Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory
Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challengi...
Ausführliche Beschreibung
Autor*in: |
Kai Song [verfasserIn] Fuqiang Liu [verfasserIn] Chao Wang [verfasserIn] Ping Wang [verfasserIn] Geyong Min [verfasserIn] |
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E-Artikel |
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Sprache: |
Englisch |
Erschienen: |
2020 |
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Übergeordnetes Werk: |
In: IEEE Access - IEEE, 2014, 8(2020), Seite 175521-175534 |
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Übergeordnetes Werk: |
volume:8 ; year:2020 ; pages:175521-175534 |
Links: |
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DOI / URN: |
10.1109/ACCESS.2020.3026392 |
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Katalog-ID: |
DOAJ056912633 |
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520 | |a Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. | ||
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10.1109/ACCESS.2020.3026392 doi (DE-627)DOAJ056912633 (DE-599)DOAJa13e587e7bb740fdb7ff6310a9f9b865 DE-627 ger DE-627 rakwb eng TK1-9971 Kai Song verfasserin aut Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. Driving behavior analysis intelligent transportation systems random matrix theory Electrical engineering. Electronics. Nuclear engineering Fuqiang Liu verfasserin aut Chao Wang verfasserin aut Ping Wang verfasserin aut Geyong Min verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 175521-175534 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:175521-175534 https://doi.org/10.1109/ACCESS.2020.3026392 kostenfrei https://doaj.org/article/a13e587e7bb740fdb7ff6310a9f9b865 kostenfrei https://ieeexplore.ieee.org/document/9205240/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 175521-175534 |
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10.1109/ACCESS.2020.3026392 doi (DE-627)DOAJ056912633 (DE-599)DOAJa13e587e7bb740fdb7ff6310a9f9b865 DE-627 ger DE-627 rakwb eng TK1-9971 Kai Song verfasserin aut Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. Driving behavior analysis intelligent transportation systems random matrix theory Electrical engineering. Electronics. Nuclear engineering Fuqiang Liu verfasserin aut Chao Wang verfasserin aut Ping Wang verfasserin aut Geyong Min verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 175521-175534 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:175521-175534 https://doi.org/10.1109/ACCESS.2020.3026392 kostenfrei https://doaj.org/article/a13e587e7bb740fdb7ff6310a9f9b865 kostenfrei https://ieeexplore.ieee.org/document/9205240/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 175521-175534 |
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10.1109/ACCESS.2020.3026392 doi (DE-627)DOAJ056912633 (DE-599)DOAJa13e587e7bb740fdb7ff6310a9f9b865 DE-627 ger DE-627 rakwb eng TK1-9971 Kai Song verfasserin aut Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. Driving behavior analysis intelligent transportation systems random matrix theory Electrical engineering. Electronics. Nuclear engineering Fuqiang Liu verfasserin aut Chao Wang verfasserin aut Ping Wang verfasserin aut Geyong Min verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 175521-175534 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:175521-175534 https://doi.org/10.1109/ACCESS.2020.3026392 kostenfrei https://doaj.org/article/a13e587e7bb740fdb7ff6310a9f9b865 kostenfrei https://ieeexplore.ieee.org/document/9205240/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 175521-175534 |
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10.1109/ACCESS.2020.3026392 doi (DE-627)DOAJ056912633 (DE-599)DOAJa13e587e7bb740fdb7ff6310a9f9b865 DE-627 ger DE-627 rakwb eng TK1-9971 Kai Song verfasserin aut Driving Stability Analysis Using Naturalistic Driving Data With Random Matrix Theory 2020 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. Driving behavior analysis intelligent transportation systems random matrix theory Electrical engineering. Electronics. Nuclear engineering Fuqiang Liu verfasserin aut Chao Wang verfasserin aut Ping Wang verfasserin aut Geyong Min verfasserin aut In IEEE Access IEEE, 2014 8(2020), Seite 175521-175534 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:8 year:2020 pages:175521-175534 https://doi.org/10.1109/ACCESS.2020.3026392 kostenfrei https://doaj.org/article/a13e587e7bb740fdb7ff6310a9f9b865 kostenfrei https://ieeexplore.ieee.org/document/9205240/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 8 2020 175521-175534 |
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Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. |
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Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. |
abstract_unstemmed |
Driving behavior analysis has diverse applications in intelligent transportation systems (ITSs). The naturalistic driving data potentially contain rich information regarding human drivers' habits and skills in practical and natural driving conditions. But mining knowledge from them is challenging. In this paper, we propose a novel approach for analyzing driving stability using naturalistic driving data. Our method can extract features, based on the random matrix theory, to reflect the statistical difference between actual driving data and the data that would be generated by a theoretically ideal driver, and thus imply the skillful level of a driver in terms of vehicle control in both longitudinal and lateral directions. The execution of our method on a practical ITS dataset is conducted. Using the extracted features, a driving behavior analysis application that partitions drivers into clusters to identify common driving stability characteristics is demonstrated and discussed. |
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score |
7.3998356 |