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Driving events; driving safety; fuzzy logic; hidden Markov models (HMMs)
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Biomedical monitoring
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automobiles
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Kalman filters
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1410
driver information systems
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alarm systems
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Estimation
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Context modeling
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Hidden Markov models
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Cameras
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Global navigation satellite system
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Biomedical monitoring
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Bayes methods
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Cyber(45), No. 12, December 2015, pp. 2780-2791.
IEEE DOI
1512
Monitoring applied to pilots.
BibRef
Artan, Y.[Yusuf],
Bulan, O.[Orhan],
Loce, R.P.[Robert P.],
Paul, P.[Peter],
Passenger Compartment Violation Detection in HOV/HOT Lanes,
ITS(17), No. 2, February 2016, pp. 395-405.
IEEE DOI
1602
BibRef
Earlier:
Driver Cell Phone Usage Detection from HOV/HOT NIR Images,
PBVS14(225-230)
IEEE DOI
1409
Automotive components.
cell phone usage detection;image classification
BibRef
Wshah, S.,
Xu, B.,
Bulan, O.,
Kumar, J.,
Paul, P.,
Deep learning architectures for domain adaptation in HOV/HOT lane
enforcement,
WACV16(1-7)
IEEE DOI
1606
Cameras
BibRef
Li, N.,
Busso, C.,
Detecting Drivers' Mirror: Checking Actions and Its Application to
Maneuver and Secondary Task Recognition,
ITS(17), No. 4, April 2016, pp. 980-992.
IEEE DOI
1604
Feature extraction
BibRef
Fridman, L.,
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Driver Gaze Region Estimation without Use of Eye Movement,
IEEE_Int_Sys(31), No. 3, May 2016, pp. 49-56.
IEEE DOI
1606
Feature extraction
BibRef
Fridman, L.,
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'Owl' and 'Lizard':
Patterns of head pose and eye pose in driver gaze classification,
IET-CV(10), No. 4, 2016, pp. 308-313.
DOI Link
1608
driver information systems
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Deng, T.,
Yang, K.,
Li, Y.,
Yan, H.,
Where Does the Driver Look? Top-Down-Based Saliency Detection in a
Traffic Driving Environment,
ITS(17), No. 7, July 2016, pp. 2051-2062.
IEEE DOI
1608
intelligent transportation systems
BibRef
Lundgren, M.,
Hammarstrand, L.,
McKelvey, T.,
Driver-Gaze Zone Estimation Using Bayesian Filtering and Gaussian
Processes,
ITS(17), No. 10, October 2016, pp. 2739-2750.
IEEE DOI
1610
Cameras
BibRef
Aghaei, A.S.,
Donmez, B.,
Liu, C.C.,
He, D.,
Liu, G.,
Plataniotis, K.N.,
Chen, H.Y.W.,
Sojoudi, Z.,
Smart Driver Monitoring: When Signal Processing Meets Human Factors:
In the driver's seat,
SPMag(33), No. 6, November 2016, pp. 35-48.
IEEE DOI
1609
BibRef
And:
Errata:
SPMag(34), No. 1, January 2017, pp. 93-93.
IEEE DOI
1702
Computer crashes
BibRef
Choi, Y.,
Han, S.I.,
Kong, S.H.,
Ko, H.,
Driver Status Monitoring Systems for Smart Vehicles Using
Physiological Sensors: A safety enhancement system from automobile
manufacturers,
SPMag(33), No. 6, November 2016, pp. 22-34.
IEEE DOI
1609
Accidents
BibRef
Perrett, T.,
Mirmehdi, M.,
Dias, E.,
Visual Monitoring of Driver and Passenger Control Panel Interactions,
ITS(18), No. 2, February 2017, pp. 321-331.
IEEE DOI
1702
Cameras
BibRef
Le, T.H.N.[T. Hoang Ngan],
Zhu, C.C.[Chen-Chen],
Zheng, Y.T.[Yu-Tong],
Luu, K.[Khoa],
Savvides, M.[Marios],
DeepSafeDrive: A grammar-aware driver parsing approach to Driver
Behavioral Situational Awareness (DB-SAW),
PR(66), No. 1, 2017, pp. 229-238.
