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

Monagi H. Alkinani, Wazir Zada Khan, Quratulain Arshad
2020 IEEE Access  
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  ...  INDEX TERMS Deep learning, human inattentive driving behavior, connected vehicles, road accident avoidance, abnormal behavior detection, distraction or aggressiveness detection, fatigue or drowsiness detection  ...  ACKNOWLEDGMENT The authors acknowledge the technical and financial support of University of Jeddah.  ... 
doi:10.1109/access.2020.2999829 fatcat:5nxtzm6yfbe4jf6nqgreqw45r4

E2DR: A Deep Learning Ensemble-Based Driver Distraction Detection with Recommendations Model

Mustafa Aljasim, Rasha Kashef
2022 Sensors  
In this paper, we have implemented a portfolio of various ensemble deep learning models that have been proven to efficiently classify driver distracted actions and provide an in-car recommendation to minimize  ...  This paper proposes E2DR, a new scalable model that uses stacking ensemble methods to combine two or more deep learning models to improve accuracy, enhance generalization, and reduce overfitting, with  ...  Data Availability Statement: The State Farm Distracted Drivers dataset can be accessed in [47] . Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22051858 pmid:35271004 pmcid:PMC8914716 fatcat:ju7sp4erdzbcrbipqmhco7wf4q

Robust Deep Learning-based Driver Distraction Detection and Classification

Amal Ezzouhri, Zakaria Charouh, Mounir Ghogho, Zouhair Guennoun
2021 IEEE Access  
In this paper, we propose a robust driver distraction detection system that extracts the driver's state from the recordings of an onboard camera using Deep Learning.  ...  The main feature of the proposed solution is the extraction of the driver's body parts, using deep learning-based segmentation, before performing the distraction detection and classification task.  ...  This work is partly funded by the National Agency for Road Safety (NARSA) of the Moroccan Ministry of Equipment, Transport, Logistics and Water, via the National Center for Scientific and Technical Research  ... 
doi:10.1109/access.2021.3133797 fatcat:vr3sfrgkpjejhdtkcjuihaylse

Driver Activity Recognition for Intelligent Vehicles: A Deep Learning Approach

Yang Xing, Chen Lv, Huaji Wang, Dongpu Cao, Efstathios Velenis, Fei-Yue Wang
2019 IEEE Transactions on Vehicular Technology  
The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection.  ...  The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analysed and discussed.  ...  Driver Distraction Detection using Binary Classifier In this section, the three CNN models are modified and trained to detect whether the driver is distracted or not.  ... 
doi:10.1109/tvt.2019.2908425 fatcat:gaqpq3rvrbdtrdceffxdav6dv4

Driver Drowsiness Detection System

Manjunath S, Banashree P, Shreya M, Sneha Manjunath Hegde, Nischal H P
2022 International Journal for Research in Applied Science and Engineering Technology  
Abstract: Recently, in addition to autonomous vehicle technology research and development, machine learning methods have been used to predict a driver's condition and emotions in order to provide information  ...  Recent developments in video processing using machine learning have enabled images obtained from cameras to be analysed with high accuracy.  ...  Sahari Real-time Detection of Distracted Driving based on Deep Learning, Weihua Sheng,Duy Tran,Ha Do,he Bai Distracted Driver Classification Using Deep Learning, Munif Alotaibi, Bandar Alotaibi November  ... 
doi:10.22214/ijraset.2022.42109 fatcat:tczyrxfcsjfnbmnyvbwuqdoxui

A Systematic Literature Review of Driver Inattention Monitoring Systems for Smart Car

Abdelfettah Soultana, Faouzia Benabbou, Nawal Sael, Sara Ouahabi
2022 International Journal of Interactive Mobile Technologies  
In particular, the present study deals with different aspects of prior studies such as the sensors used; the types of data, the feature engineering techniques, the machine-learning techniques applied and  ...  detection with both forms of fatigue and distraction.  ...  Deep learning models, such as CNN, LSTM, BILSTM, and MTCNN, are used to detect driver fatigue, as well as transfer learning, which is based on AlexNet and YOLO-V3.  ... 
doi:10.3991/ijim.v16i16.33075 fatcat:h2wwrxw2yrdbvpivq7gla6x6mu

HSDDD: A Hybrid Scheme for the Detection of Distracted Driving through Fusion of Deep Learning and Handcrafted Features

Monagi H. Alkinani, Wazir Zada Khan, Quratulain Arshad, Mudassar Raza
2022 Sensors  
Traditional methods for behavior detection of distracted drivers are not capable of capturing driver behavior features related to complex temporal features.  ...  We first obtain HOG features by using handcrafted algorithms, and then at the coordination tier, we leverage four deep CNN models including AlexNet, Inception V3, Resnet50 and VGG-16 for extracting DCNN  ...  Acknowledgments: The authors would like to thank and appreciate Hashim M. Eraqi for providing Distracted driving dataset.  ... 
doi:10.3390/s22051864 pmid:35271011 pmcid:PMC8914727 fatcat:mzohgfdvajhcbmqnlovv26qfny

Deep CNN models for Driver Activity Recognition for Intelligent Vehicles

2020 International Journal of Emerging Trends in Engineering Research  
Additionally, we propose a deep learning-based accuracy Achieved by the binary detection rate of 91.4 percent.  ...  The Gaussian Mixture Model (GMM) will be used as an input to the proposed model in handling the images like segmentation.CNN models are prepared for the function of binary detection and determine whether  ...  Create the Carnetsoft driving simulator to collect driving data and they identify 10 distracted driving patterns, not distracted, using different CNN architectures.  ... 
doi:10.30534/ijeter/2020/828102020 fatcat:z4pwmyv7kzdlxkuiye7w4khyce

