Driver Drowsiness Detection and Measurement Methods - Driver Drowsiness Detection: Systems and Solutions - page 13

extraction process is followed by training and use of machine learning algorithms
of various capabilities, strengths and weaknesses [ 9 , 12 , 18 , 19 , 22 , 26 , 38 , 41 , 42 ,
58 - 60 , 63 , 66 ].

￿ Multiple Facial Actions . Some researchers used multiple facial features, includ-

ing state and position of the eyebrow, lip and jaw dropping combined with eye
blinking [ 10 ].

Behavioral methods are considered cost effective and non-invasive, but lead to
significant technical challenges. In addition to the challenges associated with the
underlying computer vision, machine learning and image processing algorithms,
the resulting systems are required to perform in real-time and to exhibit robustness
when faced with bumpy roads, lighting changes, dirty lenses, improperly mounted
cameras, and many other real-world less-than-ideal driving situations.

2.5 Hybrid Methods

All of the previously mentioned methods have strengths and weaknesses. Vehicle-
based measurements depend on specific driving conditions (such as weather,
lighting, etc.) and can be used on specific roads only (with clearly marked signs
and lanes). Moreover, they may lead to a large number of false positives, which
would lead to a loss of confidence in the method. Behavioral measures, on the other
hand, may show huge variation in the results depending on the associated lighting
conditions. Physiological measures are reliable and accurate but their intrusive
nature is still a challenge, which may be mitigated should non-invasive physiological
sensors become feasible in the near future [ 32 , 46 ].
Several recent research studies have attempted to develop driver drowsiness
detection systems as a fusion of different methods. One study which combined
behavioral methodology and vehicle-based methodology showed that the reliability
and accuracy of the created hybrid method was significantly higher than those
using a single methodology approach [ 8 ]. Another study, which included subjective
measures in combination with behavioral and physiological measures, showed
significantly higher success rate than any individual method alone [ 67 ]. These
early results suggest that a combination of the three types of methods—behavioral,
physiological and vehicle-based—is a promising avenue worth pursuing in the
development of real-world, vehicle-mounted, driver drowsiness detection solutions.

References

1. T. Abe, T. Nonomura, Y. Komada, S. Asaoka, T. Sasai, A. Ueno, and Y. Inoue. Detecting
deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance
of wakefulness tests. International Journal of Psychophysiology , 82(3):269-274, 2011.