Abstract: In this research, a questionnaire survey was conducted
to measure nap, drowsiness and fatigue of drivers who work for long
shifts, to discuss about the work environment and health conditions for
taxi and bus drivers who work at night-time. The questionnaire sheet
used for this research was organized into the following categories:
tension/tiredness, drowsiness while driving, and the nap situation
during night-time work. The number of taxi drivers was 127 and the
number of bus drivers was 40. Concerning the results of a comparison
of nap hours of taxi and bus drivers, the taxi drivers’ nap hours are
overwhelmingly shorter, and also the frequency of drivers who
experience drowsiness is higher. The burden on bus drivers does not
change because of the system of a two-driver rotation shift. In
particular, the working environment of the taxi driver may lead to
greater fatigue accumulation than the bus driver’s environment.
Abstract: Nowadays, driving support systems, such as car
navigation systems, are getting common, and they support drivers in
several aspects. It is important for driving support systems to detect
status of driver's consciousness. Particularly, detecting driver's
drowsiness could prevent drivers from collisions caused by drowsy
driving. In this paper, we discuss the various artificial detection
methods for detecting driver's drowsiness processing technique. This
system is based on facial images analysis for warning the driver of
drowsiness or in attention to prevent traffic accidents.