Advanced Driver Fatigue Research

Eskandarian, Azim; Sayed, Riaz Akbar; Delaigue, Pierre; Mortazavi, A; Blum, Jeremy

doi:10.1037/e563992012-001

Cited by 36 publications

(25 citation statements)

References 47 publications

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“…The average square error with EEG and ECG is 1.2629 which showed better performance than with the absence of the above signals. Artificial Neural Networks(ANN) [42] Vehicle parameter data which is steering angle and eye closure data Fatigue detection Accuracy for truck study with only steering data 85%, steering and eye data 88%, Car study only steering data 86%, steering and eye data 92%.…”

Section: Driver Fatigue Recognitionmentioning

confidence: 99%

“…In the case of image processing behavioural signs of drowsiness like yawning, eyes closed and yawning while eyes closed are detected. While all of the above signs of drowsiness from EEG, gyroscope and image processing may happen even in normal driving state but the likelyhood and frequency with which it happens increases with the level of fatigue experienced by the driver [42]. Therefore for multimodal driver fatigue detection system a deep neural network that uses the prediction from EEG module, gyroscope module and vision module as inputs is proposed as shown in Fig.3.…”

Section: Driver Fatigue Recognitionmentioning

confidence: 99%

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Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing

Karuppusamy¹,

Kang

2020

IEEE Access

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Sleepiness detection system that evaluates driver's sleepiness level has always been the primary interest of researchers. There are a number of systems like electroencephalography-based sleepiness detection system (ESDS), vehicle based sleepiness estimator system, image acquisition technology and biomathematical models to detect drowsiness of drivers. However there has been less research on hybrid of these systems that detect sleepiness of drivers. In order to overcome the above limitation we propose a neural network based hybrid multimodal system that detects driver fatigue using electroencephalography(EEG) data, gyroscope data and image processing data. It was found that the proposed hybrid system performed well with a detection accuracy of 93.91% in identifying the drowsiness state of the driver.

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Section: Driver Fatigue Recognitionmentioning

confidence: 99%

Section: Driver Fatigue Recognitionmentioning

confidence: 99%

Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing

Karuppusamy¹,

Kang

2020

IEEE Access

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“…Another group of methods for driver drowsiness detection is based on performance measures and the analysis of driver behaviour and actions. These metrics are observable in vehicle control, unwanted speed change, unusual lane-changing and steering, changes in head motion and yawning (Liang and Lee, 2010;Eskandarian et al, 2007). The information about driver behaviour can be obtained in digital format, through the CAN port in the vehicle (Zhou et al, 2008).…”

Section: Problem Definition and Research Objectivesmentioning

confidence: 99%

Robust real-time driver drowsiness detection based on image processing and feature extraction methods

Keyvanara

Salehi

Monadjemi

2018

IJVS

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Recently, the human lifestyle has strongly been affected by the novel technological equipment. The applications of Artificial Intelligence are widely being utilised to improve the performance and quality of the modern life. One of the important applications of these techniques is to seek to improve public safety, including the safety of driving. The statistics indicate that the mortality of car accidents yearly constitutes a significant proportion of the overall deaths. A number of strategies have been studied to materialise driver drowsiness detection systems. One of the best strategies relies on image processing and computer vision methods. In this paper, a novel real-time method for driver drowsiness detection is presented. This method uses Haar wavelet-based features for face detection. The eye state determination has been performed using PCA feature extraction along with an SVM classifier. The proposed method has been implemented and tested on a real-time ARM based embedded system, with a camera installed in front of the driver. Results show that the presented intelligent system has a high detection accuracy, compared to the methods presented thus far, on the standard datasets such as BioID and RS-DMV.

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“…At the present stage of development of transport system, passengers tend to prefer public means of transport to individual vehicles. This tendency is well apparent in highly urbanized metropolises [1,3].…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Vibroacoustic Climate Inside the Buses Solaris Urbino 12 Used in Public Transport Systems

2012

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Despite new technological solutions and attempts to improve the working conditions during the operation of vehicles, city bus drivers are still exposed to negative impacts of noise and vibrations, causing stress, increased fatigue and lead to numerous diseases. Therefore it seems merited to investigate the acoustic climate (i.e. acoustic phenomena caused by the sources of noise of vibrations in the function of time and space) and the working conditions during the operation of Solaris Urbino 12 vehicles, widely used in public transport systems. The main aim of the research was to show the inuence of the 5 day, 8 h working week on selected psycho-physiological parameters on a driver's organism. It was assumed that the main aggravating factor of the driver's organism are vibroacoustics conditions of a work place. The results of this research will be presented in due course. This work presents the research on vibroacoustic climate in one of many city buses used Solaris Urbino 12. Investigation of the vibroacoustic climate is the branch of environmental biophysics. The measurements, tests and analysis are genuine because they were carried out under real conditions, with passengers, in the long stretches of time and in the entire route of the vehicle. Presented results are very rarely reported in the literature.

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Advanced Driver Fatigue Research

Cited by 36 publications

References 47 publications

Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing

Multimodal System to Detect Driver Fatigue Using EEG, Gyroscope, and Image Processing

Robust real-time driver drowsiness detection based on image processing and feature extraction methods

Investigation of the Vibroacoustic Climate Inside the Buses Solaris Urbino 12 Used in Public Transport Systems

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