Real-Time V2V Communication With a Machine Learning-Based System for Detecting Drowsiness of Drivers

Ahmed Y. Awad and Seshadri Mohan
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Ahmed Y. Awad: University of Arkansas at Little Rock, USA
Seshadri Mohan: University of Arkansas at Little Rock, USA

International Journal of Interdisciplinary Telecommunications and Networking (IJITN), 2021, vol. 13, issue 4, 35-50

Abstract: This article applies machine learning to detect whether a driver is drowsy and alert the driver. The drowsiness of a driver can lead to accidents resulting in severe physical injuries, including deaths, and significant economic losses. Driver fatigue resulting from sleep deprivation causes major accidents on today's roads. In 2010, nearly 24 million vehicles were involved in traffic accidents in the U.S., which resulted in more than 33,000 deaths and over 3.9 million injuries, according to the U.S. NHTSA. A significant percentage of traffic accidents can be attributed to drowsy driving. It is therefore imperative that an efficient technique is designed and implemented to detect drowsiness as soon as the driver feels drowsy and to alert and wake up the driver and thereby preventing accidents. The authors apply machine learning to detect eye closures along with yawning of a driver to optimize the system. This paper also implements DSRC to connect vehicles and create an ad hoc vehicular network on the road. When the system detects that a driver is drowsy, drivers of other nearby vehicles are alerted.

Date: 2021
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