Driver Drowsiness Detection and Measurement Methods - Driver Drowsiness Detection: Systems and Solutions - page 10

a real-world driving scenario, having electrodes attached to the driver's head,
beyond their huge inconvenience, would hinder their driving capabilities and
potentially increase the chances of an accident happening.

￿ Electrooculogram (EOG) records the electrical potential difference between the

cornea and the retina of a human eye. It is shown that this difference determines
the behavior of the eye, which can be used to monitor drivers' alertness level
[ 24 , 28 , 31 ]. This method is highly invasive since it requires direct contact with
a subject, usually in the following manner: a disposable electrode is placed on
the outer corner of each eye and a third electrode at the center of the forehead
for reference [ 34 ]. The associated methodology is relatively simple: if a slower
eye movement is detected, compared to the regular eye movement of a subject in
the awake stage, the conclusion is that the subject is becoming drowsy. Though
this type of measurement is very precise and leads to very small detection errors,
it is not the most practical for real-world, real-time implementation due to its
invasiveness and the complexity of the apparatus needed for the measurement.

The reliability and accuracy of driver drowsiness detection by using physiolog-
ical signals is very high compared to other methods. However, the intrusive nature
of measuring physiological signals remains an issue that prevents their use in real-
world scenarios. Due to the technological progress in recent years, it is possible
that some of the problems caused by these methods will be overcome in the future.
Examples include: the use of wireless devices to measure physiological signals in
a less intrusive manner by placing the electrodes on the body and obtaining signals
using wireless technologies like Zigbee or Bluetooth; or by placing electrodes on the
steering wheel [ 17 , 68 ]; or placing electrodes on the drivers seat [ 4 ]. The obtained
signals can be processed and monitored in various ways, such as using smart
phone devices [ 20 , 32 ]. Obtaining these signals in a non-intrusive way certainly
contributes towards their real-world applicability. But the question on whether this
way of collecting data may lead to increased measurement errors has not been
answered conclusively yet. Recently, a few experiments have been conducted to
validate the potential use of less-intrusive or non-intrusive systems and inspect the
implications of this trade-off [ 4 , 17 ].

2.3 Vehicle-Based Methods

Our understanding of drowsy-driving crashes is often based on subjective evidence,
such as police crash reports and driver's self-reports following the event [ 43 , 49 ].
Evidence gathered from the reports suggests that the typical drivers' and vehicles'
behavior during these events usually exhibit characteristics such as:

￿ Higher speed with little or no breaking . Fall-asleep crashes are likely to have

serious consequences. The mortality rate associated with drowsy-driving crashes
is high, probably due to the combination of higher speeds and delayed reaction
time [ 23 ].