  Driver drowsiness has caused a large number of serious injuries and deaths on
public roads and incurred billions of taxpayer dollars in costs. Hence,
monitoring of drowsiness is critical to reduce this burden on society. This
paper surveys the broad range of solutions proposed to address the challenges
of driver drowsiness, and identifies the key steps required for successful
implementation. Although some commercial products already exist, with
vehicle-based methods most commonly implemented by automotive manufacturers,
these systems may not have the level of accuracy required to properly predict
and monitor drowsiness. State-of-the-art models use physiological, behavioural
and vehicle-based methods to detect drowsiness, with hybrid methods emerging as
a superior approach. Current setbacks to implementing these methods include
late detection, intrusiveness and subject diversity. In particular,
physiological monitoring methods such as Electroencephalography (EEG) are
intrusive to drivers; while behavioural monitoring is least robust, affected by
external factors such as lighting, as well as being subject to privacy
concerns. Drowsiness detection models are often developed and validated based
on subjective measures, with the Karolinska Sleepiness Scale being the most
popular. Subjective and incoherent labelling of drowsiness, lack of on road
data and inconsistent protocols for data collection are among other challenges
to be addressed to progress drowsiness detection for reliable on-road use.
