scholarly journals Driver Drowsiness Detection and Alert System

2021 ◽  
pp. 583-588
Author(s):  
Swapnil Titare ◽  
Shubham Chinchghare ◽  
K. N. Hande
Keyword(s):  
Traffic Accidents ◽  
Learning Algorithm ◽  
Text Message ◽  
Secondary Data ◽  
Driving Safety ◽  
Drowsiness Detection ◽  
Detection Systems ◽  
Driver Drowsiness ◽  
Human Image ◽  
Day By Day

Nowadays, accidents occur during drowsy road trips and increase day by day; It is a known fact that many accidents occur due to driver fatigue and sometimes inattention, this research is primarily devoted to maximizing efforts to identify drowsiness. State of the driver under real driving conditions. The aim of driver drowsiness detection systems is to try to reduce these traffic accidents. The secondary data collected focuses on previous research on systems for detecting drowsiness and several methods have been used to detect drowsiness or inattentive driving.Our goal is to provide an interface where the program can automatically detect the driver's drowsiness and detect it in the event of an accident by using the image of a person captured by the webcam and examining how this information can be used to improve driving safety can be used. . a vehicle safety project that helps prevent accidents caused by the driver's sleep. Basically, you're collecting a human image from the webcam and exploring how that information could be used to improve driving safety. Collect images from the live webcam stream and apply machine learning algorithm to the image and recognize the drowsy driver or not.When the driver is sleepy, it plays the buzzer alarm and increases the buzzer sound. If the driver doesn't wake up, they'll send a text message and email to their family members about their situation. Hence, this utility goes beyond the problem of detecting drowsiness while driving. Eye extraction, face extraction with dlib.

scholarly journals Driver Drowsiness Detection Based on Face Feature and Perclos

2021 ◽  
pp. 108-112
Author(s):  
S. Gopi ◽  
Dr. E. Punarselvam ◽  
K. Dhivya ◽  
K. Malathi ◽  
N. Sandhanaselvi
Keyword(s):  
Traffic Accidents ◽  
Road Accidents ◽  
Steering Angle ◽  
Severe Injuries ◽  
Drowsiness Detection ◽  
Risk Of Death ◽  
Driver Drowsiness ◽  
The Face ◽  
Contactless System ◽  
Left And Right

Driving vehicles are complex and require undivided attention to prevent road accidents. Fatigue and distraction are a major risk factor that causes traffic accidents, severe injuries, and a high risk of death. Some progress has been made for driver drowsiness detection using a contact-based method that utilizes vehicle parts (such as steering angle and pressure on the pedal) and physiological signals (electrocardiogram and electromyogram). However, a contactless system is more potential for real-world conditions. In this study, we propose a computer vision-based method to detect driver's drowsiness from a video taken by a camera. The method attempts to recognize the face and then detecting the eye in every frame. From the detected eye, iris regions for left and right eyes are used to calculate the PERCLOS measure (the percentage of total time that eye is closed). The proposed method was evaluated based on public YawDD video dataset. The results found that PERCLOS value when the driver is alert is lower than when the driver is drowsy.

scholarly journals Trends and Future Prospects of the Drowsiness Detection and Estimation Technology

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7921
Author(s):  
Toshiya Arakawa
Keyword(s):  
Traffic Accidents ◽  
Autonomous Driving ◽  
Detection Methods ◽  
Future Prospects ◽  
Drowsiness Detection ◽  
Detection And Estimation ◽  
Detection Systems ◽  
Graphic Information ◽  
Types Of Information ◽  
Biometric Information

Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver’s drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and driver’s graphic information. This review summarizes the research and development trends of drowsiness detection systems based on various methods. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. In the case of autonomous driving levels 4 and 5, where the driver is not the primary driving agent, the technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination.

scholarly journals A Review of EEG Signal Features and their Application in Driver Drowsiness Detection Systems

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3786
Author(s):  
Igor Stancin ◽  
Mario Cifrek ◽  
Alan Jovic
Keyword(s):  
Deep Learning ◽  
Difficult Problem ◽  
Learning Approaches ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Detection Systems ◽  
Driver Drowsiness ◽  
Signal Features ◽  
Challenges And Opportunities ◽  
Electroencephalogram Eeg

Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that is often approached using neurophysiological signals as the basis for building a reliable system. In this context, electroencephalogram (EEG) signals are the most important source of data to achieve successful detection. In this paper, we first review EEG signal features used in the literature for a variety of tasks, then we focus on reviewing the applications of EEG features and deep learning approaches in driver drowsiness detection, and finally we discuss the open challenges and opportunities in improving driver drowsiness detection based on EEG. We show that the number of studies on driver drowsiness detection systems has increased in recent years and that future systems need to consider the wide variety of EEG signal features and deep learning approaches to increase the accuracy of detection.

ADHD is a Risk Factor of Road Crashes: A Purposive Study of Finding Relation between ADHD and Road Traffic Accident in Bangladesh

2018 ◽  
Vol 9 (08) ◽  
pp. 20531-20536
Author(s):  
Nusrat Shamima Nur ◽  
M. S. l. Mullick ◽  
Ahmed Hossain
Keyword(s):  
Risk Factor ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Road Crashes ◽  
Road Crash ◽  
Day By Day

Background: In Bangladesh fatality rate due to road traffic accidents is rising sharply day by day. At least 2297 people were killed and 5480 were injured in road traffic accidents within 1st six months of 2017.Whereas in the previous year at 2016 at least 1941 people were killed and 4794 were injured within the 1st six months. No survey has been reported in Bangladesh yet correlating ADHD as a reason of impulsive driving which ends up in a road crash.

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