Abstract
In current years, drowsy driver detection is the most necessary procedure to prevent any road accidents, probably worldwide. The aim of this study was to construct a smart alert technique for building intelligent vehicles that can automatically avoid drowsy driver impairment. But drowsiness is a natural phenomenon in the human body that happens due to different factors. Hence, it is required to design a robust alert system to avoid the cause of the mishap. In this proposed paper, we address a drowsy driver alert system that has been developed using such a technique in which the Video Stream Processing (VSP) is analyzed by eye blink concept through an Eye Aspect Ratio (EAR) and Euclidean distance of the eye. Face landmark algorithm is also used as a proper way to eye detection. When the driver’s fatigue is detected, the IoT module issues a warning message along with impact of collision and location information, thereby alerting with the help of a voice speaking through the Raspberry Pi monitoring system.
Highlights
Driver fatigue has been the main issue for countless mishaps due to tiredness, tedious road condition, and unfavorable climate situations [1]
This research provides a robust method for detecting drowsiness of drivers and collision impact system in the present time
The proposed system is used to construct a nonintruding technique for measuring drowsiness of the driver with severity of collision due to braking or mishap. This system’s main components are the Raspberry Pi3 model B module and Pi camera module that are used for persistent recording of face landmarks that are localized through facial landmark points to calculate Eye Aspect Ratio (EAR)
Summary
Driver fatigue has been the main issue for countless mishaps due to tiredness, tedious road condition, and unfavorable climate situations [1]. Road accidents mostly occur due to inadequate way of driving [2]. These situations arise if the driver is addicted to alcohol or in drowsiness [3]. With face landmark algorithm and Euclidean distance in the behavioralbased approach. These characteristics help to measure driver fatigue and instantly alert him with the help of voice speaker and forwarding an e-mail to a person (owner of vehicle) who can make him conscious [6]. The proposed system is being integrated by a credit card-sized computer known as Raspberry Pi3 and Pi camera which can trace an eye movement [9] thereby monitoring intensity of collision effects that happen at the time of accident and alerting the emergency ward of the hospitals or owners that are nearby to the accident spot along with GPS location of the accident [10, 11]
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