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Open AccessJournal ArticleDOI

Proactive Driver Alert System (PDAS) for Drowsy Drivers

Ramzi Saifan
- Vol. 5, Iss: 1, pp 42-55
TLDR
A Drowsy Driver Safety System prototype is developed to watch the driver, and generate an audio alarm when the driver is detected as drowsy, and a supplementary technique based on the relationship between the speed and the drift angle of the car is used to detect drowsiness divers.
Abstract
Vehicle drivers lose their focus when fall sleepy which causes very dangerous accidents because the drivers do not even try to avoid the accident. In many cases, these accidents are killing. Therefore, many automobile companies tried to help in detecting drowsy drivers and alert them before they commit accidents. In this paper, we are going to develop a Drowsy Driver Safety System prototype to watch the driver, and generate an audio alarm when the driver is detected as drowsy. This audio alarm helps keeping the driver in contact to avoid any consequences. There exist several drowsy detection systems. Most of them focus on detecting drowsiness using techniques such as steering behavior, lane tracking, eye detection, yawning state or a combination of them. In this paper, we also track the driver eye which is similar to some others. But, we are suggesting new techniques like driver’s hand tension, which is also a considerable feature for drowsiness detection. We also use a supplementary technique based on the relationship between the speed and the drift angle of the car to detect drowsy divers.

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Citations
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Book ChapterDOI

IOT – eye drowsiness detection system by using intel edison with gps navigation

TL;DR: A drowsiness detection system with notification of accident and the location by using Global Positioning System (GPS) navigation, where if the driver’s eyes are closed about more than 4 s, the driver considers as drowsy and an alarm system will be activated to warn the driver and notify the status and location to relative for further action via message (SMS).
Proceedings ArticleDOI

UDDSUI: An Unsafe Driving Detection System Using IoT

TL;DR: In this paper , the authors proposed an unsafe driving detection system using IoT (UDDSUI) which monitors the proximity of an adjacent car, alcohol intake and the exhaustion of the driver.
Proceedings ArticleDOI

UDDSUI: An Unsafe Driving Detection System Using IoT

TL;DR: In this article , the authors proposed an unsafe driving detection system using IoT (UDDSUI) which monitors the proximity of an adjacent car, alcohol intake and the exhaustion of the driver.
References
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A comparative study of texture measures with classification based on featured distributions

TL;DR: This paper evaluates the performance both of some texture measures which have been successfully used in various applications and of some new promising approaches proposed recently.

An overview of the 100-car naturalistic study and findings

TL;DR: The 100-Car Naturalistic Driving Study database contains extreme cases of driving behavior and performance, including severe fatigue, impairment, judgment error, risk taking, willingness to engage in secondary tasks, aggressive driving, and traffic violations as mentioned in this paper.
Book

Raspberry Pi User Guide

TL;DR: This updated third edition of the Raspberry Pi User Guide covers the model B+ Raspberry Pi and its software, additional USB ports, and changes to the GPIO, including new information on Arduino and Minecraft on the Pi.
Proceedings Article

Drowsiness monitoring by steering and lane data based features under real driving conditions

TL;DR: New measures (features) for detecting drowsiness are proposed in addition to promising features in literature in order to reduce the number of road-crashes caused by fatigued drivers using standard equipment sensors.