<p>Automobiles are an integral part of civilized society and the fastest world. An accident rate due to the fault of the driver continues to increase at an alarming rate year after year; it can cause permanent damage to the person involved and pose social risks. Logistics and commodity transportation at night are essential, but long trips can cause drowsiness caused by illness or fatigue, and can also be a problem due to the aging of drivers. Driver behavior analysis can help select drivers and can avoid accidents due to drowsiness. The Internet of Things can get real-time information to alert the driver and his relatives about the location of the driver. Detection with IoT can monitor driver sleep through physiological, behavioral, or vehicle inputs and send warning messages to higher officials and drivers’ relatives. Intimation to higher officials can be helpful in creating a database on the general sleep character of the driver. To avoid accidents, the main target is the rapid detection of drowsiness of the driver. Deep learning is used for the fastest detection and can predict accidents. The review of the latest articles supports the idea that electroencephalography (EEG) signals from the brain can provide basic information about the initial stages of sleep in a person. This systematic review summarizes the hybrid mode of detection that is found to be the best compared to the system with vehicle and physiological only considered. Detection speed improvement is also a core area where research is needed.</p>