Multi-source information fusion for drowsy driving detection based on wireless sensor networks

Liang Wei, S. C. Mukhopadhyay, Razali Jidin, Chia Pang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 7th International Conference on Sensing Technology, ICST 2013
Pages850-857
Number of pages8
DOIs
Publication statusPublished - 01 Dec 2013
Event2013 7th International Conference on Sensing Technology, ICST 2013 - Wellington, New Zealand
Duration: 03 Dec 201305 Dec 2013

Publication series

NameProceedings of the International Conference on Sensing Technology, ICST
ISSN (Print)2156-8065
ISSN (Electronic)2156-8073

Other

Other2013 7th International Conference on Sensing Technology, ICST 2013
CountryNew Zealand
CityWellington
Period03/12/1305/12/13

Fingerprint

Information fusion
Highway accidents
Wireless sensor networks
Skin
Wheels
Fatigue of materials
Electric potential
Processing

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Wei, L., Mukhopadhyay, S. C., Jidin, R., & Chen, C. P. (2013). Multi-source information fusion for drowsy driving detection based on wireless sensor networks. In 2013 7th International Conference on Sensing Technology, ICST 2013 (pp. 850-857). [6727771] (Proceedings of the International Conference on Sensing Technology, ICST). https://doi.org/10.1109/ICSensT.2013.6727771
Wei, Liang ; Mukhopadhyay, S. C. ; Jidin, Razali ; Chen, Chia Pang. / Multi-source information fusion for drowsy driving detection based on wireless sensor networks. 2013 7th International Conference on Sensing Technology, ICST 2013. 2013. pp. 850-857 (Proceedings of the International Conference on Sensing Technology, ICST).
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Wei, L, Mukhopadhyay, SC, Jidin, R & Chen, CP 2013, Multi-source information fusion for drowsy driving detection based on wireless sensor networks. in 2013 7th International Conference on Sensing Technology, ICST 2013., 6727771, Proceedings of the International Conference on Sensing Technology, ICST, pp. 850-857, 2013 7th International Conference on Sensing Technology, ICST 2013, Wellington, New Zealand, 03/12/13. https://doi.org/10.1109/ICSensT.2013.6727771

Multi-source information fusion for drowsy driving detection based on wireless sensor networks. / Wei, Liang; Mukhopadhyay, S. C.; Jidin, Razali; Chen, Chia Pang.

2013 7th International Conference on Sensing Technology, ICST 2013. 2013. p. 850-857 6727771 (Proceedings of the International Conference on Sensing Technology, ICST).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wei L, Mukhopadhyay SC, Jidin R, Chen CP. Multi-source information fusion for drowsy driving detection based on wireless sensor networks. In 2013 7th International Conference on Sensing Technology, ICST 2013. 2013. p. 850-857. 6727771. (Proceedings of the International Conference on Sensing Technology, ICST). https://doi.org/10.1109/ICSensT.2013.6727771