Autonomous Distracted Driver Detection using Machine learining Classifiers

Authors

  • Prof. Praveen Hore Department of Computer Engineering, Army Institute of Technology, Pune, India
  • Prashant Tiwari Department of Computer Engineering, Army Institute of Technology, Pune, India
  • Ashwani Tiwari Department of Computer Engineering, Army Institute of Technology, Pune, India
  • Pawan Chauhan Department of Computer Engineering, Army Institute of Technology, Pune, India
  • Ravish Sharma Department of Computer Engineering, Army Institute of Technology, Pune, India

Keywords:

Machine Learning, Deep Learning , Convolutional Neural Network ,Classification , Hyperparameters

Abstract

In most of the cases of accidents the prime cause of the accident is the distracted state of driver With the
increase of In Vehicle Information System such cases are increasing. This problem can be solved by monitoring and
predicting the state of driver while driving and installing the autonomous prevention system to take preventive actions. In
order to realize this strategy we have used Convolutional Neural Networks and deep learning concepts in order to classify
the image of driver into 10 different classes that are which include texting,talking to passenger,talking on the
phone,looking left or right,searching something at the back seat,hair and make up,operating radio and drinking. Once the
class of image is predicted the autonomous preventive system can be invoked to take the preventive steps accordingly

Published

2017-04-25

How to Cite

Prof. Praveen Hore, Prashant Tiwari, Ashwani Tiwari, Pawan Chauhan, & Ravish Sharma. (2017). Autonomous Distracted Driver Detection using Machine learining Classifiers. International Journal of Advance Engineering and Research Development (IJAERD), 4(4), 294–300. Retrieved from https://ijaerd.com/index.php/IJAERD/article/view/2545