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Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short‐term memory units
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Fatigue driving recognition network: fatigue driving recognition via convolutional neural network and long short‐term memory units (2019)Fatigue driving recognition network: fati…Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions (2017)Online Detection of Driver Fa…Driver Fatigue Detection Based on Eye State Recognition (2017)Driver Fatigue Detection Base…Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State (2016)Towards Detection of Bus Driv…Head movement-based driver drowsiness detection: A review of state-of-art techniques (2016)Head movement-based driver dr…Driver’s Fatigue Detection Based on Yawning Extraction (2014)Driver’s Fatigue Detection Ba…PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance (1998)PERCLOS: A valid psychophysio…A drowsy driver detection system for heavy vehicles (2002)Monitoring head/eye motion for driver alertness with one camera (2002)Real-Time System for Monitoring Driver Vigilance (2006)Driver Drowsiness Detection Using Multi-Channel Second Order Blind Identifications (2019)Driver’s fatigue recognition based on yawn detection in thermal images (2019)Real time detection of driver fatigue based on CNN‐LSTM (2021)A critical review of the psychophysiology of driver fatigue (2001)Real-Time Nonintrusive Monitoring and Prediction of Driver Fatigue (2004)Detecting Driver Drowsiness Based on Sensors: A Review (2012)A Comprehensive Survey of Driving Monitoring and Assistance Systems (2019)Decision-making in driver-automation shared control: A review and perspectives (2020)DMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis (2020)Driver Drowsiness Classification Using Fuzzy Wavelet-Packet-Based Feature-Extraction Algorithm (2010)Driver Behavior Analysis for Safe Driving: A Survey (2015)Automatic Detection of Driver Impairment Based on Pupillary Light Reflex (2018)Survey and Synthesis of State of the Art in Driver Monitoring (2021)A Systematic Survey of Driving Fatigue Monitoring (2022)Approximate entropy as a measure of system complexity. (1991)Monotony of road environment and driver fatigue: a simulator study (2003)Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges (2020)Review and Perspectives on Driver Digital Twin and Its Enabling Technologies for Intelligent Vehicles (2022)Machine learning and deep learning techniques for driver fatigue and drowsiness detection: a review (2023)RESEARCH ON VEHICLE-BASED DRIVER STATUS/PERFORMANCE MONITORING; DEVELOPMENT, VALIDATION, AND REFINEMENT OF ALGORITHMS FOR DETECTION OF DRIVER DROWSINESS. FINAL REPORT (1994)Research on vehicle-based driver status/performance monitoring: Development, validation, and refinement of algorithms for detection of driver drowsiness (1994)A Comprehensive Review of Driver Behavior Analysis Utilizing Smartphones (2019)A Real-time Driving Drowsiness Detection Algorithm With Individual Differences Consideration (2019)Driver Fatigue Detection Method Based on Eye States With Pupil and Iris Segmentation (2020)Recent Trends in Driver Safety Monitoring Systems: State of the Art and Challenges (2016)The ULg multimodality drowsiness database (called DROZY) and examples of use (2016)Automatic driver sleepiness detection using EEG, EOG and contextual information (2018)
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References

  • 2016Towards Detection of Bus Driver Fatigue Based on Robust Visual Analysis of Eye State328 cited
  • 2017Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions206 cited
  • 1998PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance153 cited
  • 2017Driver Fatigue Detection Based on Eye State Recognition148 cited
  • 2014Driver’s Fatigue Detection Based on Yawning Extraction107 cited
  • 2016Head movement-based driver drowsiness detection: A review of state-of-art techniques100 cited

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