Detecting drowsiness in drivers, especially multi-level drowsiness, is a difficult problem that
is often approached using neurophysiological signals as the basis for building a reliable …

Driver fatigue detection systems have potential to improve road safety by preventing crashes
and saving lives. Conventional driver monitoring systems based on driving performance and …

Introduction: Drowsiness is one of the main contributors to road-related crashes and
fatalities worldwide. To address this pressing global issue, researchers are continuing to …

Vision is the richest and most cost-effective technology for Driver Monitoring Systems (DMS),
especially after the recent success of Deep Learning (DL) methods. The lack of sufficiently …

Graph theory analysis, a mathematical approach, has been applied in brain connectivity
studies to explore the organization of network patterns. The computation of graph theory …

Existing visual-based fatigue detection methods usually monitor drivers' fatigue by capturing
their facial features, including eyelid movements, yawn frequency and head pose. However …

Electroencephalography (EEG) has been widely used for studying brain function. As cortical
signals recorded by the EEG are very weak, they are often obscured by motion artifacts and …

Drunkenness or exhaustion is a leading cause of car accidents, with severe implications for
road safety. More fatal accidents could be avoided if fatigued drivers were warned ahead of …

Road vehicle accidents are mostly due to human errors, and many such accidents could be
avoided by continuously monitoring the driver. Driver monitoring (DM) is a topic of growing …

Driver fatigue and stress significantly contribute to higher number of car accidents
worldwide. Although, different detection approaches have been already commercialized and …