Elsevier DOI
1704
Driver parsing
BibRef
Zhang, T.,
Fruchter, R.,
Frank, M.,
Are They Paying Attention? A Model-Based Method to Identify
Individuals' Mental States,
Computer(50), No. 3, March 2017, pp. 40-49.
IEEE DOI
1704
Brain modeling
BibRef
Rohit, F.[Fnu],
Kulathumani, V.[Vinod],
Kavi, R.[Rahul],
Elwarfalli, I.[Ibrahim],
Kecojevic, V.[Vlad],
Nimbarte, A.[Ashish],
Real-time drowsiness detection using wearable, lightweight brain
sensing headbands,
IET-ITS(11), No. 5, June 2017, pp. 255-263.
DOI Link
1705
BibRef
Li, G.,
Chung, W.Y.,
Combined EEG-Gyroscope-tDCS Brain Machine Interface System for Early
Management of Driver Drowsiness,
HMS(48), No. 1, February 2018, pp. 50-62.
IEEE DOI
1801
Brain-Computer Interface. Biomedical monitoring, Brain-computer interfaces, Electrodes,
Electroencephalography, Gyroscopes, Sensors, Vehicles,
transcranial direct current stimulation (tDCS)
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Zhao, L.[Lei],
Wang, Z.C.[Zeng-Cai],
Wang, X.J.[Xiao-Jin],
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Driver drowsiness detection using facial dynamic fusion information and
a DBN,
IET-ITS(12), No. 2, March 2018, pp. 127-133.
DOI Link
1801
BibRef
Jeong, M.,
Ko, B.C.,
Kwak, S.,
Nam, J.,
Driver Facial Landmark Detection in Real Driving Situations,
CirSysVideo(28), No. 10, October 2018, pp. 2753-2767.
IEEE DOI
1811
Shape, Face, Cameras, Vehicles, Monitoring, Facial landmark detection,
weighted random forest regressor, RANSAC, global face model,
real driving situation
BibRef
Zhang, C.F.[Chao-Fei],
Wang, W.J.[Wen-Jun],
Chen, C.Y.[Chao-Yang],
Zeng, C.[Chao],
Anderson, D.E.[Dennis E.],
Cheng, B.[Bo],
Determination of optimal electroencephalography recording locations for
detecting drowsy driving,
IET-ITS(12), No. 5, June 2018, pp. 345-350.
DOI Link
1805
BibRef
Lin, L.[Li],
Guo, G.[Gang],
Xu, N.[Na],
User-perceived styling experience of smart vehicles:
A method to combine eye tracking with semantic differences,
IET-ITS(13), No. 1, January 2019, pp. 72-78.
DOI Link
1901
BibRef
Li, Z.P.[Zheng-Ping],
Zhang, K.[Kai],
Chen, B.K.[Bo-Kui],
Dong, Y.H.[Yu-Han],
Zhang, L.[Lin],
Driver identification in intelligent vehicle systems using machine
learning algorithms,
IET-ITS(13), No. 1, January 2019, pp. 40-47.
DOI Link
1901
BibRef
Mårtensson, H.,
Keelan, O.,
Ahlström, C.,
Driver Sleepiness Classification Based on Physiological Data and
Driving Performance From Real Road Driving,
ITS(20), No. 2, February 2019, pp. 421-430.
IEEE DOI
1902
Sleep, Feature extraction, Vehicles, Electroencephalography,
Electrocardiography, Roads, Electrooculography, Driver sleepiness,
real driving
BibRef
Silveira, C.S.[Cláudia Sofia],
Cardoso, J.S.[Jaime S.],
Lourenço, A.L.[André L.],
Ahlström, C.[Christer],
Importance of subject-dependent classification and imbalanced
distributions in driver sleepiness detection in realistic conditions,
IET-ITS(13), No. 2, February 2019, pp. 347-355.
DOI Link
1902
BibRef
Ulhaq, A.,
He, J.,
Zhang, Y.,
Deep actionlet proposals for driver's behavior monitoring,
IVCNZ17(1-6)
IEEE DOI
1902
behavioural sciences computing, cameras,
driver information systems, face recognition, feature extraction,
Discrete Fourier transforms
BibRef
Palazzi, A.[Andrea],
Abati, D.[Davide],
Calderara, S.[Simone],
Solera, F.,
Cucchiara, R.[Rita],
Predicting the Driver's Focus of Attention: The DR(eye)VE Project,
PAMI(41), No. 7, July 2019, pp. 1720-1733.
