A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods
• Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving di...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Ge, Huimin [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022transfer abstract |
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| Schlagwörter: |
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| Umfang: |
14 |
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| Übergeordnetes Werk: |
Enthalten in: Improving point correspondence in cephalograms by using a two-stage rectified point transform - Tam, Weng-Kong ELSEVIER, 2015, a joint publication of the National Safety Council and Pergamon, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:82 ; year:2022 ; pages:261-274 ; extent:14 |
| Links: |
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| DOI / URN: |
10.1016/j.jsr.2022.06.002 |
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| 520 | |a • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. | ||
| 520 | |a • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. | ||
| 650 | 7 | |a Driving behavior |2 Elsevier | |
| 650 | 7 | |a Driving distraction recognition |2 Elsevier | |
| 650 | 7 | |a Co-occurrence analysis |2 Elsevier | |
| 650 | 7 | |a Bibliometric analysis |2 Elsevier | |
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| 700 | 1 | |a Zheng, Mingqiang |4 oth | |
| 700 | 1 | |a Lu, Ying |4 oth | |
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10.1016/j.jsr.2022.06.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001877.pica (DE-627)ELV058681094 (ELSEVIER)S0022-4375(22)00078-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ge, Huimin verfasserin aut A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. Driving behavior Elsevier Driving distraction recognition Elsevier Co-occurrence analysis Elsevier Bibliometric analysis Elsevier Bo, Yunyu oth Sun, Hui oth Zheng, Mingqiang oth Lu, Ying oth Enthalten in Elsevier Science Tam, Weng-Kong ELSEVIER Improving point correspondence in cephalograms by using a two-stage rectified point transform 2015 a joint publication of the National Safety Council and Pergamon Amsterdam [u.a.] (DE-627)ELV023925248 volume:82 year:2022 pages:261-274 extent:14 https://doi.org/10.1016/j.jsr.2022.06.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_62 GBV_ILN_104 GBV_ILN_110 GBV_ILN_120 GBV_ILN_123 GBV_ILN_130 GBV_ILN_135 GBV_ILN_181 GBV_ILN_203 GBV_ILN_294 GBV_ILN_342 GBV_ILN_721 GBV_ILN_732 GBV_ILN_788 GBV_ILN_2004 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 82 2022 261-274 14 |
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10.1016/j.jsr.2022.06.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001877.pica (DE-627)ELV058681094 (ELSEVIER)S0022-4375(22)00078-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ge, Huimin verfasserin aut A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. Driving behavior Elsevier Driving distraction recognition Elsevier Co-occurrence analysis Elsevier Bibliometric analysis Elsevier Bo, Yunyu oth Sun, Hui oth Zheng, Mingqiang oth Lu, Ying oth Enthalten in Elsevier Science Tam, Weng-Kong ELSEVIER Improving point correspondence in cephalograms by using a two-stage rectified point transform 2015 a joint publication of the National Safety Council and Pergamon Amsterdam [u.a.] (DE-627)ELV023925248 volume:82 year:2022 pages:261-274 extent:14 https://doi.org/10.1016/j.jsr.2022.06.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_62 GBV_ILN_104 GBV_ILN_110 GBV_ILN_120 GBV_ILN_123 GBV_ILN_130 GBV_ILN_135 GBV_ILN_181 GBV_ILN_203 GBV_ILN_294 GBV_ILN_342 GBV_ILN_721 GBV_ILN_732 GBV_ILN_788 GBV_ILN_2004 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 82 2022 261-274 14 |
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10.1016/j.jsr.2022.06.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001877.pica (DE-627)ELV058681094 (ELSEVIER)S0022-4375(22)00078-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ge, Huimin verfasserin aut A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. Driving behavior Elsevier Driving distraction recognition Elsevier Co-occurrence analysis Elsevier Bibliometric analysis Elsevier Bo, Yunyu oth Sun, Hui oth Zheng, Mingqiang oth Lu, Ying oth Enthalten in Elsevier Science Tam, Weng-Kong ELSEVIER Improving point correspondence in cephalograms by using a two-stage rectified point transform 2015 a joint publication of the National Safety Council and Pergamon Amsterdam [u.a.] (DE-627)ELV023925248 volume:82 year:2022 pages:261-274 extent:14 https://doi.org/10.1016/j.jsr.2022.06.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_62 GBV_ILN_104 GBV_ILN_110 GBV_ILN_120 GBV_ILN_123 GBV_ILN_130 GBV_ILN_135 GBV_ILN_181 GBV_ILN_203 GBV_ILN_294 GBV_ILN_342 GBV_ILN_721 GBV_ILN_732 GBV_ILN_788 GBV_ILN_2004 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 82 2022 261-274 14 |
