ReviewSensors (Basel, Switzerland)2022
A Review of Recent Developments in Driver Drowsiness Detection Systems.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 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
- Emotion-Aware Contextual Modelling for Robust Driver Fatigue Detection.Sensors (Basel, Switzerland) · 2026Article
- Research on drowsiness detection in UAV operators based on the random decision forest method.Scientific reports · 2026Article
- Smart Steering Wheel Prototype for In-Vehicle Vital Sign Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Driver Drowsiness Detection using Machine Learning and Deep Learning Techniques: A Systematic Review.Archives of academic emergency medicine · 2026Article
- Predicting driver fatigue using HRV measures and machine learning.Frontiers in sports and active living · 2026Article
- Analysis of Physiological Parameters and Driver Posture for Prevention of Road Accidents: A Review.Sensors (Basel, Switzerland) · 2025Review
- Yawning as Therapy? The Potential of the Conditioned Yawn Reflex as a Novel Treatment for Insomnia Disorder.Journal of sleep research · 2025Article
- A Novel Hybrid Approach for Drowsiness Detection Using EEG Scalograms to Overcome Inter-Subject Variability.Sensors (Basel, Switzerland) · 2025Article
- EffRes-DrowsyNet: A Novel Hybrid Deep Learning Model Combining EfficientNetB0 and ResNet50 for Driver Drowsiness Detection.Sensors (Basel, Switzerland) · 2025Article
- A Real-Time Embedded System for Driver Drowsiness Detection Based on Visual Analysis of the Eyes and Mouth Using Convolutional Neural Network and Mouth Aspect Ratio.Sensors (Basel, Switzerland) · 2024Article
- Efficient Generalized Electroencephalography-Based Drowsiness Detection Approach with Minimal Electrodes.Sensors (Basel, Switzerland) · 2024Article
- Ultra-Wide Band Radar Empowered Driver Drowsiness Detection with Convolutional Spatial Feature Engineering and Artificial Intelligence.Sensors (Basel, Switzerland) · 2024Article
- Persona-PhysioSync AV: Personalized Interaction through Personality and Physiology Monitoring in Autonomous Vehicles.Sensors (Basel, Switzerland) · 2024Article
- Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions.Sensors (Basel, Switzerland) · 2024Article
- Effectiveness and User Perception of an In-Vehicle Voice Warning for Hypoglycemia: Development and Feasibility Trial.JMIR human factors · 2024Article
- Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model.Sensors (Basel, Switzerland) · 2023Article
- DDD TinyML: A TinyML-Based Driver Drowsiness Detection Model Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Real-Time Machine Learning-Based Driver Drowsiness Detection Using Visual Features.Journal of imaging · 2023Article
- Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Continuous advancements in computing technology and artificial intelligence in the past decade have led to improvements in driver monitoring systems. Numerous experimental studies have collected real driver drowsiness data and applied various artificial intelligence algorithms and feature combinations with the goal of significantly enhancing the performance of these systems in real-time. This paper presents an up-to-date review of the driver drowsiness detection systems implemented over the last decade. The paper illustrates and reviews recent systems using different measures to track and detect drowsiness. Each system falls under one of four possible categories, based on the information used. Each system presented in this paper is associated with a detailed description of the features, classification algorithms, and used datasets. In addition, an evaluation of these systems is presented, in terms of the final classification accuracy, sensitivity, and precision. Furthermore, the paper highlights the recent challenges in the area of driver drowsiness detection, discusses the practicality and reliability of each of the four system types, and presents some of the future trends in the field.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.