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
Link To Document