Detection of Distracted Car Drivers using Convolutional Neural Network
Sangeetha1, R.Ambrish2, M.Madhav3, J.Karthick4
1Ms. Sangeetha, Assistant Professor, Srm Institute Of Science And Technology, Ramapuram Campus, Chennai, Tamilnadu, India.
2R.Ambrish, Btech-Information Technology At Srm Institute Of Science And Technology, Ramapuram Campus, Chennai, Tamilnadu, India.
3M.Madhav, Btech-Information Technology At Srm Institute Of Science And Technology, Ramapuram Campus, Chennai, Tamilnadu, India.
4J.Karthick, Btech-Information Technology At Srm Institute Of Science And Technology, Ramapuram Campus, Chennai, Tamilnadu, India.
Manuscript received on March 15, 2020. | Revised Manuscript received on March 24, 2020. | Manuscript published on March 30, 2020. | PP: 4175-4176 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9443038620/2020©BEIESP | DOI: 10.35940/ijrte.F9443.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: According to survey report on the internet, the road accident cases are increasing exponentially. Most of the accident cases are due to distraction of drivers, over speed, panic attacks of drivers. We are proposing a system that will take control of the vehicle system if the driver is distracted. The existing system uses a sensor based indication or Recurrent Neural Network (RNN) installed semi self driving cars. The proposed system uses Convolutional Neural Networks to understand the behaviour of the driver and the environment. Naturalistic data collection of ten drivers is being collected and are treated as a qualifying dataset.
Keywords: Distracted Drivers, Prediction, Performance
Scope of the Article: Networked-Driven Multicourse Chips.