{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:39:59Z","timestamp":1781714399329,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,3]],"date-time":"2023-08-03T00:00:00Z","timestamp":1691020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key Research and Development Program of China","award":["2022YFE0101000"],"award-info":[{"award-number":["2022YFE0101000"]}]},{"name":"The National Key Research and Development Program of China","award":["2022CQBSHTB2010"],"award-info":[{"award-number":["2022CQBSHTB2010"]}]},{"name":"The National Key Research and Development Program of China","award":["22XJZXZD05"],"award-info":[{"award-number":["22XJZXZD05"]}]},{"name":"Chongqing Postdoctoral Research Special Funding Project","award":["2022YFE0101000"],"award-info":[{"award-number":["2022YFE0101000"]}]},{"name":"Chongqing Postdoctoral Research Special Funding Project","award":["2022CQBSHTB2010"],"award-info":[{"award-number":["2022CQBSHTB2010"]}]},{"name":"Chongqing Postdoctoral Research Special Funding Project","award":["22XJZXZD05"],"award-info":[{"award-number":["22XJZXZD05"]}]},{"name":"school-level research projects","award":["2022YFE0101000"],"award-info":[{"award-number":["2022YFE0101000"]}]},{"name":"school-level research projects","award":["2022CQBSHTB2010"],"award-info":[{"award-number":["2022CQBSHTB2010"]}]},{"name":"school-level research projects","award":["22XJZXZD05"],"award-info":[{"award-number":["22XJZXZD05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.<\/jats:p>","DOI":"10.3390\/s23156920","type":"journal-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T09:28:29Z","timestamp":1691141309000},"page":"6920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2803-9439","authenticated-orcid":false,"given":"Junren","family":"Shi","sequence":"first","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingjie","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Changhao","family":"Piao","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongquan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5110","DOI":"10.3390\/s23115110","article-title":"Fault Diagnosis of the Autonomous Driving Perception System Based on Information Fusion","volume":"23","author":"Hou","year":"2023","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"103570","DOI":"10.1016\/j.robot.2020.103570","article-title":"Planning the trajectory of an autonomous wheel loader and tracking its trajectory via adaptive model predictive control","volume":"131","author":"Shi","year":"2020","journal-title":"Robot. 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