A Smartphone-Based Drowsiness Detection and Warning System for Automotive Drivers

This paper presents a smartphone-based system for the detection of drowsiness in automotive drivers. The proposed framework uses three-stage drowsiness detection. The first stage uses the percentage of eyelid closure (PERCLOS) obtained through images captured by the front camera with a modified eye state classification method. The system uses near infrared lighting for illuminating the face of the driver during night-driving. The second step uses the voiced to the unvoiced ratio obtained from the speech data from the microphone, in the event PERCLOS crosses the threshold. A final verification stage is used as a touch response within a stipulated time to declare the driver as drowsy and subsequently sound an alarm. The device maintains a log file of the periodic events of the metrics along with the corresponding GPS coordinates. The system has three advantages over existing drowsiness detection systems. First, the three-stage verification process makes the system more reliable. The second advantage is its implementation on an Android smart-phone, which is readily available to most drivers or cab owners as compared to other general purpose embedded platforms. The third advantage is the use of SMS service to inform the control room as well as the passenger regarding the loss of attention of the driver. The framework provides 93.33% drowsiness state classification as compared to a single stage which gives 86.66%.

[1]  Klaus J. Kirchberg,et al.  Robust Face Detection Using the Hausdorff Distance , 2001, AVBPA.

[2]  Linda Ng Boyle,et al.  Visual Attention in Driving: The Effects of Cognitive Load and Visual Disruption , 2007, Hum. Factors.

[3]  Karel J. Zuiderveld,et al.  Contrast Limited Adaptive Histogram Equalization , 1994, Graphics Gems.

[4]  Liying Lang,et al.  The Study of Driver Fatigue Monitor Algorithm Combined PERCLOS and AECS , 2008, 2008 International Conference on Computer Science and Software Engineering.

[5]  Aurobinda Routray,et al.  A Portable Personality Recognizer Based on Affective State Classification Using Spectral Fusion of Features , 2018, IEEE Transactions on Affective Computing.

[6]  Zhiwei Zhu,et al.  Real-time nonintrusive monitoring and prediction of driver fatigue , 2004, IEEE Transactions on Vehicular Technology.

[7]  B. Carnahan,et al.  A drowsy driver detection system for heavy vehicles , 1998, 17th DASC. AIAA/IEEE/SAE. Digital Avionics Systems Conference. Proceedings (Cat. No.98CH36267).

[8]  Matthew Kay,et al.  Cognitive rhythms: unobtrusive and continuous sensing of alertness using a mobile phone , 2016, UbiComp.

[9]  Qiang Ji,et al.  A joint cascaded framework for simultaneous eye detection and eye state estimation , 2017, Pattern Recognit..

[10]  Zhijiang Wan,et al.  An Attention Level Monitoring and Alarming System for the Driver Fatigue in the Pervasive Environment , 2013, Brain and Health Informatics.

[11]  Rafael Cabeza,et al.  Hybrid method based on topography for robust detection of iris center and eye corners , 2013, TOMCCAP.

[12]  Xiaoyang Tan,et al.  Eyes closeness detection from still images with multi-scale histograms of principal oriented gradients , 2014, Pattern Recognit..

[13]  Aurobinda Routray,et al.  A novel drowsiness detection scheme based on speech analysis with validation using simultaneous EEG recordings , 2010, 2010 IEEE International Conference on Automation Science and Engineering.

[14]  D. Dinges.  An overview of sleepiness and accidents , 1995, Journal of sleep research.

[15]  S. M. Michaelson,et al.  Human exposure to nonionizing radiant energy—Potential hazards and safety standards , 1972 .

[16]  Bernhard Schölkopf,et al.  A tutorial on support vector regression , 2004, Stat. Comput..

[17]  Huabiao Qin,et al.  Drivers drowsiness detection in embedded system , 2007, 2007 IEEE International Conference on Vehicular Electronics and Safety.

[18]  Fanglin Chen,et al.  CarSafe app: alerting drowsy and distracted drivers using dual cameras on smartphones , 2013, MobiSys.

[19]  Aurobinda Routray,et al.  Analysis of training parameters for classifiers based on Haar-like features to detect human faces , 2011, 2011 International Conference on Image Information Processing.

[20]  D. Dinges,et al.  EVALUATION OF TECHNIQUES FOR OCULAR MEASUREMENT AS AN INDEX OF FATIGUE AND THE BASIS FOR ALERTNESS MANAGEMENT , 1998 .

[21]  Aurobinda Routray,et al.  A Vision-Based System for Monitoring the Loss of Attention in Automotive Drivers , 2013, IEEE Transactions on Intelligent Transportation Systems.

[22]  Wenhui Dong,et al.  Eye state classification based on multi-feature fusion , 2009, 2009 Chinese Control and Decision Conference.

[23]  Xie Bin,et al.  A PERCLOS-Based Driver Fatigue Recognition Application for Smart Vehicle Space , 2010, 2010 Third International Symposium on Information Processing.