Full Length Driver Drowsiness Detection Model—Utilising Driver Specific Judging Parameters

Relangi, Sree Pradeep Kumar; Nilesh, Mutyam; Kumar, Kintali Pavan; Naveen, A

doi:10.1007/978-981-15-1616-0_77

Cited by 4 publications

(2 citation statements)

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“…This technology uses the eye blinking ratio to determine whether or not to slow down the vehicle. In a separate investigation [22], facial landmark points were used to identify the person's eye flickering rate. An application to develop human-computer interaction for determining the degree of tiredness was suggested by researchers in [23].…”

Section: Literature Reviewmentioning

confidence: 99%

Smart vehicle management by using sensors and an IoT based black box

Rahman

Kabir

Mizan

et al. 2022

IJRES

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As the number of transports on the road increases every day, so does the number of accidents. Reckless driving and consuming alcohol are two of the leading causes of accidents. Apart from these issues, the safety of humans and vehicles is also critical. A thorough investigation is required to minimize the accident rate and improve human safety, particularly if an incident occurs. The purpose of this study is to develop a few sensor-based black box system that will help us reduce traffic accidents by continuously providing precise guidance to the driver. At the same time, the evidence will be uploaded to its server for further evaluation. This system also includes a way of detecting drowsiness in the driver. Finally, using global positioning system (GPS) and global system for mobile communications (GSM), the relevant authorities will get information on the vehicle's condition and whereabouts. For security purposes, a panic button is introduced here to get emergency help from the security personnel by detecting the victim’s area.

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Section: Literature Reviewmentioning

confidence: 99%

Smart vehicle management by using sensors and an IoT based black box

Rahman

Kabir

Mizan

et al. 2022

IJRES

View full text Add to dashboard Cite

show abstract

“…By measuring the eye blinking ratio, the system can determine when to slow down the vehicle. Another study [7] used facial landmark points to track eye flickering rates, further enhancing drowsiness detection. Researchers in [8] suggested a human-computer interaction application that assesses the degree of tiredness by estimating head movements, eye status, and yawning, without requiring any wearable accessories.…”

Section: Introduction -mentioning

confidence: 99%

Enhancing Vehicle Safety: Smart Accident Detection Using Vehicle Black Box Technology

2024

IJSREM

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This paper introduces a state-of-the-art vehicle black box system aimed at improving vehicle safety through intelligent accident detection and rapid emergency response. The system integrates an array of sensors and Internet of Things (IoT) technologies to monitor and record critical driving data, enabling real-time accident detection and immediate alert notifications. Key components of the system include accelerometers for impact detection, gyroscopes for assessing vehicle orientation, speed sensors for monitoring velocity, and environmental sensors such as cameras and proximity detectors. Driver condition monitoring tools, like breathalysers for measuring alcohol levels, are also incorporated. Data from these sensors is continuously processed in real-time using advanced algorithms designed to identify unusual conditions indicative of accidents. For instance, sudden deceleration combined with abrupt orientation changes can signal a collision. Upon detecting an accident, the system promptly sends alerts to emergency services through a GSM-based communication module. These alerts contain detailed information about the incident, including the vehicle's location, impact severity, and pre- accident sensor data, which is crucial for reducing emergency response times. A significant benefit of the vehicle black box system is its ability to provide valuable post-accident data for forensic analysis, helping to determine accident causes and improve future vehicle safety designs. This data aids manufacturers and policymakers in developing better safety regulations and technologies

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