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An EEG-Assisted Two-Stage PERCLOS Forecasting Method for Driver Fatigue Early Warning

https://doi.org/10.2139/ssrn.6909259
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Outgoing claims: the papers this work cites, builds on, or argues with.

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Real-time driver drowsiness feedback improves driver alertness and self-reported driving performance
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Optimal design alternatives of advance guide signs of closely spaced exit ramps on urban expressways
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Blink detection using Adaboost and contour circle for fatigue recognition
inferredcites →https://doi.org/10.1016/j.compeleceng.2025.110204openalex-snapshot
CM-FusionNet: A cross-modal fusion fatigue detection method based on electroencephalogram and electrooculogram
inferredcites →https://doi.org/10.1016/j.eswa.2007.12.043openalex-snapshot
Using EEG spectral components to assess algorithms for detecting fatigue
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Driver fatigue transition prediction in highly automated driving using physiological features
inferredcites →https://doi.org/10.1016/j.trf.2014.09.005openalex-snapshot
Transition to manual: Driver behaviour when resuming control from a highly automated vehicle
inferredcites →https://doi.org/10.1016/j.trf.2022.10.012openalex-snapshot
Mental models of driver monitoring systems: Perceptions of monitoring capabilities in an online U.S-based sample
inferredcites →https://doi.org/10.1016/j.trf.2023.12.010openalex-snapshot
Monitoring driver drowsiness in partially automated vehicles: Added value from combining postural and physiological indicators
inferredcites →https://doi.org/10.1016/j.trf.2024.10.005openalex-snapshot
Drivers’ acceptance of adaptive warning–monitoring systems. Findings from a car driving simulator study
inferredcites →https://doi.org/10.1016/j.ymeth.2021.04.017openalex-snapshot
A compact and interpretable convolutional neural network for cross-subject driver drowsiness detection from single-channel EEG
inferredcites →https://doi.org/10.1038/s41598-022-05810-xopenalex-snapshot
Driver drowsiness estimation using EEG signals with a dynamical encoder–decoder modeling framework
inferredcites →https://doi.org/10.1097/01.anes.0000291444.68894.eeopenalex-snapshot
Quantification of Epileptiform Electroencephalographic Activity during Sevoflurane Mask Induction
inferredcites →https://doi.org/10.1109/ojits.2021.3102125openalex-snapshot
Toward Health–Related Accident Prevention: Symptom Detection and Intervention Based on Driver Monitoring and Verbal Interaction
inferredcites →https://doi.org/10.1109/thms.2016.2549032openalex-snapshot
Driver Assistance System With a Dual Control Scheme: Effectiveness of Identifying Driver Drowsiness and Preventing Lane Departure Accidents
inferredcites →https://doi.org/10.1109/tits.2020.2977762openalex-snapshot
Multimodal Features for Detection of Driver Stress and Fatigue: Review
inferredcites →https://doi.org/10.1109/tits.2021.3125737openalex-snapshot
Detection of Train Driver Fatigue and Distraction Based on Forehead EEG: A Time-Series Ensemble Learning Method
inferredcites →https://doi.org/10.1109/tits.2023.3347075openalex-snapshot
TFAC-Net: A Temporal-Frequential Attentional Convolutional Network for Driver Drowsiness Recognition With Single-Channel EEG
inferredcites →https://doi.org/10.1109/tits.2023.3348517openalex-snapshot
EEG-Based Driver Fatigue Detection Using Spatio-Temporal Fusion Network With Brain Region Partitioning Strategy
inferredcites →https://doi.org/10.1177/0018720816685832openalex-snapshot
Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control
inferredcites →https://doi.org/10.1177/00187208231208523openalex-snapshot
Drowsiness Mitigation Through Driver State Monitoring Systems: A Scoping Review
inferredcites →https://doi.org/10.1609/aaai.v37i9.26317openalex-snapshot
Are Transformers Effective for Time Series Forecasting?
inferredcites →https://doi.org/10.3141/1776-25openalex-snapshot
Multivariate Vehicular Traffic Flow Prediction: Evaluation of ARIMAX Modeling
inferredcites →https://doi.org/10.3390/s25237316openalex-snapshot
Research on the Prediction of Driver Fatigue Degree Based on EEG Signals
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