Sleepiness Detection for Cooperative Vehicle Navigation Strategies

Human-robot interaction (HRI) techniques has become important in the resolution of problems that can not be automated completely. Moreover, the design of strategies based on such interactions can help to compensate the agents respective limitations. This work aims the development of a HRI system, which is based on driver sleepiness to improve safety in a Renault Twizy vehicle. For this purpose, eye behavior measurements are extracted using a properly machine learning algorithm that can operate under varying illumination levels. Perclos and blink frequency measures are considered to estimate diver sleepiness, and based in such parameters, the vehicle speed is limited, which may help decrease accidents rate. The presented architecture can improve the security conditions of navigation for both users and pedestrians. The results show that it is possible to implement HRI strategies based on cognitive factors in urban areas to prevent fatal accidents.

[1]  Q. Ruan,et al.  Facial expression recognition based on the daul-tree complex wavelet transform and supervised spectral analysis , 2010, IEEE 10th INTERNATIONAL CONFERENCE ON SIGNAL PROCESSING PROCEEDINGS.

[2]  Muhammad Abdullah,et al.  Navigation in human-robot and robot-robot interaction using optimization methods , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[3]  V. Štruc.  The Matlab Toolbox for Illumination Invariant Face Recognition , 2011 .

[4]  J. Horne,et al.  Evaluation "in-car" countermeasures to sleepiness: cold air and radio. , 1998, Sleep.

[5]  Michael A. Goodrich,et al.  Human-Robot Interaction: A Survey , 2008, Found. Trends Hum. Comput. Interact..

[6]  Yuan Yan Tang,et al.  Basic Processes of Chinese Character Based on Cubic B-Spline Wavelet Transform , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

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

[8]  Kristin E. Schaefer,et al.  Relinquishing Manual Control: Collaboration Requires the Capability to Understand Robot Intent , 2016, 2016 International Conference on Collaboration Technologies and Systems (CTS).

[9]  Victor-Emil Neagoe,et al.  Face Recognition using PCA versus ICA versus LDA cascaded with the neural classifier of Concurrent Self-Organizing Maps , 2010, 2010 8th International Conference on Communications.

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

[11]  Bin Yang,et al.  Camera-based drowsiness reference for driver state classification under real driving conditions , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[12]  Sadari Jayawardena,et al.  Efficient PERCLOS and Gaze Measurement Methodologies to Estimate Driver Attention in Real Time , 2014, 2014 5th International Conference on Intelligent Systems, Modelling and Simulation.

[13]  Simone Benedetto,et al.  Driver workload and eye blink duration , 2011 .

[14]  R J Fairbanks,et al.  RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING; DEVELOPMENT, VALIDATION, AND REFINEMENT OF ALGORITHMS FOR DETECTION OF DRIVER DROWSINESS. FINAL REPORT , 1994 .

[15]  Agus Zainal Arifin,et al.  Image segmentation by histogram thresholding using hierarchical cluster analysis , 2006, Pattern Recognit. Lett..

[16]  Ronald C. Arkin,et al.  Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[17]  Linda Steg,et al.  Driving with music: Effects on arousal and performance , 2013 .

[18]  Yuan Yan Tang,et al.  Multiscale facial structure representation for face recognition under varying illumination , 2009, Pattern Recognit..

[19]  A. Williamson,et al.  Moderate sleep deprivation produces impairments in cognitive and motor performance equivalent to legally prescribed levels of alcohol intoxication , 2000, Occupational and environmental medicine.

[20]  Kitae Kim,et al.  Fusion of driver-information based driver status recognition for co-pilot system , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[21]  Yi-Jen Su,et al.  Face Recognition Based on Dual-Tree Complex Wavelet Transform under Low Illumination Environments , 2014, IVCNZ '14.

[22]  Thomas B. Sheridan,et al.  Adaptive Automation, Level of Automation, Allocation Authority, Supervisory Control, and Adaptive Control: Distinctions and Modes of Adaptation , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[23]  Haifeng Hu,et al.  Multiscale illumination normalization for face recognition using dual-tree complex wavelet transform in logarithm domain , 2011, Comput. Vis. Image Underst..

[24]  Ming-Hui Du,et al.  Face Detection using DT-CWT on Spectral Histogram , 2006, 2006 International Conference on Machine Learning and Cybernetics.

[25]  David Windridge,et al.  Artificial Co-Drivers as a Universal Enabling Technology for Future Intelligent Vehicles and Transportation Systems , 2015, IEEE Transactions on Intelligent Transportation Systems.

[26]  Qiuqi Ruan,et al.  Facial expression recognition based on complex wavelet transform , 2010 .

[27]  Evangeline Pollard,et al.  An ontology-based model to determine the automation level of an automated vehicle for co-driving , 2013, Proceedings of the 16th International Conference on Information Fusion.

[28]  M. Golz,et al.  Evaluation of PERCLOS based current fatigue monitoring technologies , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[29]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[30]  Richard Baraniuk,et al.  The Dual-tree Complex Wavelet Transform , 2007 .

[31]  Ping Zhang.  Complex wavelet feature extraction for video-based face recognition , 2010, Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon).