Vehicle Driver Monitoring through the Statistical Process Control

Assuncao, Arthur N.; Aquino, André L. L.; Santos, Ricardo C. Câmara de M.; Guimarães, Rodolfo Labiapari Mansur; Oliveira, Ricardo Augusto Rabelo

doi:10.3390/s19143059

Cited by 9 publications

(12 citation statements)

References 46 publications

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“…For instance, in [22], besides the steering wheel angle, pedal input, vehicle speed and acceleration were selected as features for classification model based on the dynamic Bayesian network. The deviation from the current lane could also be a useful indicator of driver drowsiness, as verified using an exponentially weighted moving average [23].…”

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 96%

“…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%

“…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%

“…It can be seen from Table 2 that some works [20][21][22][23][25][26][27]] did not employ cross-validation. The bias training dataset (lack of generalization) may be selected to obtain high accuracy.…”

Section: Comparisons To Related Workmentioning

confidence: 99%

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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: 96%

Section: Existing Work Of Driver Drowsiness Recognitionmentioning

confidence: 99%

Section: Research Gaps and Motivationmentioning

confidence: 99%

Section: Comparisons To Related Workmentioning

confidence: 99%

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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Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

2020

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Human drivers have different driving styles, experiences, and emotions due to unique driving characteristics, exhibiting their own driving behaviors and habits. Various research efforts have approached the problem of detecting abnormal human driver behavior with the aid of capturing and analyzing the face of driver and vehicle dynamics via image and video processing but the traditional methods are not capable of capturing complex temporal features of driving behaviors. However, with the advent of deep learning algorithms, a significant amount of research has also been conducted to predict and analyze driver's behavior or action related information using neural network algorithms. In this paper, we contribute to first classify and discuss Human Driver Inattentive Driving Behavior (HIDB) into two major categories, Driver Distraction (DD), Driver Fatigue (DF), or Drowsiness (DFD). Then we discuss the causes and effects of another human risky driving behavior called Aggressive Driving behavior (ADB). Aggressive driving Behavior (ADB) is a broad group of dangerous and aggressive driving styles that lead to severe accidents. Human abnormal driving behaviors DD, DFD, and ADB are affected by various factors including driver experience/inexperience of driving, age, and gender or illness. The study of the effects of these factors that may lead to deterioration in the driving skills and performance of a human driver is out of the scope of this paper. After describing the background of deep learning and its algorithms, we present an in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver. We attempt to achieve a comprehensive understanding of HIADB detection by presenting a detailed comparative analysis of all the recent techniques. Moreover, we highlight the fundamental requirements. Finally, we present and discuss some significant and essential open research challenges as future directions.

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Driver Mental Fatigue Detection Based on Head Posture Using New Modified reLU-BiLSTM Deep Neural Network

Ansari

Naghdy

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Vehicle Driver Monitoring through the Statistical Process Control

Cited by 9 publications

References 46 publications

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

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

Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges

Driver Mental Fatigue Detection Based on Head Posture Using New Modified reLU-BiLSTM Deep Neural Network

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