— 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...

Drowsiness detection system is regarded as an effective tool to reduce the number of road accidents. This project proposes a non-intrusive approach for detecting drowsiness in drivers, using Computer Vision. The algorithm is coded on OpenCV platform in Linux environment. The parameters considered to detect drowsiness are face and eye detection, blinking, eye closure and gaze. Input is captured ...

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...

Prof. M. M. Mukhedkar Department of E&TC Engineering, DYPCOE, DYPCOE, Ambi, Talegaon Pune, India Abstract—One of the leading reasons for traffic accidents is distracted attention of driver. As day by day numbers of vehicles on road are increasing, the proportion of number of accidents is also increasing. Drivers with a low vigilance level suffer from marked decline in their abilities of percept...

Although there is currently significant development in active vehicle safety (AVS) systems, the number of accidents, injury severity levels and fatalities has not reduced. In fact, human error, low performance, drowsiness and distraction may account for a majority in all the accident causation. Active safety systems are unaware of the context and driver status, so these systems cannot improve t...

Partial driving automation systems execute sustained lateral and longitudinal control, while humans are required to supervise the automated features. Similar with drivers using manual settings, those may also experience drowsiness. However, existing that aim detect driver drowsiness tend be unreliable. This study applies a dual-control scheme in partial context which must keep their hands on ve...

OBJECTIVES Drowsy driving is a serious highway safety problem. If drivers could be warned before they became too drowsy to drive safely, some drowsiness-related crashes could be prevented. The presentation of timely warnings, however, depends on reliable detection. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. Th...