Driver drowsiness detection using mixed-effect ordered logit model considering time cumulative effect

Zhang, Xuxin; Wang, Xuesong; Yang, Xin; Xu, Chuan; Xiao-hui, Zhu; Wei, Jiaohua

doi:10.1016/j.amar.2020.100114

Cited by 43 publications

(19 citation statements)

References 48 publications

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“…We also performed comparisons with previous studies. Previous studies [27,29,31,40] used traditional machine learning-based methods, whereas Arefnezhad et al [33] proposed a CNN-LSTM network to detect drowsy driving.…”

Section: Experiments Results For the Proposed Ensemble Cnnmentioning

confidence: 99%

“…As shown in Table 10, SWA data are used in many studies and pedal pressure data are also used to detect drowsy driving [29,31,33,40]. Although not used in this paper, in-vehicle sensor data such as lateral acceleration data [33] and vehicle speed [27] are also used. In addition, deviation of the vehicle position in a lane [27,33,40] or PERCLOS [27] are used to improve the detection performance.…”

Section: Experiments Results For the Proposed Ensemble Cnnmentioning

confidence: 99%

“…Although not used in this paper, in-vehicle sensor data such as lateral acceleration data [33] and vehicle speed [27] are also used. In addition, deviation of the vehicle position in a lane [27,33,40] or PERCLOS [27] are used to improve the detection performance.…”

Section: Experiments Results For the Proposed Ensemble Cnnmentioning

confidence: 99%

“…Sensor data-based methods use two types of sensor data: in-vehicle sensor data and external sensor data. The methods can extract statistical [27,28] and time series features [29,30] from the acquired data. They can detect changes in vehicle movement patterns using the extracted features and traditional machine learning techniques such as random forest [31] and support vector machine (SVM) [32].…”

Section: Introductionmentioning

confidence: 99%

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Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data

Jeon

Kim²,

Baek

2021

Sensors

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Drowsy driving is a major threat to the safety of drivers and road traffic. Accurate and reliable drowsy driving detection technology can reduce accidents caused by drowsy driving. In this study, we present a new method to detect drowsy driving with vehicle sensor data obtained from the steering wheel and pedal pressure. From our empirical study, we categorized drowsy driving into long-duration drowsy driving and short-duration drowsy driving. Furthermore, we propose an ensemble network model composed of convolution neural networks that can detect each type of drowsy driving. Each subnetwork is specialized to detect long- or short-duration drowsy driving using a fusion of features, obtained through time series analysis. To efficiently train the proposed network, we propose an imbalanced data-handling method that adjusts the ratio of normal driving data and drowsy driving data in the dataset by partially removing normal driving data. A dataset comprising 198.3 h of in-vehicle sensor data was acquired through a driving simulation that includes a variety of road environments such as urban environments and highways. The performance of the proposed model was evaluated with a dataset. This study achieved the detection of drowsy driving with an accuracy of up to 94.2%.

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Section: Experiments Results For the Proposed Ensemble Cnnmentioning

confidence: 99%

Section: Experiments Results For the Proposed Ensemble Cnnmentioning

confidence: 99%

Section: Experiments Results For the Proposed Ensemble Cnnmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ensemble CNN to Detect Drowsy Driving with In-Vehicle Sensor Data

Jeon

Kim²,

Baek

2021

Sensors

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“…Then, these answers tend to pass through KSS and results are generated from the KSS scale. Due to the simplicity of this approach to detect fatigue of vehicle driver is stated as simple approach which does not require complicated computation and processing [19,20,21]. However, core problem with the vehicle driving parameter in the detection of fatigue is that vehicle driving parameters often results in poor accuracy and sensitivity.…”

Section: Background Studymentioning

confidence: 99%

Robust Drowsiness Detection for Vehicle Driver using Deep Convolutional Neural Network

Saif¹,

Mahayuddin²

2020

IJACSA

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Drowsiness detection during driving is still an unsolved research problem which needs to be addressed to reduce road accidents. Researchers have been trying to solve this problem using various methods where most of these solution lacks behind in accuracy, real-time performance, costly, complex to build, and has a higher computational cost with low frame rate. This research proposes robust method for drowsiness detection of vehicle drivers based on head pose estimation and pupil detection by extracting facial region initially. Proposed method used frame aggregation strategy in case of face region cannot be extracted in any frame due to shortcomings, i.e. light reflection, shadow. In order to improve identification under highly varying lighting conditions, proposed research used cascade of regressors cutting edge method where each regression refers estimation of facial landmarks. Proposed method used deep convolutional neural network (DCNN) for accurate pupil detection to learn non linear data pattern. In this context, challenges of varying illumination, blurring and reflections for robust pupil detection are overcome by using batch normalization for stabilizing distributions of internal activations during training phase which makes overall methodology less influenced by parameter initialization. Proposed research performed extensive experimentation where accuracy rate of 98.97% was achieved using frame rate of 35 fps which is higher comparing with previous research results. Experimental results reveal the effectiveness of the proposed methodology.

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