IEEE DOI
1906
Task analysis, Vehicles, Visualization, Computational modeling,
Semantics, Cameras, Predictive models, Focus of attention,
gaze prediction
BibRef
Alletto, S.,
Palazzi, A.[Andrea],
Solera, F.,
Calderara, S.[Simone],
Cucchiara, R.[Rita],
DR(eye)VE: A Dataset for Attention-Based Tasks with Applications to
Autonomous and Assisted Driving,
CVVT16(54-60)
IEEE DOI
1612
BibRef
Morando, A.,
Victor, T.,
Dozza, M.,
A Reference Model for Driver Attention in Automation: Glance Behavior
Changes During Lateral and Longitudinal Assistance,
ITS(20), No. 8, August 2019, pp. 2999-3009.
IEEE DOI
1908
Measurement, Automation, Visualization, Roads, Automobiles, Manuals,
ADAS, attention, eye tracker, glance distribution,
visual behavior
BibRef
Tivesten, E.[Emma],
Victor, T.W.[Trent W.],
Gustavsson, P.[Pär],
Johansson, J.[Joel],
Aust, M.L.[Mikael Ljung],
Out-of-the-loop crash prediction: the automation expectation mismatch
(AEM) algorithm,
IET-ITS(13), No. 8, August 2019, pp. 1231-1240.
DOI Link
1908
BibRef
Wörle, J.[Johanna],
Metz, B.[Barbara],
Thiele, C.[Christian],
Weller, G.[Gert],
Detecting sleep in drivers during highly automated driving: the
potential of physiological parameters,
IET-ITS(13), No. 8, August 2019, pp. 1241-1248.
DOI Link
1908
BibRef
Wang, L.[Leye],
Geng, X.[Xu],
Ma, X.J.[Xiao-Juan],
Zhang, D.[Daqing],
Yang, Q.A.[Qi-Ang],
Ridesharing car detection by transfer learning,
AI(273), 2019, pp. 1-18.
Elsevier DOI
1908
Transfer learning, Co-training, Trajectory mining
BibRef
Yu, J.M.[Jong-Min],
Park, S.[Sangwoo],
Lee, S.W.[Sang-Wook],
Jeon, M.[Moongu],
Driver Drowsiness Detection Using Condition-Adaptive Representation
Learning Framework,
ITS(20), No. 11, November 2019, pp. 4206-4218.
IEEE DOI
1911
Feature extraction, Visualization, Vehicle crash testing, Sensors,
Adaptation models, Automobiles, Representation learning,
driver drowsiness detection
BibRef
Vu, T.H.[Toan H.],
Dang, A.[An],
Wang, J.C.[Jia-Ching],
A Deep Neural Network for Real-Time Driver Drowsiness Detection,
IEICE(E102-D), No. 12, December 2019, pp. 2637-2641.
WWW Link.
1912
BibRef
Chiou, C.,
Wang, W.,
Lu, S.,
Huang, C.,
Chung, P.,
Lai, Y.,
Driver Monitoring Using Sparse Representation With Part-Based
Temporal Face Descriptors,
ITS(21), No. 1, January 2020, pp. 346-361.
IEEE DOI
2001
Vehicles, Face, Monitoring, Mouth, Accidents, Computational modeling,
Advanced driver assistance systems, driver monitoring systems,
temporal face descriptors
BibRef
Borghi, G.[Guido],
Fabbri, M.,
Vezzani, R.[Roberto],
Cucchiara, R.[Rita],
Cucchiara, R.,
Face-from-Depth for Head Pose Estimation on Depth Images,
PAMI(42), No. 3, March 2020, pp. 596-609.
IEEE DOI
2002
Pose estimation, Face, Magnetic heads,
Task analysis, Lighting, Head pose estimation,
CNNs
BibRef
Borghi, G.[Guido],
Venturelli, M.[Marco],
Vezzani, R.[Roberto],
Cucchiara, R.[Rita],
POSEidon: Face-from-Depth for Driver Pose Estimation,
CVPR17(5494-5503)
IEEE DOI
1711
BibRef
Earlier: A2, A1, A3, A4:
Deep Head Pose Estimation from Depth Data for In-Car Automotive
Applications,
UHA3DS16(74-85).
