{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T21:02:21Z","timestamp":1760821341490,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,12,1]],"date-time":"2022-12-01T00:00:00Z","timestamp":1669852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High Technology Research Project of Xiangyang Science and Technology Bureau","award":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"],"award-info":[{"award-number":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"],"award-info":[{"award-number":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"]}]},{"name":"National Natural Science Foundation of China","award":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"],"award-info":[{"award-number":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"]}]},{"name":"Shanghai Sailing Program","award":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"],"award-info":[{"award-number":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"],"award-info":[{"award-number":["2020ABH001273","2022-5-YB-01","61936014","21YF1450100","2022-5-YB-01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>On high-speed roads, there are certain blind areas within the radar coverage, especially when the blind zone is in curved road sections; because the radar does not have the measurement point information in multiple frames, it is easy to have a large deviation between the real trajectory and the filtered trajectory. In this paper, we propose a track prediction method combined with a high-precision map to solve the problem of scattered tracks when the targets are in the blind area. First, the lane centerline is fitted to the upstream and downstream lane edges obtained from the high-precision map in certain steps, and the off-north angle at each fitted point is obtained. Secondly, the normal trajectory is predicted according to the conventional method; for the extrapolated trajectory, the northerly angle of the lane centerline at the current position of the trajectory is obtained, the current coordinate system is converted from the north-east-up (ENU) coordinate system to the vehicle coordinate system, and the lateral velocity of the target is set to zero in the vehicle coordinate system to reduce the error caused by the lateral velocity drag of the target. Finally, the normal trajectory is updated and corrected, and the normal and extrapolated trajectories are managed and reported. The experimental results show that the accuracy and convergence effect of the proposed methods are much better than the traditional methods.<\/jats:p>","DOI":"10.3390\/s22239371","type":"journal-article","created":{"date-parts":[[2022,12,2]],"date-time":"2022-12-02T03:28:04Z","timestamp":1669951684000},"page":"9371","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Multi-Target Tracking Algorithm Combined with High-Precision Map"],"prefix":"10.3390","volume":"22","author":[{"given":"Qingru","family":"An","sequence":"first","affiliation":[{"name":"Beijing Rxbit Electronic Technology Co., Ltd., Beijing 100081, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3900-647X","authenticated-orcid":false,"given":"Yawen","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Juan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Physics & Electronic Engineering, Hubei University of Arts and Science, Xiangyang 441053, China"}]},{"given":"Sijia","family":"Wang","sequence":"additional","affiliation":[{"name":"The Trade Desk Inc., 42 N Chestnut St., Ventura, CA 93001, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5021-3686","authenticated-orcid":false,"given":"Fengxia","family":"Han","sequence":"additional","affiliation":[{"name":"School of Software Engineering, Tongji University, Shanghai 201804, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MRA.2005.1411416","article-title":"Tracking all traffic: Computer vision algorithms for monitoring vehicles, individuals, and crowds","volume":"12","author":"Maurin","year":"2005","journal-title":"IEEE Robot. 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