Automatic Detection of Driver Impairment Based on Pupillary Light Reflex

The main objective of this paper is to determine the feasibility of designing a driver drunkenness detection system based on the dynamic analysis of a subject’s pupillary light reflex (PLR). This involuntary reaction is widely utilized in the medical field to diagnose a variety of diseases, and in this paper, the effectiveness of such a method to reveal an impairment condition due to alcohol abuse is evaluated. The test method consists in applying a light stimulus to one eye of the subject and to capture the dynamics of constriction of both eyes; for extracting the pupil size profiles from the video sequences, a two-step methodology is described, where in the first phase, the iris/pupil search within the image is performed, and in the second stage, the image is cropped to perform pupil detection on a smaller image to improve time efficiency. The undesired pupil dynamics arising in the PLR are defined and evaluated; a spontaneous oscillation of the pupil diameter is observed in the range [0, 2] Hz and the accommodation reflex causes pupil constriction of about 10% of the iris diameter. A database of pupillary light responses is acquired on different subjects in baseline condition and after alcohol consumption, and for each one, a first-order model is identified. A set of features is introduced to compare the two populations of responses and is used to design a support vector machine classifier to discriminate between “Sober” and “Drunk” states.

[1]  Robert Zobel,et al.  Don't Sleep and Drive - VW's Fatigue Detection Technology , 2005 .

[2]  Matteo Corno,et al.  A Robust Steering Assistance System for Road Departure Avoidance , 2012, IEEE Transactions on Vehicular Technology.

[3]  Ye-Hoon Kim,et al.  Real-Time Driver Drowsiness Detection for Embedded System Using Model Compression of Deep Neural Networks , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

[4]  Mohammad Mikaeili,et al.  Driving Drowsiness Detection Using Fusion of Electroencephalography, Electrooculography, and Driving Quality Signals , 2016, Journal of medical signals and sensors.

[5]  G D Roach,et al.  Comparing the effects of fatigue and alcohol consumption on locomotive engineers' performance in a rail simulator. , 2001, Journal of human ergology.

[6]  Saumya Batham,et al.  Approach to accurate circle detection: Circular Hough Transform and Local Maxima concept , 2014, 2014 International Conference on Electronics and Communication Systems (ICECS).

[7]  Bibhas Chandra Dhara,et al.  A fast iris localization using inversion transform and restricted circular Hough transform , 2015, International Conference on Advances in Pattern Recognition.

[8]  Péter Gáspár,et al.  Adaptive Cruise Control in Longitudinal Dynamics , 2017 .

[9]  Hu Hongyu,et al.  Driver Drowsiness Detection Based on Time Series Analysis of Steering Wheel Angular Velocity , 2017, 2017 9th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA).

[10]  Aurobinda Routray,et al.  Measurement of PERCLOS using eigen-eyes , 2012, 2012 4th International Conference on Intelligent Human Computer Interaction (IHCI).

[11]  K. Messaoudi,et al.  Modified circular Hough transform using FPGA , 2012, 2012 24th International Conference on Microelectronics (ICM).

[12]  Richard D. Blomberg,et al.  CRASH RISK OF ALCOHOL IMPAIRED DRIVING , 2002 .

[13]  Robert P. Chilcott,et al.  Eyeing up the Future of the Pupillary Light Reflex in Neurodiagnostics , 2018, Diagnostics.

[14]  Mohsen Babaeian,et al.  Real time driver drowsiness detection using a logistic-regression-based machine learning algorithm , 2016, 2016 IEEE Green Energy and Systems Conference (IGSEC).

[15]  Zhitao Xiao,et al.  Driver Fatigue Detection Based on Eye State Recognition , 2017, 2017 International Conference on Machine Vision and Information Technology (CMVIT).

[16]  Adam Ziebinski,et al.  A Survey of ADAS Technologies for the Future Perspective of Sensor Fusion , 2016, ICCCI.

[17]  Radu Gabriel Bozomitu,et al.  Pupil centre coordinates detection using the circular Hough transform technique , 2015, 2015 38th International Spring Seminar on Electronics Technology (ISSE).

[18]  Fabiola M. Villalobos-Castaldi,et al.  A new spontaneous pupillary oscillation-based verification system , 2013, Expert Syst. Appl..

[19]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .

[20]  Manuel Menezes de Oliveira Neto,et al.  Photorealistic models for pupil light reflex and iridal pattern deformation , 2009, TOGS.

[21]  Yo-Ping Huang,et al.  Early detection of driver drowsiness by WPT and FLFNN models , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[22]  Luca Mainardi,et al.  Characterization of affective states by pupillary dynamics and autonomic correlates , 2013, Front. Neuroeng..

[23]  J. Horne,et al.  Sleep related vehicle accidents , 1995, BMJ.

[24]  Lynn E. Baker THE PUPILLARY RESPONSE CONDITIONED TO SUBLIMINAL AUDITORY STIMULI , 1940 .

[25]  N. A. Jalil,et al.  Iris localization using colour segmentation and circular Hough transform , 2012, 2012 IEEE-EMBS Conference on Biomedical Engineering and Sciences.

[26]  Miyoung Kim,et al.  Driver drowsiness detection using the in-ear EEG , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[27]  Wataru Ohyama,et al.  Detection of eyes by circular Hough transform and histogram of gradient , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[28]  George Yannis,et al.  Reaction times of young alcohol-impaired drivers. , 2013, Accident; analysis and prevention.

[29]  Chuan-Kai Yang,et al.  Real-time eye state detection system using haar cascade classifier and circular hough transform , 2016, 2016 IEEE 5th Global Conference on Consumer Electronics.

[30]  J. Horne,et al.  Fatigue, alcohol, and serious road crashes in France: factorial study of national data , 2001, BMJ : British Medical Journal.

[31]  Dian Artanto,et al.  Drowsiness detection system based on eye-closure using a low-cost EMG and ESP8266 , 2017, 2017 2nd International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE).

[32]  Qing Liu,et al.  Driver drowsiness detection using facial dynamic fusion information and a DBN , 2018 .

[33]  Md. Ashraf Shubana khan,et al.  Towards Detection of Bus Driver Fatigue based on Robust Visual Analysis of Eye State , 2019, JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES.

[34]  Mohammad Mikaili,et al.  EEG-based Drowsiness Detection for Safe Driving Using Chaotic Features and Statistical Tests , 2011, Journal of medical signals and sensors.

[35]  Chokri Abdelmoula,et al.  A Fuzzy Based Method for Driver Drowsiness Detection , 2017, 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA).

[36]  J. Horne,et al.  Vehicle accidents related to sleep: a review. , 1999, Occupational and environmental medicine.

[37]  Giulio Panzani,et al.  Design of a lane change driver assistance system, with implementation and testing on motorbike , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[38]  M Clynes,et al.  COLOR DYNAMICS OF THE PUPIL , 1969, Annals of the New York Academy of Sciences.

[39]  Ajay Mittal,et al.  Head movement-based driver drowsiness detection: A review of state-of-art techniques , 2016, 2016 IEEE International Conference on Engineering and Technology (ICETECH).

[40]  Radu Gabriel Bozomitu,et al.  Implementation of eye-tracking system based on circular Hough transform algorithm , 2015, 2015 E-Health and Bioengineering Conference (EHB).