Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches

Kumar, Vaegae Naveen; Pulluri, Kranthi Kumar; Bagadi, Kala Praveen; Oyerinde, Olutayo O.

doi:10.1109/access.2022.3218711

Cited by 20 publications

(12 citation statements)

References 17 publications

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“…Nasari et al [17] proposed DistractNet, which achieved an accuracy of 99.32%. Vaegae et al [18] developed a real-time distracted driver detection framework using Residual Network (ResNet-50), which outperformed VGG-16 with an accuracy of 87.92.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed CNN model is not yet implemented in embedded systems to monitor driver states in real-time, raining time depends on many factors such as the number of image datasets, network architecture, and processing platform performances. [18] Combining two or more architectures and including face-based approaches helps to solve the misclassification problem.…”

Section: Refmentioning

confidence: 99%

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Detecting Abnormal Driving Behavior Using Modified DenseNet

Ayad,

Abdulmunim

2023

ijcsm

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Car accidents have serious consequences, including depletion of resources, harm to human health and well-being, and social problems. The three primary factors contributing to car accidents are driver error, external factors, and vehicle-related factors. The main objective of this paper is to address the issue of car accidents caused by driver error. To achieve this goal, a solution is proposed in the form of a modified version of the Dense model, called the 1Dimention-DenseNet, specifically designed to detect abnormal driving behavior. The model incorporates adapted dense blocks and transition layers, which enable it to accurately identify patterns indicative of abnormal driving behavior. This paper compares the performance of the 1D-DenseNet model to the original DenseNet model in detecting abnormal driving behavior in the Kaggle distracted driver behavior dataset. Results show that the 1D-DenseNet model outperforms the original DenseNet model in classification and validation accuracies, loss, and overhead. The 1D-DenseNet, after 100 epochs of training using Keras on top of TensorFlow, the 1D-DenseNet achieved a categorical cross-entropy loss of 0.19 on the validation set, with classification and validation accuracies of 99.80% and 99.96%, respectively. These findings demonstrate the effectiveness of the 1D-DenseNet model in improving the detection of abnormal driving behavior.

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Section: Related Workmentioning

confidence: 99%

Section: Refmentioning

confidence: 99%

Detecting Abnormal Driving Behavior Using Modified DenseNet

Ayad,

Abdulmunim

2023

ijcsm

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“…A Deep Learning approach was presented in [52][53][54][55] to address this problem, where the authors used a near-infrared (NIR) camera sensor to detect glances, as well as head and eye movements, without the need for user calibration at first. The proposed system was evaluated on a dedicated database, as well as on Columbia's open dataset (The Face Tracer CAVE-DB database).…”

Section: Of 19mentioning

confidence: 99%

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

Doniec¹,

Konior²,

Sieciński³

et al. 2023

Preprint

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To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring the cognitive capabilities of drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to primary activities in driving, including crossroad, parking, roundabout was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93–0.94). Furthermore, using the same algorithm, it was possible to distinguish four secondary activities related to activities of daily life and secondary when driving a car.

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“…Professors from India and South Africa developed a system that can detect the specific task the driver is performing while driving, such as eating food, drinking, or talking to the passenger [8]. This paper utilizes very advanced technologies, such as VGG-16 and ResNot-50.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Device to Analyze and Detect Driver Alertness using Media Pipe and Raspberry Pi

Feng,

Sahagun

2023

Advances in Computing &Amp; Information Technologies

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Distracted driving is an important problem that plagues the world, and is the cause of thousands of deaths in the United States. Distracted driving has cost an estimate of $44.2 billion to the US in damages. A way I can help solve this problem is creating a device that can detect if the driver is distracted. This device contains an Arducam Day n Night 1080p camera, a Raspberry Pi 3, and a buzzer. The device utilizes python and Mediapipe, a library which can locate important landmarks on the driver’s face [1]. During development, I had to face many challenges such as the driver possibly looking down subtlety with their eyes, or the camera being positioned in a weird way. During experimentation, I tested the delay, and accuracy of the device. After 35 trials, the device is accurate around 94%, and the delay is around 9 seconds. Overall, this device can be used by everyone who wants to be kept responsible and alert while driving.

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Design of an Efficient Distracted Driver Detection System: Deep Learning Approaches

Cited by 20 publications

References 17 publications

Detecting Abnormal Driving Behavior Using Modified DenseNet

Detecting Abnormal Driving Behavior Using Modified DenseNet

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

An Intelligent Device to Analyze and Detect Driver Alertness using Media Pipe and Raspberry Pi

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