Driver Drowsiness Detection Techniques: A Survey

• Published in: 2021 1st Babylon International Conference on Information Technology and Science (BICITS) pp. 45 - 51
• Main Authors: Hussein, Marwa K., Salman, Tariq M., Miry, Abbas H., Subhi, Mohammed A.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 28.04.2021
• Subjects: Automatic Drowsiness Detection; Biology; Biomedical monitoring; Driver Assistance Methods; Driver Awareness; Driver Drowsiness; Electrocardiography; Electroencephalography; Electromyography; Feature extraction; Wheels
• DOI: 10.1109/BICITS51482.2021.9509912

In recent years, driver drowsiness and sleep are significant cause of road accidents, especially when drivers drive for a long time on highways. Avoiding an accident can be the aim of smart systems nowadays. A robust driver detection system must be designed to alert the driver. This paper surveys the literature for the various techniques used to detect driver drowsiness, including but not limited to the physical-based technique that detects detecting features such as eyes state (closed or opened), eye blinking rate, yawning and head movement. Another technique used is a physiologically based technique that detects (EEG) signals, (ECG) signals, (PPG), Heart Rate Variability, (EOG) signals, and (EMG) signals to evaluate the degree of driver drowsiness. Another technique used to measure driver's drowsiness is a vehicular-based technique that monitored and controlled the vehicle using steering wheel movement (SWM) and the standard deviation of lane position (SDLP). The last technique is a hybrid technique that combined more than one technique to detect driver drowsiness. This paper will highlight the limitations, advantages, remaining issues, and challenges of the suggested methods.