An Efficient Approach to Detect Driver Distraction during Mobile Phone Usage

  • Authors

    • Medha Sawhney
    • Vasundhara Acharya
    • Krishna Prakasha
    2018-12-19
    https://doi.org/10.14419/ijet.v7i4.41.24307
  • Convolutional Neural Network, Distracted Driver detection, Mobile phone usage of driver, Image classification, Object detection.

  • A distracted driver is an invitation to a fatal vehicle accident. People lose their lives every day due to distracted driving and using mobile phones while driving is one of the primary reasons behind road accidents. Hence, detection of mobile phone usage to alert the driver or for an autonomous system to take over becomes extremely important. In an attempt to solve this issue of distracted driving, the authors proposed a Convolution Neural Network (CNN)  based model to detect mobile phone usage by the driver. The proposed work presents not only a practical solution to the problem but also a comparison between traditional approaches (Support Vector Machine with HOG) and a CNN based model. The traditional methods are both implemented and tested by the authors. The presented model performs input segmentation to achieve an efficient accuracy of 97%. Deep learning was found to be the best solution to detect driver distraction while on a call accurately

     

     

     

  • References

    1. [1] M. A. Regan, J. D. Lee, and K. L. Young, Driver distraction: Theory, effects, and mitigation. Boca Raton, FL, USA: CRC Press, 2008.

      [2] M. Peissner, V. Doebler, and F. Metze, “Can voice interaction help reducing the level of distraction and prevent accidents?†2011

      [3] 2016. Research Note: Distracted Driving 2014. (Apr 2016). https://crashstats.nhtsa.dot.gov/api/public/viewpublication/812260

      [4] D. L. Strayer, F. A. Drews, and D. J. Crouch. A Comparison of the Cell Phone Driver and the Drunk Driver. Human factors: The Journal of the Human Factors and Ergonomics Society, 48(2):381–391, 2006

      [5] Gregory M Fitch, Susan A Soccolich, Feng Guo, Julie McClafferty, Youjia Fang, and others. 2013. The impact of hand-held and hands-free cell phone use on driving performance and safety-critical event risk. Technical Report.

      [6] Colbran, Samuel, Kaiqi Cen, and Danni Luo. "Classification of Driver Distraction."

      [7] BVLC. Solver : http://caffe.berkeleyvision.org/tutorial/solver.html

      [8] Streiffer, C.; Raghavendra, R.; Benson, T.; Srivatsa, M. DarNet: A Deep Learning Solution for Distracted Driving Detection. In Proceedings of the Middleware Industry 2017, Industrial Track of the 18th International Middleware Conference, Las. Vegas, NV, USA, 11–15 December 2017.

      [9] Hssayeni, Murtadha D; Saxena, Sagar; Ptucha, Raymond; Savakis, Andreas, “Distracted Driver Detection: Deep Learning vs Handcrafted Featuresâ€, Society for Imaging Science and Technology, Imaging and Multimedia Analytics in a Web and Mobile World 2017, pp. 20-26(7)

      [10] R. Berri, F. Osório, R. Parpinelli and A. Silva, "A hybrid vision system for detecting use of mobile phones while driving," 2016 International Joint Conference on Neural Networks (IJCNN), Vancouver, BC, 2016, pp. 4601-4610.

      [11] H. Yasar, "Detection of Driver's mobile phone usage," 2017IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), Manila, 2017, pp. 1-4.

      [12] K. Seshadri, F. Juefei-Xu, D. K. Pal, and M. Savvides. Driver Cell Phone Usage Detection on Strategic Highway Research Program (SHRP2) Face View Videos. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2015 IEEE Conference on, June 2015.

      [13] R. A. Berri, A. G. Silva, R. S. Parpinelli, E. Girardi, and R. Arthur, “A pattern recognition system for detecting use of mobile phones while driving,†arXiv preprint arXiv:1408.0680, 2014.

      [14] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016, http://www.deeplearningbook.org.

      [15] Supervised Convolution Neural Network : http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork/. Accessed on : 2018-08-27.

      [16] Convolution Neural Networks: http://cs231n.github.io/convolutional-networks/#overview Accessed on : 2018-08-27.

      [17] Classification based on Deep Convolutional Neural Networks: http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Accessed on : 2018-08-27.

      [18] Core Layers on CNN in Keras: https://keras.io/layers/core/ Accessed on : 2018-08-27.

      [19] Dalal, Navneet, and Bill Triggs. "Histograms of oriented gradients for human detection." Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005.

      [20] True Positive and True Negative: https://developers.google.com/machine-learning/crash-course/classification/true-false-positive-negative Accessed on : 2018-08-27.

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  • How to Cite

    Sawhney, M., Acharya, V., & Prakasha, K. (2018). An Efficient Approach to Detect Driver Distraction during Mobile Phone Usage. International Journal of Engineering & Technology, 7(4.41), 86-90. https://doi.org/10.14419/ijet.v7i4.41.24307