Springer DOI
1806
Face, Neural networks, Pose estimation, Vehicles.
BibRef
Frigieri, E.[Elia],
Borghi, G.[Guido],
Vezzani, R.[Roberto],
Cucchiara, R.[Rita],
Fast and Accurate Facial Landmark Localization in Depth Images for
In-Car Applications,
CIAP17(I:539-549).
Springer DOI
1711
BibRef
Nishiyuki, K.[Kenta],
Shiau, J.Y.[Jia-Yau],
Nagae, S.[Shigenori],
Yabuuchi, T.[Tomohiro],
Kinoshita, K.[Koichi],
Hasegawa, Y.[Yuki],
Yamashita, T.[Takayoshi],
Fujiyoshi, H.[Hironobu],
Driver Drowsiness Estimation by Parallel Linked Time-Domain CNN with
Novel Temporal Measures on Eye States,
IEICE(E103-D), No. 6, June 2020, pp. 1276-1286.
WWW Link.
2006
BibRef
Wang, X.Y.[Xiao-Yuan],
Guo, Y.Q.[Yong-Qing],
Ban, J.[Jeff],
Xu, Q.[Qing],
Bai, C.L.[Cheng-Lin],
Liu, S.L.[Shan-Liang],
Driver emotion recognition of multiple-ECG feature fusion based on BP
network and D-S evidence,
IET-ITS(14), No. 8, August 2020, pp. 815-824.
DOI Link
2007
BibRef
Yang, L.,
Dong, K.,
Dmitruk, A.J.,
Brighton, J.,
Zhao, Y.,
A Dual-Cameras-Based Driver Gaze Mapping System With an Application
on Non-Driving Activities Monitoring,
ITS(21), No. 10, October 2020, pp. 4318-4327.
IEEE DOI
2010
Vehicles, Cameras, Feature extraction, Face, Monitoring, Visualization,
Gaze tracking, Driver attention evaluation, Level 3 automation,
heat map
BibRef
Yang, L.C.[Li-Chao],
Dong, K.[Kuo],
Ding, Y.[Yan],
Brighton, J.[James],
Zhan, Z.F.[Zhen-Fei],
Zhao, Y.F.[Yi-Fan],
Recognition of visual-related non-driving activities using a
dual-camera monitoring system,
PR(116), 2021, pp. 107955.
Elsevier DOI
2106
Driver behaviour, Level 3 automation,
Non-driving related task, activities identification
BibRef
Yin, J.L.,
Chen, B.H.,
Lai, K.H.R.,
Driver Danger-Level Monitoring System Using Multi-Sourced Big Driving
Data,
ITS(21), No. 12, December 2020, pp. 5271-5282.
IEEE DOI
2012
Vehicles, Feature extraction, Biomedical monitoring, Monitoring,
Roads, Analytical models, Collaboration, Danger-level analysis,
multi-sourced driving data
BibRef
Hu, Y.,
Lu, M.,
Xie, C.,
Lu, X.,
Driver Drowsiness Recognition via 3D Conditional GAN and Two-Level
Attention Bi-LSTM,
CirSysVideo(30), No. 12, December 2020, pp. 4755-4768.
IEEE DOI
2012
Generative adversarial networks, Vehicles,
Feature extraction, bidirectional long short-term memory
BibRef
Yang, H.,
Liu, L.,
Min, W.,
Yang, X.,
Xiong, X.,
Driver Yawning Detection Based on Subtle Facial Action Recognition,
MultMed(23), 2021, pp. 572-583.
IEEE DOI
2102
convolutional neural nets,
deep learning (artificial intelligence), 3D deep learning network
BibRef
Jiang, Y.,
Zhang, Y.,
Lin, C.,
Wu, D.,
Lin, C.T.,
EEG-Based Driver Drowsiness Estimation Using an Online Multi-View and
Transfer TSK Fuzzy System,
ITS(22), No. 3, March 2021, pp. 1752-1764.