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10.1016/j.jsr.2022.06.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001877.pica (DE-627)ELV058681094 (ELSEVIER)S0022-4375(22)00078-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ge, Huimin verfasserin aut A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. Driving behavior Elsevier Driving distraction recognition Elsevier Co-occurrence analysis Elsevier Bibliometric analysis Elsevier Bo, Yunyu oth Sun, Hui oth Zheng, Mingqiang oth Lu, Ying oth Enthalten in Elsevier Science Tam, Weng-Kong ELSEVIER Improving point correspondence in cephalograms by using a two-stage rectified point transform 2015 a joint publication of the National Safety Council and Pergamon Amsterdam [u.a.] (DE-627)ELV023925248 volume:82 year:2022 pages:261-274 extent:14 https://doi.org/10.1016/j.jsr.2022.06.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_62 GBV_ILN_104 GBV_ILN_110 GBV_ILN_120 GBV_ILN_123 GBV_ILN_130 GBV_ILN_135 GBV_ILN_181 GBV_ILN_203 GBV_ILN_294 GBV_ILN_342 GBV_ILN_721 GBV_ILN_732 GBV_ILN_788 GBV_ILN_2004 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 82 2022 261-274 14 |
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10.1016/j.jsr.2022.06.002 doi /cbs_pica/cbs_olc/import_discovery/elsevier/einzuspielen/GBV00000000001877.pica (DE-627)ELV058681094 (ELSEVIER)S0022-4375(22)00078-0 DE-627 ger DE-627 rakwb eng 610 VZ 570 VZ 610 VZ 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl 42.15 bkl Ge, Huimin verfasserin aut A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods 2022transfer abstract 14 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. • Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. Driving behavior Elsevier Driving distraction recognition Elsevier Co-occurrence analysis Elsevier Bibliometric analysis Elsevier Bo, Yunyu oth Sun, Hui oth Zheng, Mingqiang oth Lu, Ying oth Enthalten in Elsevier Science Tam, Weng-Kong ELSEVIER Improving point correspondence in cephalograms by using a two-stage rectified point transform 2015 a joint publication of the National Safety Council and Pergamon Amsterdam [u.a.] (DE-627)ELV023925248 volume:82 year:2022 pages:261-274 extent:14 https://doi.org/10.1016/j.jsr.2022.06.002 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA GBV_ILN_22 GBV_ILN_24 GBV_ILN_40 GBV_ILN_62 GBV_ILN_104 GBV_ILN_110 GBV_ILN_120 GBV_ILN_123 GBV_ILN_130 GBV_ILN_135 GBV_ILN_181 GBV_ILN_203 GBV_ILN_294 GBV_ILN_342 GBV_ILN_721 GBV_ILN_732 GBV_ILN_788 GBV_ILN_2004 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ 42.15 Zellbiologie VZ AR 82 2022 261-274 14 |
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Enthalten in Improving point correspondence in cephalograms by using a two-stage rectified point transform Amsterdam [u.a.] volume:82 year:2022 pages:261-274 extent:14 |
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Improving point correspondence in cephalograms by using a two-stage rectified point transform |
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A review of research on driving distraction based on bibliometrics and co-occurrence: Focus on driving distraction recognition methods |
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• Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. |
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• Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. |
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• Co-occurrence network analysis of driving distraction literature in terms of time, country, publication, author and keywords. • Deep learning algorithm recognition model based on video surveillance data source has become the mainstream hotspot distraction recognition method. • Among the driving distraction detection methods based on video images, the accuracy of CNN is higher than that of random forest, but the real-time performance of random forest is better than that of CNN. • Based on ECG and GER to detect driving distraction, there is contact with the driver, the accuracy is high, but the real-time performance is poor. • Driving volatility can be used as a measure of driving distraction, and it is of great significance to study driving distraction. |
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