Every year, traffic accidents due to human errors cause increasing amounts of deaths and injuries globally. To help reduce the amount of fatalities, in the paper presented here, a new module for Advanced Driver Assistance System (ADAS) which deals with automatic driver drowsiness detection based on visual information and Artificial Intelligence is presented. The aim of this system is to locate,...

This algorithm gives insight into the possible technique to recognize the state of the eye whether open or close. The algorithm explores the various features of the eyes when in closed and in open states. Using this feature, the decision is made whether the state of the eye is closed or open to detect drowsiness while driving.

This paper reviews the methodology of detecting drowsiness of a person from Electrocardiogram (ECG) signal. ECG is one of the most important physiological biosignal which can be used to study the cardiological behavior of a person. It has become utterly essential nowadays to predict the drowsiness of a person in a situation as important as driving. The Heart Rate Variability (HRV) derived from ...

Fatigue is the most vital factor of road fatalities, and one manifestation fatigue during driving drowsiness. In this paper, we propose using deep Q-learning to study correlation between drowsiness performance. This carried out by analyzing an electroencephalogram (EEG) dataset captured a simulated endurance test. Driving safety research EEG data represents important brain-computer interface (B...

This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-bas...

The advance of computing technology has provided the means for building intelligent vehicle systems. Drowsy driver detection system is one of the potential applications of intelligent vehicle systems. Previous approaches to drowsiness detection primarily make pre-assumptions about the relevant behavior, focusing on blink rate, eye closure, and yawning. Here we employ machine learning to data-mi...

This project is the extension of NATSRL FY 2008 and FY2009 projects, titled as “Real-time Nonintrusive Detection of Driver Drowsiness”, which aims to develop a real-time, nonintrusive driver drowsiness detection system to reduce drowsiness-caused accidents. In our previous research, nonintrusive sensors for driver’s heart beat measurement were developed and implemented on the vehicle steering w...

The main idea behind this project is to develop a system which can detect drowsiness of the driver and giving an indication in the form of alarm. Since a large number of road accidents occur due to the driver drowsiness, here we are proposing a new transportation system which will reduce the accidents. This system will monitor the driver’s eyes using camera and face detection which is very impo...

— Sleepiness is one of the first causal factors of accidents. An estimated 10-30% of road deaths are related to fatigue driving. A large number of research studies have been conducted to reduce the risk of accidents while driving. Many of these studies are based on the detection of biological signals by drowsiness/sleepiness. The activity of the autonomic nervous system (ANS) presented alterati...