Multi-vehicle tracking with microscopic traffic flow model-based particle filtering
This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Song, Dan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019transfer abstract |
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| Schlagwörter: |
Microscopic traffic flow model |
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| Umfang: |
8 |
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| Übergeordnetes Werk: |
Enthalten in: Epithelial morphogenesis in organoids - Lee, Byung Ho ELSEVIER, 2021, a journal of IFAC, the International Federation of Automatic Control, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:105 ; year:2019 ; pages:28-35 ; extent:8 |
| Links: |
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| DOI / URN: |
10.1016/j.automatica.2019.03.016 |
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| Katalog-ID: |
ELV047155914 |
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| 520 | |a This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. | ||
| 520 | |a This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. | ||
| 650 | 7 | |a Microscopic traffic flow model |2 Elsevier | |
| 650 | 7 | |a Road traffic |2 Elsevier | |
| 650 | 7 | |a Posterior Cramer–Rao lower bound |2 Elsevier | |
| 650 | 7 | |a Multi-vehicle tracking |2 Elsevier | |
| 650 | 7 | |a Particle filter |2 Elsevier | |
| 700 | 1 | |a Tharmarasa, Ratnasingham |4 oth | |
| 700 | 1 | |a Florea, Mihai C. |4 oth | |
| 700 | 1 | |a Duclos-Hindie, Nicolas |4 oth | |
| 700 | 1 | |a Fernando, Xavier N. |4 oth | |
| 700 | 1 | |a Kirubarajan, Thiagalingam |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier, Pergamon Press |a Lee, Byung Ho ELSEVIER |t Epithelial morphogenesis in organoids |d 2021 |d a journal of IFAC, the International Federation of Automatic Control |g Amsterdam [u.a.] |w (DE-627)ELV007443196 |
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10.1016/j.automatica.2019.03.016 doi GBV00000000000659.pica (DE-627)ELV047155914 (ELSEVIER)S0005-1098(19)30137-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.48 bkl Song, Dan verfasserin aut Multi-vehicle tracking with microscopic traffic flow model-based particle filtering 2019transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. Microscopic traffic flow model Elsevier Road traffic Elsevier Posterior Cramer–Rao lower bound Elsevier Multi-vehicle tracking Elsevier Particle filter Elsevier Tharmarasa, Ratnasingham oth Florea, Mihai C. oth Duclos-Hindie, Nicolas oth Fernando, Xavier N. oth Kirubarajan, Thiagalingam oth Enthalten in Elsevier, Pergamon Press Lee, Byung Ho ELSEVIER Epithelial morphogenesis in organoids 2021 a journal of IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV007443196 volume:105 year:2019 pages:28-35 extent:8 https://doi.org/10.1016/j.automatica.2019.03.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.48 Medizinische Genetik VZ AR 105 2019 28-35 8 |
| spelling |
10.1016/j.automatica.2019.03.016 doi GBV00000000000659.pica (DE-627)ELV047155914 (ELSEVIER)S0005-1098(19)30137-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.48 bkl Song, Dan verfasserin aut Multi-vehicle tracking with microscopic traffic flow model-based particle filtering 2019transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. Microscopic traffic flow model Elsevier Road traffic Elsevier Posterior Cramer–Rao lower bound Elsevier Multi-vehicle tracking Elsevier Particle filter Elsevier Tharmarasa, Ratnasingham oth Florea, Mihai C. oth Duclos-Hindie, Nicolas oth Fernando, Xavier N. oth Kirubarajan, Thiagalingam oth Enthalten in Elsevier, Pergamon Press Lee, Byung Ho ELSEVIER Epithelial morphogenesis in organoids 2021 a journal of IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV007443196 volume:105 year:2019 pages:28-35 extent:8 https://doi.org/10.1016/j.automatica.2019.03.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.48 Medizinische Genetik VZ AR 105 2019 28-35 8 |
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10.1016/j.automatica.2019.03.016 doi GBV00000000000659.pica (DE-627)ELV047155914 (ELSEVIER)S0005-1098(19)30137-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.48 bkl Song, Dan verfasserin aut Multi-vehicle tracking with microscopic traffic flow model-based particle filtering 2019transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. Microscopic traffic flow model Elsevier Road traffic Elsevier Posterior Cramer–Rao lower bound Elsevier Multi-vehicle tracking Elsevier Particle filter Elsevier Tharmarasa, Ratnasingham oth Florea, Mihai C. oth Duclos-Hindie, Nicolas oth Fernando, Xavier N. oth Kirubarajan, Thiagalingam oth Enthalten in Elsevier, Pergamon Press Lee, Byung Ho ELSEVIER Epithelial morphogenesis in organoids 2021 a journal of IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV007443196 volume:105 year:2019 pages:28-35 extent:8 https://doi.org/10.1016/j.automatica.2019.03.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.48 Medizinische Genetik VZ AR 105 2019 28-35 8 |
