Research Aspects - Driver Drowsiness Detection: Systems and Solutions - page 34

A DDD system must be reliable, since ultimately there are human lives at stake.
It belongs to the categories of systems for which the cost of a false positive is
significantly lower than the cost of a false negative. In other words, it is best to
(occasionally) issue an alert when none was necessary (a false positive) than to miss
a truly serious situation that might lead to an accident (a false negative).
A DDD system should be nonintrusive. Its setup and components should be used
in a way that does not disturb normal driving. The driver's undivided attention has to
be on the road and the situation ahead of the car. The driver should not be distracted
by audio and visual distractions coming from the system. Moreover, the hardware
portion of the system should be small and discreet and properly placed, so as to
not occlude part of the driver's view. Moreover, physical interactions between the
system and the driver should be kept to a minimum, basically providing a quick
setup and calibration in the initialization phase, a friendly way to dismiss false
alarms (if any), and very little else.
Finally, a DDD system has to be proactive. It has to be capable of attracting
the driver's attention when necessary. Usually, a DDD system will use audio/visual
cues to communicate warning/alert messages to the driver, reporting suspicion of
drowsy behavior and trying to prevent a potentially dangerous situation. Care must
be taken to avoid causing sudden erratic behavior or startling the driver due to a
loud alarm, for example. Moreover, fallback provisions (e.g., applying the vehicle's
brakes) might be implemented if it has become clear that the driver is not responding
to the system's warnings.

References

1. T. Ahonen, A. Hadid, and M. Pietikäinen. Face recognition with local binary patterns. In

ECCV (1) , pages 469-481, 2004.
2. F. Bella. Driver perception of roadside configurations on two-lane rural roads: Effects on speed
and lateral placement. Accident Analysis and Prevention , 50(0):251-262, 2013.
3. L. Bergasa, J. Nuevo, M. Sotelo, R. Barea, and M. Lopez. Real-time system for monitoring
driver vigilance. Intelligent Transportation Systems, IEEE Transactions on , 7(1):63-77, 2006.
4. E. Cheng, B. Kong, R. Hu, and F. Zheng. Eye state detection in facial image based on linear
prediction error of wavelet coefficients. In Robotics and Biomimetics, 2008. ROBIO 2008.
IEEE International Conference on , pages 1388-1392, 2009.
5. D. Dawson, C. J. van den Heuvel, K. J. Reid, S. N. Biggs, and S. D. Baulk. Chasing the
silver bullet: Measuring driver fatigue using simple and complex tasks. Accident analysis and
prevention , 40(1):396-402, 2008.
6. W. Dong and P. Qu. Eye state classification based on multi-feature fusion. In Control and

Decision Conference, 2009. CCDC '09. Chinese , pages 231-234, 2009.
7. M. J. Flores, J. M. Armingol, and A. de la Escalera. Real-time warning system for driver

drowsiness detection using visual information. Journal of Intelligent and Robotic Systems ,

59(2):103-125, 2010.
8. T. Hong, H. Qin, and Q. Sun. An improved real time eye state identification system in driver
drowsiness detection. In Control and Automation, 2007. ICCA 2007. IEEE International
Conference on , pages 1449-1453, 2007.