Multi-source information fusion for drowsy driving detection based on wireless sensor networks
Drowsy driving is a major cause of road accidents. This paper analyses the drivers' behavior in the state of fatigue driving and introduces the latest developments of drowsy driving detection technology. In this study we also propose a drowsy driving detection based on the driver's physiol...
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2023
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my.uniten.dspace-294432023-12-28T12:13:07Z Multi-source information fusion for drowsy driving detection based on wireless sensor networks Wei L. Mukhopadhyay S.C. Jidin R. Chen C.-P. 55726997800 24479163700 6508169028 23491392900 driver behaviour drowsy driving wireless sensor networks Automobile drivers Hierarchical systems Highway accidents Physiological models Contextual information Driver behaviour Drowsy driving Electroencephalographic (EEG) Multi-source information fusion Physiological signals Response characteristic Transportation control Wireless sensor networks Drowsy driving is a major cause of road accidents. This paper analyses the drivers' behavior in the state of fatigue driving and introduces the latest developments of drowsy driving detection technology. In this study we also propose a drowsy driving detection based on the driver's physiological signals such as eye activity measures, the inclination of the driver's head, sagging posture, heart beat rate, skin electric potential, and electroencephalographic (EEG) activities, as well as response characteristics, decline in gripping force on the steering wheel and lane keeping characteristics. By developing a hierarchical model that is able to collect the sensing data, analyze the driving behavior and the reactions to the driver, it can provide a safe and a comfortable driving environment. Combining different indications of drowsiness and processing the contextual information to predict whether a driver is drowsy, the system not only issues a warning for the driver, but also provides the drowsy driving information to transportation control center and other vehicles if necessary. � 2013 IEEE. Final 2023-12-28T04:13:07Z 2023-12-28T04:13:07Z 2013 Conference paper 10.1109/ICSensT.2013.6727771 2-s2.0-84897880672 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897880672&doi=10.1109%2fICSensT.2013.6727771&partnerID=40&md5=896cfbaf922695733e56594548e18399 https://irepository.uniten.edu.my/handle/123456789/29443 6727771 850 857 Scopus |
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driver behaviour drowsy driving wireless sensor networks Automobile drivers Hierarchical systems Highway accidents Physiological models Contextual information Driver behaviour Drowsy driving Electroencephalographic (EEG) Multi-source information fusion Physiological signals Response characteristic Transportation control Wireless sensor networks |
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driver behaviour drowsy driving wireless sensor networks Automobile drivers Hierarchical systems Highway accidents Physiological models Contextual information Driver behaviour Drowsy driving Electroencephalographic (EEG) Multi-source information fusion Physiological signals Response characteristic Transportation control Wireless sensor networks Wei L. Mukhopadhyay S.C. Jidin R. Chen C.-P. Multi-source information fusion for drowsy driving detection based on wireless sensor networks |
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Drowsy driving is a major cause of road accidents. This paper analyses the drivers' behavior in the state of fatigue driving and introduces the latest developments of drowsy driving detection technology. In this study we also propose a drowsy driving detection based on the driver's physiological signals such as eye activity measures, the inclination of the driver's head, sagging posture, heart beat rate, skin electric potential, and electroencephalographic (EEG) activities, as well as response characteristics, decline in gripping force on the steering wheel and lane keeping characteristics. By developing a hierarchical model that is able to collect the sensing data, analyze the driving behavior and the reactions to the driver, it can provide a safe and a comfortable driving environment. Combining different indications of drowsiness and processing the contextual information to predict whether a driver is drowsy, the system not only issues a warning for the driver, but also provides the drowsy driving information to transportation control center and other vehicles if necessary. � 2013 IEEE. |
| author2 |
55726997800 |
| author_facet |
55726997800 Wei L. Mukhopadhyay S.C. Jidin R. Chen C.-P. |
| format |
Conference paper |
| author |
Wei L. Mukhopadhyay S.C. Jidin R. Chen C.-P. |
| author_sort |
Wei L. |
| title |
Multi-source information fusion for drowsy driving detection based on wireless sensor networks |
| title_short |
Multi-source information fusion for drowsy driving detection based on wireless sensor networks |
| title_full |
Multi-source information fusion for drowsy driving detection based on wireless sensor networks |
| title_fullStr |
Multi-source information fusion for drowsy driving detection based on wireless sensor networks |
| title_full_unstemmed |
Multi-source information fusion for drowsy driving detection based on wireless sensor networks |
| title_sort |
multi-source information fusion for drowsy driving detection based on wireless sensor networks |
| publishDate |
2023 |
| _version_ |
1806424397693583360 |
| score |
13.211869 |