• DocumentCode
    124465
  • Title

    Sober-Drive: A smartphone-assisted drowsy driving detection system

  • Author

    Lunbo Xu ; Shunyang Li ; Kaigui Bian ; Tong Zhao ; Wei Yan

  • Author_Institution
    Sch. of EECS, Peking Univ., Beijing, China
  • fYear
    2014
  • fDate
    3-6 Feb. 2014
  • Firstpage
    398
  • Lastpage
    402
  • Abstract
    Drowsy driving, a combination of sleepiness and driving, has become a worldwide problem that often leads to tragic accidents and outcomes. Existing research findings have shown that the percentage of closure of eyelid (a.k.a PERCLOS) is an effective indicator to evaluate the driver´s drowsiness. We present the Sober-Drive system, which leverages PERCLOS for on-vehicle drowsy driving detection using smart phones. Specifically, Sober-Drive is built upon a number of indicators that are discrete-approximated from PERCLOS, blink time and blink rate, and it exploits the Neural Network to classify the eye “open/close” states. We developed a Sober-Drive prototype on Android smart phones, and we conducted extensive real-world experiments to evaluate its performance, the results of which show that Sober-Drive has a high detection rate of more than 90% for drowsy driving behaviors.
  • Keywords
    face recognition; gaze tracking; monitoring; neural nets; sensors; sleep; smart phones; Android smart phones; PERCLOS; accidents; blink rate; blink time; drowsy driving detection system; neural network; percentage of closure of eyelid; sleepiness; smartphone; sober drive system; Accuracy; Algorithm design and analysis; Fatigue; Neural networks; Smart phones; Training; Vehicles; drowsiness detection; eye detection; neural network; personalized training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing, Networking and Communications (ICNC), 2014 International Conference on
  • Conference_Location
    Honolulu, HI
  • Type

    conf

  • DOI
    10.1109/ICCNC.2014.6785367
  • Filename
    6785367