Intelligent Drowsiness Drive Warning System

Joo, Young-Hoon; Kim, Jinkyu

doi:10.5391/jkiis.2008.18.2.223

Cited by 6 publications

(2 citation statements)

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“…반의 졸음감지 연구가 주류를 이루었다 [3,4] . 또한 스트 레스와 말초체온과의 상관관계 [5] , 심박수의 변화 [6] , 피 부 임피던스 신호분석 [7] 을 연구결과가 발표되었다 [8] .…”

Section: 졸음 감지에 대한 기존 연구는 눈의 깜빡거림 (Blinking) 동공의 크기 눈의 응시방향 얼굴표정을 unclassified

Electroencephalogram-Based Driver Drowsiness Detection System Using Errors-In-Variables(EIV) and Multilayer Perceptron(MLP)

Song

2014

The Journal of Korean Institute of Communications and Informati

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Drowsy driving is a large proportion of the total car accidents. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. Many researches have been published that to measure electroencephalogram(EEG) signals is the effective way in order to be aware of fatigue and drowsiness of drivers. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, transition, and drowsiness. This paper proposes a drowsiness detection system using errors-in-variables(EIV) for extraction of feature vectors and multilayer perceptron (MLP) for classification. The proposed method evaluates robustness for noise and compares to the previous one using linear predictive coding (LPC) combined with MLP. From evaluation results, we conclude that the proposed scheme outperforms the previous one in the low signal-to-noise ratio regime.

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Section: 졸음 감지에 대한 기존 연구는 눈의 깜빡거림 (Blinking) 동공의 크기 눈의 응시방향 얼굴표정을 unclassified

Electroencephalogram-Based Driver Drowsiness Detection System Using Errors-In-Variables(EIV) and Multilayer Perceptron(MLP)

Song

2014

The Journal of Korean Institute of Communications and Informati

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“…Most driver state detection (DSD) technologies adopt methods based on cameras or other sensors, but these devices are difficult to install and may constrain the behavior of drivers. Most in-vehicle DSD systems are also less reliable than rule-based artificial intelligence (AI) systems [3]. Furthermore, as the detection accuracy depends on the in-vehicle environment, which varies continuously, the outputs of DSD systems are prone to uncertainty.…”

Section: Introductionmentioning

confidence: 99%

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

Chung

Kim

2020

Sustainability

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This paper elucidates the development of a deep learning–based driver assistant that can prevent driving accidents arising from drowsiness. As a precursor to this assistant, the relationship between the sensation of sleep depravity among drivers during long journeys and CO2 concentrations in vehicles is established. Multimodal signals are collected by the assistant using five sensors that measure the levels of CO, CO2, and particulate matter (PM), as well as the temperature and humidity. These signals are then transmitted to a server via the Internet of Things, and a deep neural network utilizes this information to analyze the air quality in the vehicle. The deep network employs long short-term memory (LSTM), skip-generative adversarial network (GAN), and variational auto-encoder (VAE) models to build an air quality anomaly detection model. The deep learning models gather data via LSTM, while the semi-supervised deep learning models collect data via GANs and VAEs. The purpose of this assistant is to provide vehicle air quality information, such as PM alerts and sleep-deprived driving alerts, to drivers in real time and thereby prevent accidents.

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A Study on Drowsy Driving Detection using SURF

Choi¹,

Choi²

2012

J. Korea Inst. Intelligent. Transp. Syst.

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In this paper, we propose a drowsy driver detection system with a novel eye state detection method that is adaptive to various vehicle environment such as glasses, light and so forth using SURF(Speed Up Robust Feature) which can extract quickly local features from images. Also the performance of eye state detection is improved as individual three eye-state templates of each driver can be made using Bayesian inference. The experimental results under various environment with average 98.1% and 96.1% detection rate in the daytime and at night respectively and those in the opened ZJU database with average 97.8% detection rate show that the proposed method outperforms the current state-of-the-art.

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Intelligent Drowsiness Drive Warning System

Cited by 6 publications

References 0 publications

Electroencephalogram-Based Driver Drowsiness Detection System Using Errors-In-Variables(EIV) and Multilayer Perceptron(MLP)

Electroencephalogram-Based Driver Drowsiness Detection System Using Errors-In-Variables(EIV) and Multilayer Perceptron(MLP)

An Automobile Environment Detection System Based on Deep Neural Network and its Implementation Using IoT-Enabled In-Vehicle Air Quality Sensors

A Study on Drowsy Driving Detection using SURF

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