Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques

Jabbar, Rateb; Al-Khalifa, Khalifa N.; Kharbeche, Mohamed; Alhajyaseen, Wael; Jafari, Mohsen A.; Shan, Jiayuan

doi:10.1016/j.procs.2018.04.060

Cited by 171 publications

(72 citation statements)

References 7 publications

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“…These methods evaluate mainly three parameters: eye movements (eye blinking and eye closure activity) via eye-tracking, that was also investigated for usage in maritime operations and aviation [19][20][21], facial expressions (yawning, jaw drop, brow rise, and lip stretch), and head position (head scaling/nodding) [22]. In particular, many studies focused on the use of machine (deep) learning-based approaches [23][24][25][26][27]. Apart from research, numerous commercial products are available that rely on behavioral measures for drowsiness detection.…”

Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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Drowsy driving imposes a high safety risk. Current systems often use driving behavior parameters for driver drowsiness detection. The continuous driving automation reduces the availability of these parameters, therefore reducing the scope of such methods. Especially, techniques that include physiological measurements seem to be a promising alternative. However, in a dynamic environment such as driving, only non- or minimal intrusive methods are accepted, and vibrations from the roadbed could lead to degraded sensor technology. This work contributes to driver drowsiness detection with a machine learning approach applied solely to physiological data collected from a non-intrusive retrofittable system in the form of a wrist-worn wearable sensor. To check accuracy and feasibility, results are compared with reference data from a medical-grade ECG device. A user study with 30 participants in a high-fidelity driving simulator was conducted. Several machine learning algorithms for binary classification were applied in user-dependent and independent tests. Results provide evidence that the non-intrusive setting achieves a similar accuracy as compared to the medical-grade device, and high accuracies (>92%) could be achieved, especially in a user-dependent scenario. The proposed approach offers new possibilities for human–machine interaction in a car and especially for driver state monitoring in the field of automated driving.

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Section: Driver Drowsiness Measurement Technologiesmentioning

confidence: 99%

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Kundinger

Sofra

Riener

2020

Sensors

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“…Compare to the above deep-learning models, the authors in [35] used a CNN model with a transfer learning technique to develop the DDD system. On 5.5% misclassification accuracy was reported on a pre-trained model of AlexNet through visual features.…”

Section: Related Workmentioning

confidence: 99%

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

Abbas¹

2020

IJACSA

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Road accidents mainly caused by the state of driver drowsiness. Detection of driver drowsiness (DDD) or fatigue is an important and challenging task to save roadside accidents. To help reduce the mortality rate, the "HybridFatigue" DDD system was proposed. This HybridFatigue system is based on integrating visual features through PERCLOS measure and non-visual features by heartbeat (ECG) sensors. A hybrid system was implemented to combine both visual and non-visual features. Those hybrid features have been extracted and classified as driver fatigue by advanced deep-learning-based architectures in real-time. A multi-layer based transfer learning approach by using a convolutional neural network (CNN) and deep-belief network (DBN) was used to detect driver fatigue from hybrid features. To solve night-time driving and to get accurate results, the ECG sensors were utilized on steering by analyzing heartbeat signals in case if the camera is not enough to get facial features. Also to solve the accurate detection of center head-position of drivers, two-cameras were mounted instead of a single camera. As a result, a new HybridFatigue system was proposed to get high accuracy of driver's fatigue. To train and test this HybridFatigue system, three online datasets were used. Compare to state-of-the-art DDD system, the HybridFatigue system is outperformed. On average, the HybridFatigue system achieved 94.5% detection accuracy on 4250 images when tested on different subjects in the variable environment. The experimental results indicate that the HybridFatigue system can be utilized to decrease accidents.

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“…The second one is the Driver Drowsiness Detection system that collects data about the driver's behavior to identify if he or she is drowsy or not. More information about this system can be found in (Jabbar et al, 2018a;Jabbar et al, 2018b;Jabbar et al, 2019;Jabbar et al, 2020).…”

Section: The Perception Layermentioning

confidence: 99%

A Model-Based Testing Framework for Validating an IoT Solution for Blockchain-Based Vehicles Communication

Jabbar¹,

Krichen²,

Kharbeche³

et al. 2020

Preprint

Self Cite

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<div>The emergence of embedded and connected smart technologies, systems, and devices has enabled the concept of smart cities by connecting every ``thing'' to the Internet and in particular in transportation through the Internet of Vehicles (IoV). The main purpose of IoV is to prevent fatal crashes by resolving traffic and road safety problems. Nevertheless, it is paramount to ensure secure and accurate transmission and recording of data in ``Vehicle-to-Vehicle'' (V2V) and ``Vehicle-to-Infrastructure'' (V2I) communication. </div><div>To improve ``Vehicle-to-Everything'' (V2X) communication, this work uses Blockchain technology for developing a Blockchain-based IoT system aimed at establishing secure communication and developing a fully decentralized cloud computing platform.</div><div> Moreover, the authors propose a model-based framework to validate the proposed approach. This framework is mainly based on the use of the Attack Trees (AT) and timed automaton (TA) formalisms in order to test the functional, load and security aspects. An optimization phase for testers placement inspired by fog computing is proposed as well.</div>

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Real-time Driver Drowsiness Detection for Android Application Using Deep Neural Networks Techniques

Cited by 171 publications

References 7 publications

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

Assessment of the Potential of Wrist-Worn Wearable Sensors for Driver Drowsiness Detection

HybridFatigue: A Real-time Driver Drowsiness Detection using Hybrid Features and Transfer Learning

A Model-Based Testing Framework for Validating an IoT Solution for Blockchain-Based Vehicles Communication

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