Preliminary development and test of a new automatic drowsiness quantification system using range and intensity images obtained from a dashboard-mounted near-infrared 3D range sensor [PDF]
, 2016 Langohr, Thomas +2 more
core
Improving automatic detection of driver fatigue and distraction using machine learning
Changes and advances in information technology have played an important role in the development of intelligent vehicle systems in recent years. Driver fatigue and distracted driving are important factors in traffic accidents.
Wu, Dongjiang
core
Geometric Brownian motion (GBM) random process model appears to be an excellent choice for modeling realizations of perclos signals [PDF]
Sleep Medicine, 2017 Ebrahimbabaie Varnosfaderani, Pouyan +1 more
openaire +1 more source
Attention-based multi-semantic dynamical graph convolutional network for eeg-based fatigue detection. [PDF]
Front Neurosci, 2023 Liu H +7 more
europepmc +1 more source
Forecasting psychomotor vigilance test performance from facial videos. [PDF]
SleepAbe T.
europepmc +1 more source
TMU-Net: A Transformer-Based Multimodal Framework with Uncertainty Quantification for Driver Fatigue Detection. [PDF]
Sensors (Basel)Zhang Y, Xu X, Du Y, Zhang N.
europepmc +1 more source
On Fatigue Detection for Air Traffic Controllers Based on Fuzzy Fusion of Multiple Features. [PDF]
Comput Math Methods Med, 2022 Hu Y +6 more
europepmc +1 more source
A dense multi-pooling convolutional network for driving fatigue detection. [PDF]
Sci RepHan Q +6 more
europepmc +1 more source