Trailer angle estimation using radar point clouds
Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. T...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Olutomilayo, Kunle T. [verfasserIn] Bahramgiri, Mojtaba [verfasserIn] Nooshabadi, Saeid [verfasserIn] Oh, JinHyoung [verfasserIn] Lakehal-Ayat, Mohsen [verfasserIn] Rogan, Douglas [verfasserIn] Fuhrmann, Daniel R. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Signal processing - Amsterdam [u.a.] : Elsevier, 1979, 188 |
|---|---|
| Übergeordnetes Werk: |
volume:188 |
| DOI / URN: |
10.1016/j.sigpro.2021.108221 |
|---|
| Katalog-ID: |
ELV00638823X |
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| 245 | 1 | 0 | |a Trailer angle estimation using radar point clouds |
| 264 | 1 | |c 2021 | |
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| 520 | |a Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. | ||
| 650 | 4 | |a Hitch angle estimation | |
| 650 | 4 | |a Trailer rotation | |
| 650 | 4 | |a Radar tracker | |
| 650 | 4 | |a Point set registration | |
| 650 | 4 | |a Track-before-detect | |
| 650 | 4 | |a Kalman filter | |
| 700 | 1 | |a Bahramgiri, Mojtaba |e verfasserin |4 aut | |
| 700 | 1 | |a Nooshabadi, Saeid |e verfasserin |4 aut | |
| 700 | 1 | |a Oh, JinHyoung |e verfasserin |4 aut | |
| 700 | 1 | |a Lakehal-Ayat, Mohsen |e verfasserin |4 aut | |
| 700 | 1 | |a Rogan, Douglas |e verfasserin |4 aut | |
| 700 | 1 | |a Fuhrmann, Daniel R. |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Signal processing |d Amsterdam [u.a.] : Elsevier, 1979 |g 188 |h Online-Ressource |w (DE-627)265784166 |w (DE-600)1466346-6 |w (DE-576)074891022 |7 nnns |
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10.1016/j.sigpro.2021.108221 doi (DE-627)ELV00638823X (ELSEVIER)S0165-1684(21)00258-9 DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl Olutomilayo, Kunle T. verfasserin aut Trailer angle estimation using radar point clouds 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. Hitch angle estimation Trailer rotation Radar tracker Point set registration Track-before-detect Kalman filter Bahramgiri, Mojtaba verfasserin aut Nooshabadi, Saeid verfasserin aut Oh, JinHyoung verfasserin aut Lakehal-Ayat, Mohsen verfasserin aut Rogan, Douglas verfasserin aut Fuhrmann, Daniel R. verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 188 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:188 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 188 |
| spelling |
10.1016/j.sigpro.2021.108221 doi (DE-627)ELV00638823X (ELSEVIER)S0165-1684(21)00258-9 DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl Olutomilayo, Kunle T. verfasserin aut Trailer angle estimation using radar point clouds 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. Hitch angle estimation Trailer rotation Radar tracker Point set registration Track-before-detect Kalman filter Bahramgiri, Mojtaba verfasserin aut Nooshabadi, Saeid verfasserin aut Oh, JinHyoung verfasserin aut Lakehal-Ayat, Mohsen verfasserin aut Rogan, Douglas verfasserin aut Fuhrmann, Daniel R. verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 188 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:188 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 188 |
| allfields_unstemmed |
10.1016/j.sigpro.2021.108221 doi (DE-627)ELV00638823X (ELSEVIER)S0165-1684(21)00258-9 DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl Olutomilayo, Kunle T. verfasserin aut Trailer angle estimation using radar point clouds 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. Hitch angle estimation Trailer rotation Radar tracker Point set registration Track-before-detect Kalman filter Bahramgiri, Mojtaba verfasserin aut Nooshabadi, Saeid verfasserin aut Oh, JinHyoung verfasserin aut Lakehal-Ayat, Mohsen verfasserin aut Rogan, Douglas verfasserin aut Fuhrmann, Daniel R. verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 188 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:188 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 188 |
| allfieldsGer |
10.1016/j.sigpro.2021.108221 doi (DE-627)ELV00638823X (ELSEVIER)S0165-1684(21)00258-9 DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl Olutomilayo, Kunle T. verfasserin aut Trailer angle estimation using radar point clouds 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. Hitch angle estimation Trailer rotation Radar tracker Point set registration Track-before-detect Kalman filter Bahramgiri, Mojtaba verfasserin aut Nooshabadi, Saeid verfasserin aut Oh, JinHyoung verfasserin aut Lakehal-Ayat, Mohsen verfasserin aut Rogan, Douglas verfasserin aut Fuhrmann, Daniel R. verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 188 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:188 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 188 |
