Abstract
Driver monitoring systems have been improved over time as artificial intelligence and computer technology have advanced. Several experimental studies have collected real-world driver drowsiness data and used various artificial intelligence algorithms and feature combinations to dramatically improve the real-time effectiveness of these systems. This study presents an updated assessment of the driver sleepiness detection systems implemented over the last decade. In modern automobiles, assessing the driver's cognitive condition is an important aspect of passenger safety. The term cognitive state refers to a driver's mental and emotional state, which has a substantial impact on their ability to drive safely. Drivers' cognitive states may be altered by factors such as fatigue, distraction, stress, or disability. Intelligent automotive technology may be able to adapt and aid the driver by identifying varied conditions in real-time, reducing the frequency of accidents. The face, being an integral component of the body, communicates a significant quantity of information. The facial expressions, such as blinking and yawning patterns, exhibit changes in a driver when they are experiencing fatigue.
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