1","article-title":"Current State and Development Prospects of Autonomous Haulage at Surface Mines","volume":"174","author":"Voronov","year":"2020","journal-title":"E3S Web Conf. EDP Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Haris, M., and Glowacz, A. (2022). Navigating an Automated Driving Vehicle via the Early Fusion of Multi-Modality. Sensors, 22.","DOI":"10.3390\/s22041425"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1109\/ACCESS.2019.2962554","article-title":"Multi-sensor fusion in automated driving: A survey","volume":"8","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Prakash, A., Chitta, K., and Geiger, A. (2021, January 20\u201325). Multi-modal fusion transformer for end-to-end autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00700"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, Y.J., Park, J., O\u2019Toole, M., and Kitani, K. (2022, January 21\u201324). Modality-Agnostic Learning for Radar-Lidar Fusion in Vehicle Detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPR52688.2022.00099"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.ifacol.2019.08.060","article-title":"A geometric model based 2D LiDAR\/radar sensor fusion for tracking surrounding vehicles","volume":"52","author":"Lee","year":"2019","journal-title":"IFAC-PapersOnLine"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Wagner, T., Feger, R., and Stelzer, A. (2018, January 26\u201328). Modifications of the OPTICS clustering algorithm for short-range radar tracking applications. Proceedings of the 2018 15th European Radar Conference (EuRAD), Madrid, Spain.","DOI":"10.23919\/EuRAD.2018.8546579"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Deng, D. (2020, January 25\u201327). DBSCAN clustering algorithm based on density. Proceedings of the 2020 7th International Forum on Electrical Engineering and Automation (IFEEA), Hefei, China.","DOI":"10.1109\/IFEEA51475.2020.00199"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/5989\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:07:01Z","timestamp":1760141221000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/16\/5989"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,11]]},"references-count":35,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22165989"],"URL":"https:\/\/doi.org\/10.3390\/s22165989","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,11]]}}}