{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T13:24:57Z","timestamp":1762953897930,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T00:00:00Z","timestamp":1675036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Concerning roadside traffic detection applications, and to address the millimeter-wave radar\u2019s missing data problem caused by target occlusion or the absence of features in low-speed conditions, this paper proposes a trajectory compensation method regarding car-following behavior. Referring to the installation scheme of the detector, a coordinate transformation method is presented to unify the radar spatial coordinates with the road coordinates. Considering the driver\u2019s car-following behavior, the optimal velocity model (OV), full velocity difference model (FVD), and the full velocity difference and acceleration (FVDA) model are applied for tracking the vehicle\u2019s trajectory related to the movement of the vehicle ahead. Finally, a data compensation scheme is presented. Taking actual trajectory data as samples, the proposed methods are verifiably useful for compensating for missing data and reconstructing target trajectories. Statistical results of different missing data trajectories demonstrate the rationality of the application of car-following models for the missing data compensation, and the FVDA model performs well compared with the OV and FVD models.<\/jats:p>","DOI":"10.3390\/s23031515","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:28:34Z","timestamp":1675045714000},"page":"1515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Trajectory Compensation Method Considering the Car-Following Behavior for Data Missing of Millimeter-Wave Radar in Roadside Detection Applications"],"prefix":"10.3390","volume":"23","author":[{"given":"Rui","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Transportation, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiqing","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Transportation and Logistics Engineering, Shandong Jiaotong University, Jinan 250399, China"},{"name":"College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunmin","family":"Teng","sequence":"additional","affiliation":[{"name":"College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"31794","DOI":"10.3390\/s151229889","article-title":"Sensing Traffic Density Combining V2V and V2I Wireless Communications","volume":"15","author":"Sanguesa","year":"2015","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"4592124","DOI":"10.1155\/2021\/4592124","article-title":"Traffic Flow Parameters Collection under Variable Illumination Based on Data Fusion","volume":"2021","author":"Jin","year":"2021","journal-title":"J. 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