Early Distracted Driving Detection from Smartphone Audio Signals
R Angeline1, Anshul Bhargava2, Santhosh P3, L Alikhan4, Ashar SK5
1R. Angeline, Department of Computer Science and Engineering, SRM Institiute of Science and Technology, Chennai, India.
2Anshul Bhargava, Department of Computer Science and Engineering, SRM Institiute of Science and Technology, Chennai, India.
3P. Santhosh, Department of Computer Science and Engineering, SRM Institiute of Science and Technology, Chennai, India.
4L. Alikhan, Department of Computer Science and Engineering, SRM Institiute of Science and Technology, Chennai, India.
5Ashar SK, Department of Computer Science and Engineering, SRM Institiute of Science and Technology, Chennai, India.
Manuscript received on 10 April 2019 | Revised Manuscript received on 15 May 2019 | Manuscript published on 30 May 2019 | PP: 269-272 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3089058119/19©BEIESP
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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: In most of the road accidents cases registered, the prime cause always revolves around distracted driving. Distracted driving comprises of four types of driving events: Fetching Forward, Eating or Drinking, Turning Back, Picking Drops. This problem can be resolved by predicting the driver’s behavior and take preventive actions beforehand. To make this into reality, we have used concepts of deep learning. It use the recorded audio signals from the driver’s cellular devices, these signals are created during any of the above driving event occurs. It convert them into a frequency-time profile and then pass them to a classifier, which classifies them into four mentioned driving eventsbased on that preventive measure could be taken.
Index Terms: Distraction, Driver State, in-Vehicle Signal, Unsupervised Learning, and Supervised Learning.
Scope of the Article: E-Learning