Drive-Net: Convolutional Network for Driver Distraction Detection

Mohammed S. Majdi, Sundaresh Ram, Jonathan T. Gill, Jeffrey J. Rodriguez
2018 2018 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI)  
In this paper, we present an automated supervised learning method called Drive-Net for driver distraction detection.  ...  Drive-Net uses a combination of a convolutional neural network (CNN) and a random decision forest for classifying images of a driver.  ...  With this in mind, we propose a new supervised learning algorithm called Drive-Net that combines a CNN and a random forest in a cascaded fashion for application to the problem of driver distraction detection  ... 
doi:10.1109/ssiai.2018.8470309 dblp:conf/ssiai/MajdiRGR18 fatcat:fk2ggl3we5hozf7j2rj7aydvhm

Distracted Driver Detection with Deep Convolutional Neural Network

2019 International journal of recent technology and engineering  
Motivated by recent advancement of deep learning and computer vision in predicting drivers' behaviour, this paper attempts to investigate the optimal deep learning network architecture to accurately detect  ...  distracted drivers over visual feed.  ...  Deep learning is employed to classify the images taken by the webcam and determine whether or not the driver is distracted. II.  ... 
doi:10.35940/ijrte.d5131.118419 fatcat:lwo767qbsnfldkn2v6zkkpc6uy

Pose Estimation of Driver's Head Panning Based on Interpolation and Motion Vectors under a Boosting Framework

Syed Farooq Ali, Ahmed Sohail Aslam, Mazhar Javed Awan, Awais Yasin, Robertas Damaševičius
2021 Applied Sciences  
These features are calculated using facial landmark detection algorithms, including the Active Shape Model (ASM) and Boosted Regression with Markov Networks (BoRMaN).  ...  The study proposes novel geometric and spatial scale-invariant features under a boosting framework for detecting a driver's distraction due to the driver's head panning.  ...  Distracted driver detection: Deep learning vs handcrafted features. Electron. Imaging 2017, 2017, 20–26. [CrossRef] 62. Peng, J.; Shao, Y.  ... 
doi:10.3390/app112411600 fatcat:kmxuwox3h5g4dexhv7nl7beoqe

Context-Aware Driver Distraction Severity Classification using LSTM Network

Adebamigbe Fasanmade, Suleiman Aliyu, Ying He, Ali H. Al-Bayatti, Mhd Saeed Sharif, Ahmed S. Alfakeeh
2019 2019 International Conference on Computing, Electronics & Communications Engineering (iCCECE)  
In this paper, we present a deep learning technique that classifies drivers' distraction behaviour using three contextual awareness parameters: speed, manoeuver and event type.  ...  Advanced Driving Assistance Systems (ADAS) has been a critical component in vehicles and vital to the safety of vehicle drivers and public road transportation systems.  ...  Future work will consider developing a multi-level CNN-based driver detection model and combine this with contextual data to automate the detection of the driver's distraction and classify severity using  ... 
doi:10.1109/iccece46942.2019.8941966 fatcat:5vjzlznxwrfrpllebhjndlbr4m

Real-time detection of distracted driving based on deep learning

Duy Tran, Ha Manh Do, Weihua Sheng, He Bai, Girish Chowdhary
2018 IET Intelligent Transport Systems  
According to the World Health Organization the most common cause behind these accidents is driver's distraction and in many cases is caused by the use of a mobile phone.  ...  An attempt to develop a system for detecting distracted drivers and warn the responsible person against it was done.  ...  Le et al. using the dataset above, achieved higher accuracy that is, 94.2% using the Faster-RCNN [33] deep learning model.  ... 
doi:10.1049/iet-its.2018.5172 fatcat:ee7y772wl5eqhidwjuqf3ugt7e

A Hybrid Driver Fatigue and Distraction Detection Model Using AlexNet Based on Facial Features

Salma Anber, Wafaa Alsaggaf, Wafaa Shalash
2022 Electronics  
The newly trained model was able to predict drivers' drowsiness behaviors. The second approach is the use of AlexNet to extract features by training the top layers of the network.  ...  For this reason, we propose and compare two AlexNet CNN-based models to detect drivers' fatigue behaviors, relying on head position and mouth movements as behavioral measures.  ...  We also combined fatigue and distraction detection in one model.  ... 
doi:10.3390/electronics11020285 fatcat:wsxqadnvf5axvmcmecghfvkrxq

An Efficient Deep Learning Framework for Distracted Driver Detection

Faiqa Sajid, Abdul Rehman Javed, Asma Basharat, Natalia Kryvinska, Adil Afzal, Muhammad Rizwan
2021 IEEE Access  
In contrast, the EfficientDet model detects the objects involved in these distracting activities and the region of interest of the body parts from the images to make predictions strong and accomplish state-of-art  ...  As per the national highway traffic safety administration's investigation, 45% of vehicle crashes are done by a distracted driver right around each.  ...  In this paper, we use deep learning to detect and identify the distractions of a driver. A camera mounted above the dashboard captures RGB images.  ... 
doi:10.1109/access.2021.3138137 fatcat:mwcvkmyrk5hejlvsyzx5zm5vne
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