Vehicle Driver Drowsiness Monitoring and Warning System

Vesselényi, Tiberiu; Rus, Alexandru; Mitran, Tudor; Tataru, Bogdan; Moldovan, Ovidiu

doi:10.1007/978-3-319-45447-4_96

Cited by 5 publications

(10 citation statements)

References 8 publications

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“…The method first uses physiological signal measuring instruments to obtain the driver's electrocardiogram (ECG), electroencephalogram (EEG) [4][5][6][7][8][9], electromyography (EMG) [5,7,8], eyeball rotation (EOG) [7], heart rate variability (HRV) [10][11][12], respiratory state, etc., then extract the characteristics of the above signals, and build a classification model according to the characteristics to estimate the driver's fatigue.…”

Section: Fatigue Detection Based On Driver's Physiological Signalmentioning

confidence: 99%

“…LIN et al [4] extracted independent component analysis (ICA) features from EEG signals, constructed fuzzy neural networks and linear regression models based on the features to estimate driver fatigue. Vesseleny et al [5] detected driving fatigue based on EEG and EMG measurement technology. Zhang et al [7] have proposed a real-time driving fatigue assessment method using the entropy and complexity of EEG, EMG and EOG.…”

Section: Fatigue Detection Based On Driver's Physiological Signalmentioning

confidence: 99%

“…Therefore, the output of the C layer has F depth component matrix, that is, the output depth of the C layer is D = F depth . After the convolution operation of the input data and the filter, the value of the element located in the i th row and the j th column is z d,i,j at a certain output depth d of the output matrix, as shown in formula (5).…”

Section: Layermentioning

confidence: 99%

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Real time detection of driver fatigue based on CNN‐LSTM

Liu

et al. 2021

IET Image Processing

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Fatigue driving is one of the main causes of traffic accidents. In order to solve this problem, a new Convolutional Neural Network and Long Short-Term Memory (CNN-LSTM) based real-time driver fatigue detection method is proposed. First of all, using simple linear clustering algorithm (SLIC), the driver's image is divided into super pixels of uniform size, which are used as input of CNN, and CNN is trained to automatically learn the features of eyes and mouth contained in the image, and then the location and area of eyes and mouth are obtained by using the trained CNN. On this basis, the eye feature parameter Perclos, mouth feature parameter MClosed and face orientation feature parameter Phdown are extracted, and the above feature parameters on the continuous time series and steering wheel angle feature parameter SA are taken as the input of LSTM, and the fatigue level is taken as the output to detect the fatigue state of the driver in real time. Experimental data shows that this method can not only overcome the influence of illumination, background, angle and individual differences, but also the accuracy of detection can reach 99.78%, and the average detection time is 16.94 ms/frame.

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Section: Fatigue Detection Based On Driver's Physiological Signalmentioning

confidence: 99%

Section: Fatigue Detection Based On Driver's Physiological Signalmentioning

confidence: 99%

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Real time detection of driver fatigue based on CNN‐LSTM

Liu

et al. 2021

IET Image Processing

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“…Over time, several methods have been proposed to determine driver fatigue: analysis of the driver’s movements and face expressions, or biological signals measures that relate to monitoring of the driver behavior and vehicle movement [ 6 ]. The method of analyzing the driver’s facial movements and expressions includes techniques for measuring mouth movements, eye closing, eye blinking, and head position [ 7 , 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions

Beles,

Vesselenyi,

Rus

et al. 2024

Sensors

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The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver’s alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle’s commands.

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“…In the literature (Vesselenyi et al, 2009(Vesselenyi et al, , 2016(Vesselenyi et al, , 2017(Vesselenyi et al, , 2019a(Vesselenyi et al, , 2019bNagy et al, 2017Nagy et al, , 2018, several system variants were presented, among which one of the systems was based on the use of EEG, EOG and ECG sensors. These sensors have a good enough accuracy, but a rather inconvenient problem exists.…”

Section: Introductionmentioning

confidence: 99%

Driver alertness monitoring system in the context of safety increasing and sustainable energy use

Scurt

Beles²,

Vesselényi³

et al. 2023

CogSust

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Road transport is an important factor in carbon dioxide emissions. These emissions can be reduced by improving propulsion sources and traffic flow (avoiding traffic jams due to accidents). This article presents a system for monitoring and warning the driver to prevent a possible accident involving material damage, injury, or loss of life. The system performs video monitoring of the driver in order to determine his state (tired or attentive). By reducing traffic incidents and traffic jams, the energy consumed will not be wasted; thus, more sustainable transport energy use can be achieved.

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Vehicle Driver Drowsiness Monitoring and Warning System

Cited by 5 publications

References 8 publications

Real time detection of driver fatigue based on CNN‐LSTM

Real time detection of driver fatigue based on CNN‐LSTM

Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions

Driver alertness monitoring system in the context of safety increasing and sustainable energy use

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