Detection of Driver Drowsiness Using 3D Deep Neural Network and Semi-Supervised Gradient Boosting Machine

Huynh, Xuan-Phung; Park, Sang-Min; Kim, Yong-Guk

doi:10.1007/978-3-319-54526-4_10

Cited by 60 publications

(31 citation statements)

References 11 publications

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“…The suggested methodology has a certain advantage over other DD detection methods described in Section II. Particularly, as compared to the methods, where the behavioral and psychological attributes are applied [6][7][8][9][10][11][12][13][14][15][16][17][18], the proposed approach does not require additional devices, such as cameras and neuroscan systems. Those devices increase the system cost [8], what in its turn is a potential resistance for system application in a commercial passenger vehicle.…”

Section: Discussionmentioning

confidence: 99%

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Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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In addition to vehicle control, drivers often perform secondary tasks that impede driving. Reduction of driver distraction is an important challenge for the safety of intelligent transportation systems. In this paper, a methodology for detection and evaluation of driver distraction while performing secondary tasks is described and an appropriate hardware and software environment is offered and studied. The system includes a model of normal driving, a subsystem for measuring the errors from the secondary tasks, and a module for total distraction evaluation. A new machine learning algorithm defines driver performance in lane keeping and speed maintenance on a specific road segment. To recognize the errors, a method is proposed, which compares normal driving parameters with ones obtained while conducting a secondary task. To evaluate distraction, an effective fuzzy logic algorithm is used. To verify the proposed approach, a case study with driver-in-the-loop experiments was carried out, in which participants performed the secondary task, namely chatting on a cell phone. The results presented in this research confirm its capability to detect and to precisely measure a level of abnormal driver performance.

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Section: Discussionmentioning

confidence: 99%

“…The gaze direction and the head orientation are the most popular input attributes [8]. Artificial neural network (NN) and gradient boosting machine combination were proposed in [9]. The glance region prediction algorithm was designed using random forest classifier in [10] and convolutional NN -in [11].…”

Section: Related Work and Problem Statementmentioning

confidence: 99%

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Aksjonov

Nedoma

Vodovozov

et al. 2019

IEEE Trans. Intell. Transport. Syst.

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“…The results show that the Bayesian procedure parameterized Xgboost outperformed Random Forest and SVM did better for larger sample sizes . Xuan-Phung Huynh et.al [3] proposed an approach for identifying drowsiness of the driver by examining facial manners like nodding, eye-closure and yawning. They used 3D Convolution Neural Network to pull out features in spatial-temporal domain, and for drowsiness categorization, they used gradient boosting .They applied semi-supervised learning to improve overall performance.…”

Section: Related Workmentioning

confidence: 99%

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Murty¹,

Kumar²

2019

IJEAT

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Machine learning is used extensively in medical diagnosis to predict the existence of diseases. Existing classification algorithms are frequently used for automatic detection of diseases. But most of the times, they do not give 100% accurate results. Boosting techniques are often used in Machine learning to get maximum classification accuracy. Though several boosting techniques are in place but the XGBoost algorithm is doing extremely well for some selected data sets. Building an XGBoost model is simple but improving the model by tuning the parameters is a challenging task. There are many parameters to the XGBoost algorithm and deciding what set of parameters to tune and the ideal values of these parameters is a cumbersome and time taking task. We, in this paper, tuned the XGBoost model for the first time for Liver disease prediction and got 99% accuracy by tuning some of the hyper parameters. It is observed that the model proposed by us exhibited highest classification accuracy compared to all other models built till now by machine learning researchers and some regularly used algorithms like Support Vector Machines (SVM), Naive Bayes (NB), C4.5 Decision tree, Random Belief Networks, Alternating Decision Trees (ADT) experimented by us.

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“…Based on our previous work [3] in the same area of drowsiness detection with deep learning, in the current paper, we use another model of Recurrent Neural Networks (RNN) that we will detail in the following sections-in particular that these models are well-performing and powerful at the calculation level [4][5][6], which allows us to save time in the training process because this step is often costly in time and money. For this, we used the dataset Drowsy Driver Detection (NTHU-DDD) [7] used in the work [8].…”

Section: Introductionmentioning

confidence: 99%

Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

2020

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In recent years, the rise of car accident fatalities has grown significantly around the world. Hence, road security has become a global concern and a challenging problem that needs to be solved. The deaths caused by road accidents are still increasing and currently viewed as a significant general medical issue. The most recent developments have made in advancing knowledge and scientific capacities of vehicles, enabling them to see and examine street situations to counteract mishaps and secure travelers. Therefore, the analysis of driver’s behaviors on the road has become one of the leading research subjects in recent years, particularly drowsiness, as it grants the most elevated factor of mishaps and is the primary source of death on roads. This paper presents a way to analyze and anticipate driver drowsiness by applying a Recurrent Neural Network over a sequence frame driver’s face. We used a dataset to shape and approve our model and implemented repetitive neural network architecture multi-layer model-based 3D Convolutional Networks to detect driver drowsiness. After a training session, we obtained a promising accuracy that approaches a 92% acceptance rate, which made it possible to develop a real-time driver monitoring system to reduce road accidents.

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Detection of Driver Drowsiness Using 3D Deep Neural Network and Semi-Supervised Gradient Boosting Machine

Cited by 60 publications

References 11 publications

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Detection and Evaluation of Driver Distraction Using Machine Learning and Fuzzy Logic

Accurate Liver Disease Prediction with Extreme Gradient Boosting

Real-Time System for Driver Fatigue Detection Based on a Recurrent Neuronal Network

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