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PERCLOS: A valid psychophysiological measure of alertness as assessed by psychomotor vigilance

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References

1994Research on vehicle-based driver status/performance monitoring: Development, validation, and refinement of algorithms for detection of driver drowsiness182 cited

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2016Recent Trends in Driver Safety Monitoring Systems: State of the Art and Challenges168 cited
2016The ULg multimodality drowsiness database (called DROZY) and examples of use150 cited
2018Automatic driver sleepiness detection using EEG, EOG and contextual information145 cited
2019Methodology and Mobile Application for Driver Behavior Analysis and Accident Prevention139 cited
2013Various Approaches for Driver and Driving Behavior Monitoring: A Review136 cited
2009Detection of Driver Fatigue Caused by Sleep Deprivation135 cited
2018Driver Drowsiness Detection Using Condition-Adaptive Representation Learning Framework124 cited
2014EOG-based drowsiness detection using convolutional neural networks117 cited
2017Driver drowsiness detection using facial dynamic fusion information and a DBN96 cited
2021Using long short term memory and convolutional neural networks for driver drowsiness detection93 cited
2015Real-time driver drowsiness feedback improves driver alertness and self-reported driving performance92 cited
2021Survey and Synthesis of State of the Art in Driver Monitoring86 cited
2019

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Looking at the Driver/Rider in Autonomous Vehicles to Predict Take-Over Readiness

85 cited

2021Exploration of the effects of task-related fatigue on eye-motion features and its value in improving driver fatigue-related technology79 cited

2019Comprehensive Drowsiness Level Detection Model Combining Multimodal Information77 cited

2016Real-Time Driver Drowsiness Detection System Based on PERCLOS and Grayscale Image Processing77 cited