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10.1016/j.sigpro.2021.108221 doi (DE-627)ELV00638823X (ELSEVIER)S0165-1684(21)00258-9 DE-627 ger DE-627 rda eng 004 000 DE-600 53.73 bkl Olutomilayo, Kunle T. verfasserin aut Trailer angle estimation using radar point clouds 2021 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. Hitch angle estimation Trailer rotation Radar tracker Point set registration Track-before-detect Kalman filter Bahramgiri, Mojtaba verfasserin aut Nooshabadi, Saeid verfasserin aut Oh, JinHyoung verfasserin aut Lakehal-Ayat, Mohsen verfasserin aut Rogan, Douglas verfasserin aut Fuhrmann, Daniel R. verfasserin aut Enthalten in Signal processing Amsterdam [u.a.] : Elsevier, 1979 188 Online-Ressource (DE-627)265784166 (DE-600)1466346-6 (DE-576)074891022 nnns volume:188 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_224 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4393 53.73 Nachrichtenübertragung AR 188 |
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Hitch angle estimation Trailer rotation Radar tracker Point set registration Track-before-detect Kalman filter |
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004 000 DE-600 53.73 bkl Trailer angle estimation using radar point clouds Hitch angle estimation Trailer rotation Radar tracker Point set registration Track-before-detect Kalman filter |
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ddc 004 bkl 53.73 misc Hitch angle estimation misc Trailer rotation misc Radar tracker misc Point set registration misc Track-before-detect misc Kalman filter |
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ddc 004 bkl 53.73 misc Hitch angle estimation misc Trailer rotation misc Radar tracker misc Point set registration misc Track-before-detect misc Kalman filter |
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ddc 004 bkl 53.73 misc Hitch angle estimation misc Trailer rotation misc Radar tracker misc Point set registration misc Track-before-detect misc Kalman filter |
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Trailer angle estimation using radar point clouds |
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Trailer angle estimation using radar point clouds |
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Olutomilayo, Kunle T. |
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Signal processing |
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Olutomilayo, Kunle T. Bahramgiri, Mojtaba Nooshabadi, Saeid Oh, JinHyoung Lakehal-Ayat, Mohsen Rogan, Douglas Fuhrmann, Daniel R. |
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Olutomilayo, Kunle T. |
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10.1016/j.sigpro.2021.108221 |
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004 000 |
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trailer angle estimation using radar point clouds |
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Trailer angle estimation using radar point clouds |
| abstract |
Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. |
| abstractGer |
Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. |
| abstract_unstemmed |
Algorithms for trailer control and backup need to keep track of the trailer angle, therefore, the angle needs to be determined. This work shows that the angle can be estimated using 2D point clouds collected from two automotive radars installed in the taillight fixtures of a trailer-coupled truck. The detection threshold of each radar is reduced to allow more detections of the trailer. A tracking procedure is introduced to find the trailer detections as the trailer rotates; the tracked set of detections are then compared with one or more reference sets to estimate the angle. Estimated angles are further refined with a Kalman filter to obtain smooth estimates of the angle. The algorithm is tested on 15 datasets; each dataset is obtained in an experiment in which the trailer is rotated up to about 40 ∘ in both radar directions. The estimates from all the datasets result in a global root mean squared error of about 0 . 9 ∘ for up to 10 ∘ absolute trailer rotation; about 1 . 3 ∘ for up to 20 ∘ absolute rotation; about 1 . 5 ∘ for up to 30 ∘ absolute rotation; and about 1 . 9 ∘ for all trailer angles considered. The algorithm executes in about 3 ms on a typical personal computer. |
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| title_short |
Trailer angle estimation using radar point clouds |
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Bahramgiri, Mojtaba Nooshabadi, Saeid Oh, JinHyoung Lakehal-Ayat, Mohsen Rogan, Douglas Fuhrmann, Daniel R. |
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