A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment
In order to conduct risk assessment for collision-free decision making of intelligent vehicles in a complex road traffic environment, it is essential to conduct stable tracking and state estimation of multiple vehicle targets. Therefore, this paper proposes a multitarget tracking method in line with...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-10-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/19/3255 |
| _version_ | 1797551655135215616 |
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| author | Dequan Zeng Lu Xiong Zhuoping Yu Qiping Chen Zhiqiang Fu Zhuoren Li Peizhi Zhang Puhang Xu Zixuan Qian Hongyu Xiao Peiyuan Fang Zhiqiang Li Bo Leng |
| author_facet | Dequan Zeng Lu Xiong Zhuoping Yu Qiping Chen Zhiqiang Fu Zhuoren Li Peizhi Zhang Puhang Xu Zixuan Qian Hongyu Xiao Peiyuan Fang Zhiqiang Li Bo Leng |
| author_sort | Dequan Zeng |
| collection | DOAJ |
| description | In order to conduct risk assessment for collision-free decision making of intelligent vehicles in a complex road traffic environment, it is essential to conduct stable tracking and state estimation of multiple vehicle targets. Therefore, this paper proposes a multitarget tracking method in line with the priority data association rule. Firstly, a standard coordinate turn process model is deduced as the existence of translation and rotation of the vehicle on the two-dimensional ground plane. Secondly, an unscented Kalman filter algorithm is developed due to the nonlinear radar measurement model. Thirdly, a priority data association rule, which puts the targets in a priority queue according to the number of times they are associated, is designed to filter out noise, given that it is unlikely for a vehicle target as an inertial system to appear or disappear instantly in practice. In addition, the data association rule specifies that the closer the target is to the front of the queue, the more prioritized the association with the newly observed target would be. Finally, the track management algorithm is constructed. On this basis, a series of real vehicle tests were conducted on real roads with four typical scenarios. According to the test results, the proposed algorithm is effective in filtering out noise and is suboptimal as the nearest neighbor data association. |
| first_indexed | 2024-03-10T15:48:10Z |
| format | Article |
| id | doaj.art-cbe9847607a24c818123327038b18f60 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T15:48:10Z |
| publishDate | 2020-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-cbe9847607a24c818123327038b18f602023-11-20T16:16:36ZengMDPI AGRemote Sensing2072-42922020-10-011219325510.3390/rs12193255A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk AssessmentDequan Zeng0Lu Xiong1Zhuoping Yu2Qiping Chen3Zhiqiang Fu4Zhuoren Li5Peizhi Zhang6Puhang Xu7Zixuan Qian8Hongyu Xiao9Peiyuan Fang10Zhiqiang Li11Bo Leng12School of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Mechatronics and Vehicle Engineering, East China Jiaotong University, Nanchang 330013, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaSchool of Automotive Studies, Tongji University, Shanghai 201804, ChinaIn order to conduct risk assessment for collision-free decision making of intelligent vehicles in a complex road traffic environment, it is essential to conduct stable tracking and state estimation of multiple vehicle targets. Therefore, this paper proposes a multitarget tracking method in line with the priority data association rule. Firstly, a standard coordinate turn process model is deduced as the existence of translation and rotation of the vehicle on the two-dimensional ground plane. Secondly, an unscented Kalman filter algorithm is developed due to the nonlinear radar measurement model. Thirdly, a priority data association rule, which puts the targets in a priority queue according to the number of times they are associated, is designed to filter out noise, given that it is unlikely for a vehicle target as an inertial system to appear or disappear instantly in practice. In addition, the data association rule specifies that the closer the target is to the front of the queue, the more prioritized the association with the newly observed target would be. Finally, the track management algorithm is constructed. On this basis, a series of real vehicle tests were conducted on real roads with four typical scenarios. According to the test results, the proposed algorithm is effective in filtering out noise and is suboptimal as the nearest neighbor data association.https://www.mdpi.com/2072-4292/12/19/3255multitarget trackingintelligent vehiclerisk assessmentunscented Kalman filterfinite stateflowpriority data association policy |
| spellingShingle | Dequan Zeng Lu Xiong Zhuoping Yu Qiping Chen Zhiqiang Fu Zhuoren Li Peizhi Zhang Puhang Xu Zixuan Qian Hongyu Xiao Peiyuan Fang Zhiqiang Li Bo Leng A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment Remote Sensing multitarget tracking intelligent vehicle risk assessment unscented Kalman filter finite stateflow priority data association policy |
| title | A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment |
| title_full | A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment |
| title_fullStr | A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment |
| title_full_unstemmed | A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment |
| title_short | A Priority Data Association Policy for Multitarget Tracking on Intelligent Vehicle Risk Assessment |
| title_sort | priority data association policy for multitarget tracking on intelligent vehicle risk assessment |
| topic | multitarget tracking intelligent vehicle risk assessment unscented Kalman filter finite stateflow priority data association policy |
| url | https://www.mdpi.com/2072-4292/12/19/3255 |
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