ReviewSensors (Basel, Switzerland)2021
Trends and Future Prospects of the Drowsiness Detection and Estimation Technology.
Review in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A Bio-Inspired Physiological-Behavioral Fusion Method for Human-Factor Risk Assessment of Imported Unmanned Intelligent System Operators in Port-Security Scenarios.Biomimetics (Basel, Switzerland) · 2026Article
- Real-Time Driver Attention Detection in Complex Driving Environments via Binocular Depth Compensation and Multi-Source Temporal Bidirectional Long Short-Term Memory Network.Sensors (Basel, Switzerland) · 2025Article
- Enhancing convolutional neural networks in electroencephalogram driver drowsiness detection using human inspired optimizers.Scientific reports · 2025Article
- Multi-Index Driver Drowsiness Detection Method Based on Driver's Facial Recognition Using Haar Features and Histograms of Oriented Gradients.Sensors (Basel, Switzerland) · 2024Article
- Wireless ear EEG to monitor drowsiness.Nature communications · 2024Article
- Threats Detection during Human-Computer Interaction in Driver Monitoring Systems.Sensors (Basel, Switzerland) · 2022Article
- A Review of Recent Developments in Driver Drowsiness Detection Systems.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
1 author.
Funding
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Abstract
Drowsiness is among the important factors that cause traffic accidents; therefore, a monitoring system is necessary to detect the state of a driver's drowsiness. Driver monitoring systems usually detect three types of information: biometric information, vehicle behavior, and driver's graphic information. This review summarizes the research and development trends of drowsiness detection systems based on various methods. Drowsiness detection methods based on the three types of information are discussed. A prospect for arousal level detection and estimation technology for autonomous driving is also presented. In the case of autonomous driving levels 4 and 5, where the driver is not the primary driving agent, the technology will not be used to detect and estimate wakefulness for accident prevention; rather, it can be used to ensure that the driver has enough sleep to arrive comfortably at the destination.
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Registered trials
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