Research graph
References from Towards driver abnormal behavior detection under local occlusion and small object conditions. Local targets link to admitted publications; unresolved targets remain external evidence.
Hybrid-aligned domain adaptation for driver distraction recognition
2026 · External reference
Spatio-temporal pruning: A generic method for edge-based 3D distracted driver recognition
2026 · External reference
CAFFT: Cross-attention feature fusion transformer for risky driving behavior recognition
10.1109/tits.2026.3651971 · 2026 · External reference
LFGNet: Low-level feature-guided network for drivers’ calling behavior detection
10.1016/j.compeleceng.2025.110401 · 2025 · External reference
A lightweight model for distracted driver detection based on neural architecture search and coordinate attention
10.1016/j.compeleceng.2025.110235 · 2025 · External reference
A temporal–spatial deep learning approach for driver distraction detection based on EEG signals
10.1109/tase.2021.3088897 · 2022 · External reference
Detection of train driver fatigue and distraction based on forehead EEG: A time-series ensemble learning method
10.1109/tits.2021.3125737 · 2022 · External reference
Driver distraction detection based on vehicle dynamics using naturalistic driving data
10.1016/j.trc.2022.103561 · 2022 · External reference
Online detection of driver fatigue using steering wheel angles for real driving conditions
10.3390/s17030495 · 2017 · External reference
A method for fatigue detection based on driver’s steering wheel grip
10.1016/j.ergon.2021.103083 · 2021 · External reference
MVim: A high-accuracy, lightweight neural network for real-time driver behavior recognition
10.1016/j.eswa.2025.129091 · 2026 · External reference
Driver fatigue detection based on improved YOLOv7
2024 · External reference
CogMamba: Multi-task driver cognitive load and physiological non-contact estimation with multimodal facial features
10.3390/s25185620 · 2025 · External reference
DriSm_YNet: A breakthrough in real-time recognition of driver smoking behavior using YOLO-NAS
10.1007/s00521-024-10162-w · 2024 · External reference
Deep unsupervised multi-modal fusion network for detecting driver distraction
10.1016/j.neucom.2020.09.023 · 2021 · External reference
Simultaneous eye blink characterization and elimination from low-channel prefrontal EEG signals enhances driver drowsiness detection
10.1109/jbhi.2021.3096984 · 2022 · External reference
Driving fatigue detection based on hybrid electroencephalography and eye tracking
10.1109/jbhi.2024.3446952 · 2024 · External reference
End-to-end fatigue driving EEG signal detection model based on improved temporal-graph convolution network
10.1016/j.compbiomed.2022.106431 · 2023 · External reference
EEG-based driver drowsiness detection based on simulated driving environment
10.1016/j.neucom.2024.128961 · 2025 · External reference
Driver behavior soft-sensor based on neurofuzzy systems and weighted projection on principal components
10.1109/jsen.2020.2995921 · 2020 · External reference
10.1109/iros55552.2023.10342070
10.1109/iros55552.2023.10342070 · External reference
Unresolved reference
2024 · External reference
Automatic driver distraction detection using deep convolutional neural networks
2022 · External reference
CEAM-YOLOv7: Improved YOLOv7 based on channel expansion and attention mechanism for driver distraction behavior detection
10.1109/access.2022.3228331 · 2022 · External reference
Multimodal driver distraction detection using dual-channel network of CNN and transformer
10.1016/j.eswa.2023.121066 · 2023 · External reference
Unresolved reference
2023 · External reference
Contrastive multiview coding
2020 · External reference
Multiview classification with cohesion and diversity
10.1109/tcyb.2018.2881474 · 2020 · External reference
A survey on multiview clustering
10.1109/tai.2021.3065894 · 2021 · External reference
Deep multiview learning for hyperspectral image classification
10.1109/tgrs.2020.3034133 · 2021 · External reference
10.1109/iccv.2019.00972
10.1109/iccv.2019.00972 · External reference
E2DR: A deep learning ensemble-based driver distraction detection with recommendations model
10.3390/s22051858 · 2022 · External reference
Recognizing distractions for assistive driving by tracking body parts
10.1109/tcsvt.2018.2818407 · 2019 · External reference
Driver distraction identification with an ensemble of convolutional neural networks
2019 · External reference
10.1007/978-3-030-66823-5_23
10.1007/978-3-030-66823-5_23 · External reference
Distracted driver detection by combining in-vehicle and image data using deep learning
10.1016/j.asoc.2020.106657 · 2020 · External reference
SSD: Single shot MultiBox detector
2016 · External reference
Unresolved reference
2024 · External reference
10.1109/cvpr52729.2023.00721
10.1109/cvpr52729.2023.00721 · External reference
Unresolved reference
2024 · External reference
Unresolved reference
