Analysis of Driver Point of Regard Determinations with Eye-Gesture Templates Using Receiver Operating Characteristic

Authors: Siti Nor Hafizah binti Mohd Zaid, Mohamed Abdel-Maguid, Abdel-Hamid Soliman

Abstract:

An Advance Driver Assistance System (ADAS) is a computer system on board a vehicle which is used to reduce the risk of vehicular accidents by monitoring factors relating to the driver, vehicle and environment and taking some action when a risk is identified. Much work has been done on assessing vehicle and environmental state but there is still comparatively little published work that tackles the problem of driver state. Visual attention is one such driver state. In fact, some researchers claim that lack of attention is the main cause of accidents as factors such as fatigue, alcohol or drug use, distraction and speeding all impair the driver-s capacity to pay attention to the vehicle and road conditions [1]. This seems to imply that the main cause of accidents is inappropriate driver behaviour in cases where the driver is not giving full attention while driving. The work presented in this paper proposes an ADAS system which uses an image based template matching algorithm to detect if a driver is failing to observe particular windscreen cells. This is achieved by dividing the windscreen into 24 uniform cells (4 rows of 6 columns) and matching video images of the driver-s left eye with eye-gesture templates drawn from images of the driver looking at the centre of each windscreen cell. The main contribution of this paper is to assess the accuracy of this approach using Receiver Operating Characteristic analysis. The results of our evaluation give a sensitivity value of 84.3% and a specificity value of 85.0% for the eye-gesture template approach indicating that it may be useful for driver point of regard determinations.

Keywords: Advanced Driver Assistance Systems, Eye-Tracking, Hazard Detection.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1078863

References:

[1] Fletcher,L., and Zelinsky, A., ÔÇÿDriver Inattention Detection Based on Eye Gazed - Road Even Correlation-, The International Journal of Robotics Research, pp.774-801, June 2009.
[2] WHO, ÔÇÿWorld Health Report, Technical Report-, World Health Organisation, http://www.who.int/whr2001/2001/main/en/index.html, 2001.
[3] OECD/ECMT, ÔÇÿAmbitious Road Safety Targets and the Safe System Approach-, OECD Publishing, 2006.
[4] Department for Transport Statistics, Retrieved September 20, 2012, from theDepartment for Transport: http://www.dft.gov.uk/statistics?orderby=date&post_type =table&series=road-accidents-and-safety-series), 2010.
[5] MohdZaid, S.N.H., Mohamed, A.M., Soliman, A.H., ÔÇÿTowards EyeGesture Analysis for Driver Assistance Systems using Template Matching, Proceeding of 4th Engineering Conference (EnCon), Kuching, Sarawak,Malaysia, 2011.
[6] MohdZaid, S., N., H., Mohamed, A.M., and Soliman, A.H., ÔÇÿEyeGesture Analysis for Driver Hazard Awareness-, World Academy of Science,Engineering and Technology (WASET), Tokyo, Japan, Vol.65,pp.1240-1246, 2012.
[7] MohdZaid , S., N., H., Mohamed, A.M., Soliman, A., H., ÔÇÿEye Gesture Analysis with Head Movement for Advanced Driver Assistance Systems-, World Academy of Science, Engineering and Technology (WASET), France, Vol 66, pp. 1082- 1088, 2012.
[8] Dingus, T.A., Klauer, S.G., Neale, V.L., Petersen, A., Lee, S.E.,Sudweeks, J., ÔÇÿThe 100-car Naturalistic Driving Study, Phase IIresults of The 100 Car-Field Experiment (Technical Report No DOT HS 810593)--, Washington, DC-NHTSA, 2006.
[9] Klauer, S.G., Dingus, T.A., Neale, V.L., Sudweeks, J., and Ramsey, D.,ÔÇÿThe impact of Driver Inattention on Near-Crash/Crash Risk : AnAnalysis Using the 100-car Naturalistic Driving Study Data (Technical Report No. DOT HS 810 594)-, Washington DC: NHTSA, 2006.
[10] Fletcher, L., Loy, G., Barnes, N., and Zelinsky, A., ÔÇÿCorrelating Driver Gaze with Road Scene for Driver Assistance Systems-, Journal of Robotics and Autonomous Systems, pp.71-84. 2005a.
[11] Fletcher, L., Petersson, L., Barnes, N., Austin, D., and Zelinsky, A., ÔÇÿA Sign Reading Driver Assistance System Using Eye Gaze-, Proceedingsof the IEEE International Conference on Robotics and Automation, pp.4655- 4660.
[12] Gee, A.H. and Cipolla, R., ÔÇÿDetermining the Gaze of Faces in Images-, Image and Vision Computing, Vol.12, No.10, pp. 639-647, 1994.
[13] Cheng,S.Y., and Trivedi, M.M., ÔÇÿTowards a Comparative Study of Lane Tracking Using Omni-directional and Rectilinear Images for Driver Assistance Systems-, ICRA, Workshop: Planning, Perception and Navigation for Intelligent Vehicles, 2007.
[14] McCall, J.C. Wipf, D.P.Trivedi, M.M.Rao, B.D., ÔÇÿLane Change IntentAnalysis Using Robust Operators and Sparse Bayesian Learning-, IEEE Transaction on Intelligent Transportation Systems, Vol.8, No.3, pp.431-440, 2007.
[15] Doshi, A., and Trivedi, M., ÔÇÿOn the Roles of Eye Gaze and Head Dynamics in Predicting Driver-s Intent to Change Lanes-, IEEE Intelligent Transportation Systems, Vol.3, No.10, pp.453-462, 2009.
[16] Youden W., J., ÔÇÿAn Index for Rating Diagnostic Tests-. Cancer vol.3 pp.32-35, 1950.