Open Access
MATEC Web Conf.
Volume 90, 2017
The 2nd International Conference on Automotive Innovation and Green Vehicle (AiGEV 2016)
Article Number 01034
Number of page(s) 10
Published online 20 December 2016
  1. US Department of Transportation, an Evaluation of Emerging Driver Fatigue Detection Measures and Technologies (2009). [Google Scholar]
  2. R. O. Phillips, F. Sagberg and T. Bjørnskau, Fatigue in operators of land- and seabased transport forms in Norway, Risk Profiles Fatigue in Transport Report IV”, Project Report, Institute of Transport Economics, Oslo, TØI (report 1440/2015). [Google Scholar]
  3. MIROS (Malaysian Institute of Road Safety Research), Five research pillars, (Accessed October 2016) [Google Scholar]
  4. A. Frank et al., The exploration of physical fatigue, sleep and depression in paramedics: a pilot study, Australasian Journal of Paramedicine, 9(1), pp. 1–33 (2012) [Google Scholar]
  5. M. H. Alsibai and S. Abdul Manap, A Study on Driver Fatigue Notification Systems, ARPN Journal of Engineering and Applied Sciences, 11 (18), pp. 10987–10992 (2016) [Google Scholar]
  6. L. W. Li et al., Multi-sensor soft-computing system for driver drowsiness detection, Proceedings of Online conference on soft computing in industrial applications, pp. 1–10 (2012) [Google Scholar]
  7. H. Singh, J.S. Bhatia and J. Kaur. Eye tracking based driver fatigue monitoring and warning system, International Conference on Power Electronics (IICPE). India. pp. 1-6 (2011) [Google Scholar]
  8. Mercedes Benz Tech. Center:, (Accessed October 2016) [Google Scholar]
  9. Royal society for the prevention of accidents, Driver fatigue and road accidents a literature review and position paper (2001) [Google Scholar]
  10. H. L. Man, G. P. Hyung, H. L. Seok, S. Y. Kang, S. L. Kil., An adaptive cruise control system for autonomous vehicles, International Journal of Precision Engineering and Manufacturing, 14 (3), pp 373-380 (2013) [CrossRef] [Google Scholar]
  11. S. L. Tzuu-Hseng and S. J. Chang, Autonomous Fuzzy Parking Control of a Car-Like Mobile Robot, IEEE transactions on systems, man, and cybernetics—part a: systems and humans, 3, pp. 451-465 (2003) [Google Scholar]
  12. N. E. Cavalcanti et al., Development Control Parking Access Using Techniques Digital Image Processing And Applied Computational Intelligence, IEEE Latin America Transactions, 13(1), pp. 272-276 (2015) [CrossRef] [Google Scholar]
  13. S.-Y. Juang, J.-G. Juang, Remote control of a mobile robot for indoor patrol. Appl. Sci. 6 (8), URL (2016) [Google Scholar]
  14. M. H. Alsibai, H. Manap, A. A. Abdullah, Enhanced face 323 recognition method performance on android vs windows platform. ARPN Journal of Engineering and Applied Sciences, 10 (23), pp. 17479–17485 (2015) [Google Scholar]
  15. P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 511-518 (2011) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.