Adaptive driver assistance system based on Traffic Information Saliency Map

In this paper, we propose a framework that can prevent accidents due to careless or inattentive driving by providing the necessary traffic information to the driver. The proposed system complements the driver by providing the missed cognitive information regarding the traffic. The proposed system is divided into three parts. First, the system checks the condition of the driver in real time, and detects the status of the driver in terms of driving ability. Second, we propose bottom-up and top-down processes based on Traffic Information Saliency Map (TISM) which contains the distribution corresponding to the external road information using bottom-up traffic information saliency map and top-down importance information such as pedestrian and traffic light detection results. Computer experimental results show that the proposed method works well for monitoring of internal situation for driver's attention as well as external environment.

[1]  Christof Koch,et al.  A Model of Saliency-Based Visual Attention for Rapid Scene Analysis , 2009 .

[3]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[4]  David Gerónimo Gómez,et al.  Survey of Pedestrian Detection for Advanced Driver Assistance Systems , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Miguel Ángel Sotelo,et al.  Real-time system for monitoring driver vigilance , 2004, Proceedings of the IEEE International Symposium on Industrial Electronics, 2005. ISIE 2005..

[6]  ZuWhan Kim,et al.  Robust Lane Detection and Tracking in Challenging Scenarios , 2008, IEEE Transactions on Intelligent Transportation Systems.

[7]  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.

[8]  Davis E. King,et al.  Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..

[9]  Andry Rakotonirainy,et al.  Affordable visual driver monitoring system for fatigue and monotony , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[10]  Minho Lee,et al.  In-attention State Monitoring for a Driver Based on Head Pose and Eye Blinking Detection Using One Class Support Vector Machine , 2014, ICONIP.

[11]  Mohan M. Trivedi,et al.  Head Pose Estimation for Driver Assistance Systems: A Robust Algorithm and Experimental Evaluation , 2007, 2007 IEEE Intelligent Transportation Systems Conference.

[12]  Jingyu Yang,et al.  Driver fatigue alarm based on eye detection and gaze estimation , 2007, International Symposium on Multispectral Image Processing and Pattern Recognition.

[13]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Shin Yamamoto,et al.  Measurement of Driver's Consciousness by Image Processing -A Method for Presuming Driver's Drowsiness by Eye-Blinks coping with Individual Differences - , 2006, 2006 IEEE International Conference on Systems, Man and Cybernetics.

[15]  Mohan M. Trivedi,et al.  On the Roles of Eye Gaze and Head Dynamics in Predicting Driver's Intent to Change Lanes , 2009, IEEE Transactions on Intelligent Transportation Systems.

[16]  Minho Lee,et al.  Visual Selective Attention Model Considering Bottom-Up Saliency and Psychological Distance , 2010, ICONIP.

[17]  Wolfgang Birk,et al.  A driver-distraction-based lane-keeping assistance system , 2007 .

[18]  Andreas Ernst,et al.  Face detection with the modified census transform , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[19]  Lionel Prevost,et al.  A Cascade of Boosted Generative and Discriminative Classifiers for Vehicle Detection , 2008, EURASIP J. Adv. Signal Process..