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10.1016/j.automatica.2019.03.016 doi GBV00000000000659.pica (DE-627)ELV047155914 (ELSEVIER)S0005-1098(19)30137-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.48 bkl Song, Dan verfasserin aut Multi-vehicle tracking with microscopic traffic flow model-based particle filtering 2019transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. Microscopic traffic flow model Elsevier Road traffic Elsevier Posterior Cramer–Rao lower bound Elsevier Multi-vehicle tracking Elsevier Particle filter Elsevier Tharmarasa, Ratnasingham oth Florea, Mihai C. oth Duclos-Hindie, Nicolas oth Fernando, Xavier N. oth Kirubarajan, Thiagalingam oth Enthalten in Elsevier, Pergamon Press Lee, Byung Ho ELSEVIER Epithelial morphogenesis in organoids 2021 a journal of IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV007443196 volume:105 year:2019 pages:28-35 extent:8 https://doi.org/10.1016/j.automatica.2019.03.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.48 Medizinische Genetik VZ AR 105 2019 28-35 8 |
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10.1016/j.automatica.2019.03.016 doi GBV00000000000659.pica (DE-627)ELV047155914 (ELSEVIER)S0005-1098(19)30137-2 DE-627 ger DE-627 rakwb eng 610 VZ 44.48 bkl Song, Dan verfasserin aut Multi-vehicle tracking with microscopic traffic flow model-based particle filtering 2019transfer abstract 8 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. Microscopic traffic flow model Elsevier Road traffic Elsevier Posterior Cramer–Rao lower bound Elsevier Multi-vehicle tracking Elsevier Particle filter Elsevier Tharmarasa, Ratnasingham oth Florea, Mihai C. oth Duclos-Hindie, Nicolas oth Fernando, Xavier N. oth Kirubarajan, Thiagalingam oth Enthalten in Elsevier, Pergamon Press Lee, Byung Ho ELSEVIER Epithelial morphogenesis in organoids 2021 a journal of IFAC, the International Federation of Automatic Control Amsterdam [u.a.] (DE-627)ELV007443196 volume:105 year:2019 pages:28-35 extent:8 https://doi.org/10.1016/j.automatica.2019.03.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 44.48 Medizinische Genetik VZ AR 105 2019 28-35 8 |
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English |
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Enthalten in Epithelial morphogenesis in organoids Amsterdam [u.a.] volume:105 year:2019 pages:28-35 extent:8 |
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Enthalten in Epithelial morphogenesis in organoids Amsterdam [u.a.] volume:105 year:2019 pages:28-35 extent:8 |
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Microscopic traffic flow model Road traffic Posterior Cramer–Rao lower bound Multi-vehicle tracking Particle filter |
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Epithelial morphogenesis in organoids |
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Song, Dan @@aut@@ Tharmarasa, Ratnasingham @@oth@@ Florea, Mihai C. @@oth@@ Duclos-Hindie, Nicolas @@oth@@ Fernando, Xavier N. @@oth@@ Kirubarajan, Thiagalingam @@oth@@ |
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Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. 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This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. |
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This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. |
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This paper addresses the problem of tracking multiple vehicles on multi-lane roads with consideration for interactions among vehicles. Due to limited lane resources and traffic heterogeneity, vehicles have to interact with neighboring vehicles while moving along roads to improve their navigability and safety, resulting in highly dependent motions. However, multitarget tracking algorithms generally assume that targets move independently of one another. To address this limitation, using the microscopic traffic flow (MTF) model for modeling vehicle dynamics in the presence of interactions with surrounding traffic, an MTF-based tracking algorithm is proposed under the particle filter (PF) framework. The recursive maximum likelihood (RML) method is integrated into the PF to estimate unknown parameters in the MTF model. The posterior Cramer–Rao lower bound (PCRLB) is also derived for this problem. The performance of the proposed MTF-PF algorithm is compared with those of existing algorithms for multi-vehicle tracking on multi-lane roads. Numerical results show that the proposed algorithm requires less prior information while yielding more accurate and consistent tracks. |
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