IEEE DOI
2103
Brain modeling, Electroencephalography, Estimation, Vehicles,
Fuzzy systems, Feature extraction, Accidents, Transfer learning,
EEG
BibRef
Persson, A.[Anna],
Jonasson, H.[Hanna],
Fredriksson, I.[Ingemar],
Wiklund, U.[Urban],
Ahlström, C.[Christer],
Heart Rate Variability for Classification of Alert Versus Sleep
Deprived Drivers in Real Road Driving Conditions,
ITS(22), No. 6, June 2021, pp. 3316-3325.
IEEE DOI
2106
Sleep, Heart rate variability, Vehicles, Feature extraction,
Measurement, Roads, Driver sleepiness, detection, classification,
heart rate variability
BibRef
Balam, V.P.[Venkata Phanikrishna],
Sameer, V.U.[Venkata Udaya],
Chinara, S.[Suchismitha],
Automated classification system for drowsiness detection using
convolutional neural network and electroencephalogram,
IET-ITS(15), No. 4, 2021, pp. 514-524.
DOI Link
2106
BibRef
Lv, K.[Kai],
Sheng, H.[Hao],
Xiong, Z.[Zhang],
Li, W.[Wei],
Zheng, L.[Liang],
Improving Driver Gaze Prediction With Reinforced Attention,
MultMed(23), 2021, pp. 4198-4207.
IEEE DOI
2112
Task analysis, Computational modeling, Vehicles, Predictive models,
Hidden Markov models, Semantics,
deep learning
BibRef
Zhou, F.[Feng],
Yang, X.J.[X. Jessie],
de Winter, J.C.F.[Joost C. F.],
Using Eye-Tracking Data to Predict Situation Awareness in Real Time
During Takeover Transitions in Conditionally Automated Driving,
ITS(23), No. 3, March 2022, pp. 2284-2295.
IEEE DOI
2203
Particle measurements, Atmospheric measurements, Vehicles,
Task analysis, Time measurement, Predictive models, explainability
BibRef
Hu, T.C.[Tian-Cheng],
Jha, S.[Sumit],
Busso, C.[Carlos],
Temporal Head Pose Estimation From Point Cloud in Naturalistic
Driving Conditions,
ITS(23), No. 7, July 2022, pp. 8063-8076.
IEEE DOI
2207
Feature extraction, Pose estimation, Magnetic heads, Vehicles,
Deep learning, Cameras, Driver head pose estimation, deep learning,
temporal modeling
BibRef
Huang, J.L.[Jian-Ling],
Long, Y.[Yan],
Zhao, X.H.[Xiao-Hua],
Driver Glance Behavior Modeling Based on Semi-Supervised Clustering
and Piecewise Aggregate Representation,
ITS(23), No. 7, July 2022, pp. 8396-8411.
IEEE DOI
2207
Vehicles, Mirrors, Hidden Markov models, Visualization,
Task analysis, Spatiotemporal phenomena, Automobiles,
two-dimensional dynamic time warping
BibRef
Qian, K.[Kun],
Koike, T.[Tomoya],
Nakamura, T.[Toru],
Schuller, B.W.[Björn W.],
Yamamoto, Y.[Yoshiharu],
Learning Multimodal Representations for Drowsiness Detection,
ITS(23), No. 8, August 2022, pp. 11539-11548.
IEEE DOI
2208
Brain modeling, Vehicles, Data models, Feature extraction,
Electroencephalography, Predictive models, Machine learning, deep learning
BibRef
Qin, L.[Long],
Shi, Y.[Yi],
He, Y.H.[Ya-Hui],
Zhang, J.R.[Jun-Rui],
Zhang, X.S.[Xian-Shi],
Li, Y.J.[Yong-Jie],
Deng, T.[Tao],
Yan, H.M.[Hong-Mei],
ID-YOLO: Real-Time Salient Object Detection Based on the Driver's
Fixation Region,
ITS(23), No. 9, September 2022, pp. 15898-15908.
