Drowsiness monitoring based on steering wheel status

Chai, Meng; Li, Shiwu; Sun, Wencai; Guo, Mengzhu; Huang, Mengyuan

doi:10.1016/j.trd.2018.07.007

Cited by 61 publications

(37 citation statements)

References 17 publications

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“…Typical algorithms for driver drowsiness recognition were based on three types of inputs: (i) the biometric-signal-based approach [16][17][18][19]; (ii) the vehicle-based approach [20][21][22][23] and (iii) the image-based approach [24][25][26][27]. Approach (i) is intrusive whereas approaches (ii) and (iii) are non-intrusive.…”

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

“…With the same input, multilevel ordered logit model was implemented [21]. Some works utilized more measurements as inputs for driver drowsiness recognition.…”

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

“…Various methods have been proposed for driver drowsiness recognition [16][17][18][19][20][21][22][23][24][25][26][27] and driver stress recognition [28][29][30][31][32][33][34][35]. Nonetheless, there exist several challenges and limitations that require further research.…”

Section: Research Gaps and Motivationmentioning

confidence: 99%

“…First, the comparison was carried out on driver drowsiness recognition. Table 2 summarizes the performance between proposed MOGA optimized D-MKL-SVM and existing works [16][17][18][19][20][21][22][23][24][25][26][27]. It summarizes the category of input signals, dataset, methodology, types of cross-validation and performance.…”

Section: Comparisons To Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

Chui

Lytras

Liu

2020

Sensors

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Driver drowsiness and stress are major causes of traffic deaths and injuries, which ultimately wreak havoc on world economic loss. Researchers are in full swing to develop various algorithms for both drowsiness and stress recognition. In contrast to existing works, this paper proposes a generic model using multiple-objective genetic algorithm optimized deep multiple kernel learning support vector machine that is capable to recognize both driver drowsiness and stress. This algorithm simplifies the research formulations and model complexity that one model fits two applications. Results reveal that the proposed algorithm achieves an average sensitivity of 99%, specificity of 98.3% and area under the receiver operating characteristic curve (AUC) of 97.1% for driver drowsiness recognition. For driver stress recognition, the best performance is yielded with average sensitivity of 98.7%, specificity of 98.4% and AUC of 96.9%. Analysis also indicates that the proposed algorithm using multiple-objective genetic algorithm has better performance compared to the grid search method. Multiple kernel learning enhances the performance significantly compared to single typical kernel. Compared with existing works, the proposed algorithm not only achieves higher accuracy but also addressing the typical issues of dataset in simulated environment, no cross-validation and unreliable measurement stability of input signals. Driver drowsiness is about the sleepiness of drivers that is an undesired condition in real-world driving. Surveys revealed that more than half of adult drivers felt sleepy while driving and even more severe that about 30% of them fell asleep [3]. This high prevalence can further estimate that drivers could experience accidents (affect or being influenced attributable to drowsy driving). It is worth mentioning that driver drowsiness is differed from driver fatigue [4]. Drivers drive unconsciously in the former condition but are with a conscious status in latter condition. In reality, people are usually fatigued in today's fast paced world.Stress is body's way to react any kind of threat, challenge and demand. Research has revealed that stress has played a crucial role in adapting to driving and making decision [5]. The primary sources for driver stress are congestion and adverse driving condition as well as time pressure [6]. The stress can lead to poor and dangerous driving behaviors, for instance, flashing high beams, eliciting anger in drivers, road rage and aggressive driving, which are major causes of road traffic accidents [7]. Various recent studies have been carried out on the investigation between psychological factors and driving behaviors. In [8], results revealed that anger leads to stress, which is reflecting in the form of aggressive and negative cognitive driving behavior. On the other hand, research argued that risky drivers generally exhibited more antisocial and substance misuse, reward sensitive personality features and sensation seeking [9]. In addition, traffic penalties reported by public transp...

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Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

“…With the same input, multilevel ordered logit model was implemented [21]. Some works utilized more measurements as inputs for driver drowsiness recognition.…”

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

Section: Research Gaps and Motivationmentioning

confidence: 99%

Section: Comparisons To Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A Generic Design of Driver Drowsiness and Stress Recognition Using MOGA Optimized Deep MKL-SVM

Chui

Lytras

Liu

2020

Sensors

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“…There are several causes of traffic accidents such as the operator's consciousness, use of the phone, drug or alcohol use, fatigue which leads to drowsiness and loss of concentration. So far, several techniques for detecting drowsiness have been studied [3][4][5][6][7][8][9][10][11][12][13][14][15][16], which can be divided into three basic directions as follows:…”

Section: Introductionmentioning

confidence: 99%

A driver drowsiness and distraction warning system based on raspberry Pi 3 Kit

Thanh¹,

Linh²

2019

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In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and "frontal face detectors" in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of "Normal", "Close eyes", "Yawn" or "Distraction".

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