Abstract

Falling asleep while driving is a major part of road accidents. Traffic accidents can be considered as a public health problem and several factors like drugs, driving without rest, sleep disorders, alcohol consumption affect sleep deprivation. Furthermore, drivers are also unaware of falling asleep situations, such as highway hypnosis. All these factors cause accidents while driving and are often fatal. A good background should be provided for drivers to implement effective driver warning systems and other countermeasures just before the accident. In this study, Long Short-Term Memory (LSTM) deep learning based driver warning system has been proposed to prevent road accidents. The Electrocardiogram (ECG) signals of the drivers are processed instantaneously to check whether they go into sleep or not. Experimental studies have been carried out on two different human data sets as sleep mode and awake mode. The simulation results confirm the effectiveness of the proposed method and show its superiority over other state-of-the art methods.

Highlights

  • In the 21st century, the drowsy driving is known as the one of most important cause of accidents

  • There are different drawbacks of driving a car more than 8 or 9 hours, such as serious hearty issues begin due to fatigue and distraction which affects the driving performance and raises the accident risk

  • Research shows that significant deterioration in driving performance initiated as a

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Summary

Introduction

In the 21st century, the drowsy driving is known as the one of most important cause of accidents. It is ignored and underestimated, it is a dangerous factor contributing to accidents. Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) hükümlerine göre açık erişimli bir makaledir. Result of on average more than 16 hours of sleep deprivation. This situation poses serious dangers in terms of traffic safety

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