2024 · External reference
10.1109/cvpr52733.2024.01605
10.1109/cvpr52733.2024.01605 · External reference
Unresolved reference
2024 · External reference
10.1109/cvpr.2016.90
10.1109/cvpr.2016.90 · External reference
10.1109/iccv.2017.324
10.1109/iccv.2017.324 · External reference
Improving real-time driver distraction detection via constrained attention mechanism
10.1016/j.engappai.2023.107408 · 2024 · External reference
10.1007/978-3-030-66823-5_23
10.1007/978-3-030-66823-5_23 · ExternalCitation · doi-reference
DriSm_YNet: A breakthrough in real-time recognition of driver smoking behavior using YOLO-NAS
10.1007/s00521-024-10162-w · ExternalCitation · doi-reference
Distracted driver detection by combining in-vehicle and image data using deep learning
10.1016/j.asoc.2020.106657 · ExternalCitation · doi-reference
End-to-end fatigue driving EEG signal detection model based on improved temporal-graph convolution network
10.1016/j.compbiomed.2022.106431 · ExternalCitation · doi-reference
A lightweight model for distracted driver detection based on neural architecture search and coordinate attention
10.1016/j.compeleceng.2025.110235 · ExternalCitation · doi-reference
LFGNet: Low-level feature-guided network for drivers’ calling behavior detection
10.1016/j.compeleceng.2025.110401 · ExternalCitation · doi-reference
Improving real-time driver distraction detection via constrained attention mechanism
10.1016/j.engappai.2023.107408 · ExternalCitation · doi-reference
A method for fatigue detection based on driver’s steering wheel grip
10.1016/j.ergon.2021.103083 · ExternalCitation · doi-reference
Multimodal driver distraction detection using dual-channel network of CNN and transformer
10.1016/j.eswa.2023.121066 · ExternalCitation · doi-reference
MVim: A high-accuracy, lightweight neural network for real-time driver behavior recognition
10.1016/j.eswa.2025.129091 · ExternalCitation · doi-reference
Deep unsupervised multi-modal fusion network for detecting driver distraction
10.1016/j.neucom.2020.09.023 · ExternalCitation · doi-reference
EEG-based driver drowsiness detection based on simulated driving environment
10.1016/j.neucom.2024.128961 · ExternalCitation · doi-reference
Driver distraction detection based on vehicle dynamics using naturalistic driving data
10.1016/j.trc.2022.103561 · ExternalCitation · doi-reference
CEAM-YOLOv7: Improved YOLOv7 based on channel expansion and attention mechanism for driver distraction behavior detection
10.1109/access.2022.3228331 · ExternalCitation · doi-reference
10.1109/cvpr.2016.90
10.1109/cvpr.2016.90 · ExternalCitation · doi-reference
10.1109/cvpr52729.2023.00721
10.1109/cvpr52729.2023.00721 · ExternalCitation · doi-reference
10.1109/cvpr52733.2024.01605
10.1109/cvpr52733.2024.01605 · ExternalCitation · doi-reference
10.1109/iccv.2017.324
10.1109/iccv.2017.324 · ExternalCitation · doi-reference
10.1109/iccv.2019.00972
10.1109/iccv.2019.00972 · ExternalCitation · doi-reference
10.1109/iros55552.2023.10342070
10.1109/iros55552.2023.10342070 · ExternalCitation · doi-reference
Simultaneous eye blink characterization and elimination from low-channel prefrontal EEG signals enhances driver drowsiness detection
10.1109/jbhi.2021.3096984 · ExternalCitation · doi-reference
Driving fatigue detection based on hybrid electroencephalography and eye tracking
10.1109/jbhi.2024.3446952 · ExternalCitation · doi-reference
Driver behavior soft-sensor based on neurofuzzy systems and weighted projection on principal components
10.1109/jsen.2020.2995921 · ExternalCitation · doi-reference
A survey on multiview clustering
10.1109/tai.2021.3065894 · ExternalCitation · doi-reference
A temporal–spatial deep learning approach for driver distraction detection based on EEG signals
10.1109/tase.2021.3088897 · ExternalCitation · doi-reference
Recognizing distractions for assistive driving by tracking body parts
10.1109/tcsvt.2018.2818407 · ExternalCitation · doi-reference
Multiview classification with cohesion and diversity
10.1109/tcyb.2018.2881474 · ExternalCitation · doi-reference
Deep multiview learning for hyperspectral image classification
10.1109/tgrs.2020.3034133 · ExternalCitation · doi-reference
Detection of train driver fatigue and distraction based on forehead EEG: A time-series ensemble learning method
10.1109/tits.2021.3125737 · ExternalCitation · doi-reference
CAFFT: Cross-attention feature fusion transformer for risky driving behavior recognition
10.1109/tits.2026.3651971 · ExternalCitation · doi-reference
Online detection of driver fatigue using steering wheel angles for real driving conditions
10.3390/s17030495 · ExternalCitation · doi-reference
E2DR: A deep learning ensemble-based driver distraction detection with recommendations model
10.3390/s22051858 · ExternalCitation · doi-reference
CogMamba: Multi-task driver cognitive load and physiological non-contact estimation with multimodal facial features
10.3390/s25185620 · ExternalCitation · doi-reference