IEEE DOI
2209
Vehicles, Object detection, Videos, Predictive models, Visualization,
Task analysis, Vehicle dynamics, Visual attention, eye tracking,
traffic driving
BibRef
Ahmed, M.[Muneeb],
Masood, S.[Sarfaraz],
Ahmad, M.[Musheer],
El-Latif, A.A.A.[Ahmed A. Abd],
Intelligent Driver Drowsiness Detection for Traffic Safety Based on
Multi CNN Deep Model and Facial Subsampling,
ITS(23), No. 10, October 2022, pp. 19743-19752.
IEEE DOI
2210
Brain modeling, Feature extraction, Computational modeling,
Vehicles, Convolution, Physiology, Kernel,
multitask cascaded convolutional networks (MTCNN)
BibRef
Baccour, M.H.[Mohamed Hedi],
Driewer, F.[Frauke],
Schäck, T.[Tim],
Kasneci, E.[Enkelejda],
Comparative Analysis of Vehicle-Based and Driver-Based Features for
Driver Drowsiness Monitoring by Support Vector Machines,
ITS(23), No. 12, December 2022, pp. 23164-23178.
IEEE DOI
2212
Vehicles, Monitoring, Behavioral sciences, Cameras, Safety,
Feature extraction, Biomedical monitoring, Drowsiness, road safety,
feature selection
BibRef
Mou, L.[Luntian],
Zhou, C.[Chao],
Xie, P.[Pengtao],
Zhao, P.F.[Peng-Fei],
Jain, R.[Ramesh],
Gao, W.[Wen],
Yin, B.C.[Bao-Cai],
Isotropic Self-Supervised Learning for Driver Drowsiness Detection
With Attention-Based Multimodal Fusion,
MultMed(25), 2023, pp. 529-542.
IEEE DOI
2302
Feature extraction, Convolutional neural networks, Videos,
Hidden Markov models, Vehicles, Dictionaries, multimodal fusion model
BibRef
Wörle, J.[Johanna],
Metz, B.[Barbara],
Prill, A.[Andy],
How to Induce Drowsiness When Testing Driver Drowsiness and Attention
Warning (DDAW) Systems,
ITS(24), No. 5, May 2023, pp. 4758-4764.
IEEE DOI
2305
Sleep, Vehicles, Testing, Regulation, Safety, Particle measurements,
Europe, Drowsiness detection, type approval, test procedure, EU regulation
BibRef
Lu, Y.S.[Yan-Sha],
Liu, C.S.[Chun-Sheng],
Chang, F.L.[Fa-Liang],
Liu, H.[Hui],
Huan, H.Q.[Heng-Qiang],
JHPFA-Net: Joint Head Pose and Facial Action Network for Driver
Yawning Detection Across Arbitrary Poses in Videos,
ITS(24), No. 11, November 2023, pp. 11850-11863.
IEEE DOI
2311
BibRef
Chougule, A.[Amit],
Shah, J.[Jash],
Chamola, V.[Vinay],
Kanhere, S.[Salil],
Enabling Safe ITS: EEG-Based Microsleep Detection in VANETs,
ITS(24), No. 12, December 2023, pp. 15773-15783.
IEEE DOI
2312
BibRef
Yang, L.[Lie],
Yang, H.H.[Hao-Han],
Wei, H.[Henglai],
Hu, Z.X.[Zhong-Xu],
Lv, C.[Chen],
Video-Based Driver Drowsiness Detection With Optimised Utilization of
Key Facial Features,
ITS(25), No. 7, July 2024, pp. 6938-6950.
IEEE DOI
2407
Feature extraction, Electroencephalography, Mouth, Vehicles,
Physiology, Interference, Facial features, local facial areas
BibRef
Gong, P.L.[Pei-Liang],
Wang, P.P.[Peng-Pai],
Zhou, Y.Y.[Yue-Ying],
Wen, X.[Xuyun],
Zhang, D.Q.[Dao-Qiang],
TFAC-Net: A Temporal-Frequential Attentional Convolutional Network
for Driver Drowsiness Recognition With Single-Channel EEG,
ITS(25), No. 7, July 2024, pp. 7004-7016.
IEEE DOI
2407
Electroencephalography, Feature extraction, Vehicles, Fatigue,
Task analysis, Deep learning, Convolutional neural networks,
attention mechanism
BibRef
Zemblys, R.[Raimondas],
Ahlström, C.[Christer],
Kircher, K.[Katja],
Finér, S.[Svitlana],
Practical aspects of measuring camera-based indicators of alcohol
intoxication in manual and automated driving,
IET-ITS(18), No. 8, 2024, pp. 1408-1427.
