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 datamin...

Primary Area Surrogate measures of safety ABSTRACT This paper describes the development of an in-vehicle measurement system that monitors the physiological signals (i.e., heart rate, heart rate variation, breathing and eye brinking) of drivers. These physiological signals will be utilized to detect the onset of driver fatigue, crucial for timely applying drowsiness countermeasures. Fatigue driv...

In recent years preventing accidents under drowsiness state has become a major focus for active safety driving. To reduce the accidents rate, it is needed to provide an efficient safety measure. Literature says that, the drowsiness condition of driver is best monitored by using an eye blink sensor (or) fabric electrode (or) ECG Sensor. But by monitoring the drowsiness condition alone, the accid...

This paper aims to provide reliable indications of driver drowsiness based on the characteristics of driver–vehicle interaction. A test bed was built under a simulated driving environment, and a total of 12 subjects participated in two experiment sessions requiring different levels of sleep (partial sleepdeprivation versus no sleep-deprivation) before the experiment. The performance of the subj...

The purpose of this paper is to develop a driver drowsiness and monitoring system that could act as an assistant the during driving process. aimed at reducing fatal crashes caused by driver’s distraction. For drowsiness, operates analysing eye blinks yawn frequency while for distraction, works based on head pose estimation tracking. alarm will be triggered if any these conditions occur. Main pa...

Due to demographic change, more elderly people have the need to preserve and support mobility by car despite age-related functional limitations. Since accidents by the elderly are primarily caused by age related limitations, and not by careless or irresponsible behavior, it may be beneficial to detect driving impairing conditions. The presented review gives an overview of technologies to detect...

Drowsiness is a serious problem, which causes a large number of car crashes every year.This paper presents an original drowsiness detection method based on the fuzzy merging of several eye blinking features extracted from an electrooculogram (EOG). These features are computed each second using a sliding window. This method is compared to two supervised learning classifiers: a prototype nearest ...

The large number of automobile accidents due to driver drowsiness is a critical concern of many countries. To solve this problem, numerous methods of countermeasure have been proposed. However, the results were unsatisfactory due to inadequate accuracy of drowsiness detection. In this study, we introduce a new approach, a combination of EEG and NIRS, to detect driver drowsiness. EEG, EOG, ECG a...

This paper presents a non-intrusive approach for monitoring driver drowsiness using the fusion of several optimized indicators based on driver physical and driving performance measures, obtained from ADAS (Advanced Driver Assistant Systems) in simulated conditions. The paper is focused on real-time drowsiness detection technology rather than on long-term sleep/awake regulation prediction techno...