DOI Link
2408
advanced driver assistance systems, automotive engineering,
drugs, gaze tracking
BibRef
Camberg, S.[Simone],
Hüllermeier, E.[Eyke],
An Extensive Analysis of Different Approaches to Driver Gaze
Classification,
ITS(25), No. 11, November 2024, pp. 16435-16448.
IEEE DOI
2411
Vehicles, Cameras, Estimation, Magnetic heads, Feature extraction,
Accuracy, Task analysis, Driver monitoring systems, gaze estimation
BibRef
Balam, V.P.[Venkata Phanikrishna],
Systematic Review of Single-Channel EEG-Based Drowsiness Detection
Methods,
ITS(25), No. 11, November 2024, pp. 15210-15228.
IEEE DOI
2411
Electroencephalography, Feature extraction, Brain modeling,
Accuracy, Solid modeling, Sleep, Monitoring,
signal processing
BibRef
Zhang, J.M.[Jia-Ming],
Zhang, F.Z.[Fang-Zuo],
Wei, H.T.[Hong-Tao],
PSSS-EEG: A Probabilistic-masking Self-Supervised Swin-transformer
model for EEG-based drowsiness recognition,
PR(158), 2025, pp. 111005.
Elsevier DOI
2411
Electroencephalography (EEG), Drowsiness recognition,
Data augmentation, Transformer, Self-supervised learning
BibRef
Lu, W.[Wei],
Liu, W.J.[Wen-Jian],
EEG-based Drowsiness and Alertness Recognition Using Feature Fusion
and Deep Residual Networks,
CVIDL23(300-304)
IEEE DOI
2403
Time-frequency analysis, Road accidents, Sleep,
Computational modeling, Training data, Brain modeling,
Deep Residual Networks
BibRef
Urselmann, T.[Teun],
Cañas, P.N.[Paola Natalia],
Ortega, J.D.[Juan Diego],
Nieto, M.[Marcos],
Semi-automatic Pipeline for Large-scale Dataset Annotation Task:
A DMD Application,
InVehicle22(560-574).
Springer DOI
2304
Driver Monitoring Dataset.
BibRef
Tüfekci, G.[Gülin],
Kayabasi, A.[Alper],
Akagündüz, E.[Erdem],
Ulusoy, I.[Ilkay],
Detecting Driver Drowsiness as an Anomaly Using Lstm Autoencoders,
InVehicle22(549-559).
Springer DOI
2304
BibRef
Banerjee, S.[Sandipan],
Joshi, A.[Ajjen],
Turcot, J.[Jay],
Reimer, B.[Bryan],
Mishra, T.[Taniya],
Driver Glance Classification In-the-wild: Towards Generalization
Across Domains and Subjects,
FG21(1-8)
IEEE DOI
2303
Training, Visualization, Costs, Face recognition, Crops, Cameras, Data models
BibRef
Sharak, S.[Salem],
Das, K.[Kapotaksha],
Riani, K.[Kais],
Abouelenien, M.[Mohamed],
Burzo, M.[Mihai],
Mihalcea, R.[Rada],
Contact Versus Noncontact Detection of Driver's Drowsiness,
ICPR22(967-974)
IEEE DOI
2212
Visualization, Costs, Cameras, Hardware, Planning, Reliability
BibRef
Ghosh, S.[Shreya],
Dhall, A.[Abhinav],
Sharma, G.[Garima],
Gupta, S.[Sarthak],
Sebe, N.[Nicu],
Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver
Gaze Zone Estimation Dataset,
AVVision21(2896-2905)
IEEE DOI
2112
Pipelines, Lighting, Estimation,
Convolutional neural networks, Labeling
BibRef
Vijay, M.[Malaika],
Vinayak, N.N.[Nandagopal Netrakanti],
Nunna, M.[Maanvi],
Natarajan, S.[Subramanyam],
Real-Time Driver Drowsiness Detection using Facial Action Units,
ICPR21(10113-10119)
IEEE DOI
2105
Training, Scalability, Face recognition, Pipelines, Neural networks,
Mouth, Muscles, Facial Action Units, Transfer Learning, XGBoost, Convolutional Neural Networks
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Andriyanov, N.[Nikita],
First Step Towards Creating a Software Package for Detecting the
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IMTA20(314-322).
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Theagarajan, R.,
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DeepDriver:
Automated System For measuring Valence and Arousal in Car Driver Videos,
ICPR18(2546-2551)
IEEE DOI
1812
convolution, driver information systems, face recognition,
feature extraction, feedforward neural nets,
Logic gates
BibRef
Dehzangi, O.,
Taherisadr, M.,
Driver Distraction Detection using MEL Cepstrum Representation of
Galvanic Skin Responses and Convolutional Neural Networks*,
ICPR18(1481-1486)
IEEE DOI
1812
Feature extraction, Time-frequency analysis, Vehicles,
Skin, Task analysis, Cepstrum, component,
BibRef
Dehzangi, O.,
Masilamani, S.,
Unobtrusive Driver Drowsiness Prediction Using Driving Behavior from
Vehicular Sensors,
ICPR18(3598-3603)
IEEE DOI
1812
Vehicles, Feature extraction, Wheels, Sleep, Predictive models,
Sensors, Correlation, Driver Drowsiness, KSS, Correlation analysis,
Classification & Regression
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Tang, R.[Rui],
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1807
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Sleep Deprivation Detection for Real-Time Driver Monitoring Using Deep
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1807
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Stafford-Fraser, Q.,
Robinson, P.,
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Skrypchuk, L.,
Analysis of Yawning Behaviour in Spontaneous Expressions of Drowsy
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FG18(571-576)
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Face, Feature extraction, Histograms, Mouth, Task analysis, Vehicles,
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Schwarz, A.,
Haurilet, M.,
Martinez, M.,
Stiefelhagen, R.,
DriveAHead: A Large-Scale Driver Head Pose Dataset,
CVVT17(1165-1174)
IEEE DOI
1709
Glass, Head, Lighting, Magnetic heads, Pose estimation,
Vehicles
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Reddy, B.,
Kim, Y.H.,
Yun, S.,
Seo, C.,
Jang, J.,
Real-Time Driver Drowsiness Detection for Embedded System Using Model
Compression of Deep Neural Networks,
ECVW17(438-445)
IEEE DOI
1709
Brain modeling, Computational modeling, Face, Machine learning,
Mouth, Real-time systems, Vehicles, Deep Learning,
Driver Monitoring System, Drowsiness Detection,
Knowledge Distillation, Model Compression., Real-time, Deep, Neural, Network
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Martin, S.,
Trivedi, M.M.,
On looking at faces in an automobile: Issues, algorithms and
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1705
Detectors, Face detection, Head, Lighting, Pose estimation, Training, Vehicles
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Rangesh, A.,
Trivedi, M.M.,
HandyNet: A One-stop Solution to Detect, Segment, Localize &
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HumanPose18(1216-12167)
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1812
Vehicles, Automation, Task analysis, Training, Wheels,
Cameras
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Martin, S.,
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ICPR16(2783-2788)
IEEE DOI
1705
Data models, Feature extraction, Head, Iris, Predictive models,
Radio frequency, Vehicles
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ICPR16(573-578)
IEEE DOI
1705
Convolution, Databases, Feature extraction, Neural networks,
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Driver Drowsiness Detection via a Hierarchical Temporal Deep Belief
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1704
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Detection of Driver Drowsiness Using 3D Deep Neural Network and
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1704
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Shih, T.H.[Tun-Huai],
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MSTN: Multistage Spatial-Temporal Network for Driver Drowsiness
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1704
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Park, S.[Sanghyuk],
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Drowsy16(III: 154-164).
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Learning Feature Representation for Face Verification,
AVSS17(1-6)
IEEE DOI
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convolution, face recognition, feature extraction,
image classification, image representation,
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Park, S.[Sangwoo],
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ICIP14(214-218)
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CVVT13(616-623)
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Chapter on Motion -- Feature-Based, Long Range, Motion and Structure Estimates, Tracking, Surveillance, Activities continues in
Driver Distraction Analysis, Driver Attention, Driver Inattention .