IEEE Consumer Electronics Magazine - October 2015 - 108

Tier-1 suppliers have to develop
modules or units that bring into
play new technologies and
methods that fit OEMs' systems
and requirements.
the driver was drowsy, sleepy, asleep, and/or fatigued
the driver was under the influence of alcohol, drugs, and/or
medication
▼ the driver was inattentive (talking, eating, etc.)
▼ a cellular telephone was present in the vehicle
▼ a cellular telephone was in use in vehicle.
These factors contribute to about 17.5% of the total number
of collisions. It is important to note that this trend has been
present since 2003-2007, as can be seen in Table 1, where
the contribution of fatigue and inattention to the total number
of vehicle collisions is about 16% (average) [1].
Light-vehicle (trucks and cars) crashes are an issue that
affects several countries. For example, the United States is
greatly troubled by this problem, with fatigue causing a large
number of deaths and injuries. The NHTSA states that nearly
1,500 people were killed in 2002, and, for the period from
1989 to 1992, there were 71,000 injuries from 100,000 collisions [39]. Also, as mentioned previously, during 2003-2007,
approximately 16% of collisions were related to fatigue and
inattention (see Table 1), which are very significant contributors to crashes [1]. These data lead to the conclusion that,
besides the material damages caused by these incidents, there
are many injuries and deaths produced by collisions. The
importance of preventing or minimizing incidents of this
nature is, therefore, increasing tremendously.
Driving requires the driver's attention, but today's drivers live
with a lot of pressure and exhausting situations that may diminish
their perception and vehicle-control capabilities. There are multiple aspects that could contribute to drivers' drowsiness. Among
the most important are fatigue (either mental or physical), lack of
▼
▼

Table 1. Total vehicle collisions due to fatigue
and inattention (2003-2007) [1].

Year

Total Vehicle
Collisions

Number of
Collisions Due
to Fatigue and
Inattention

Collisions Due
to Fatigue and
Inattention (%)

2003

58,805

8,835

15

2004

58,729

9,122

15.5

2005

59,495

8,949

15

2006

58,094

9,837

16.9

2007

55,926

9,797

17.5

108 IEEE Consumer Electronics Magazine

^

october 2015

sleep, and drug consumption [2]-[4]. Drivers are not typically
conscious of how their capabilities could be diminished due to
drowsiness. For example, a survey of long-haul drivers in the
United States [5] shows that 28% of the evaluated individuals
have unintentionally dozed or slept at the wheel.
One possible solution is to enable the vehicle to detect
drowsiness or discrepancies in the driver's behavior and alert
the user when it occurs. The best strategy for such systems is
to indicate the current drowsiness condition [5]-[6]. The main
function of drowsiness-/fatigue-detection (DFD) systems [2],
[7]-[9] is to monitor the driver's condition and take action
accordingly. There could be several stages, such as
▼ defining, as adequately as possible, a certain category of
alertness level for the conditions detected
▼ describing whether the category is acceptable for the user
to keep driving
▼ if the alertness level is not tolerable, triggering an alarm to
inform the user of his or her current state (i.e., recommending a nap as a good fatigue countermeasure [3]-[6],
[10]) or generating an action to diminish the drowsiness
state that could cause a collision (i.e., vibration stimuli on
the seat belt [11] or playing music [12], [13], etc.).
Besides monitoring differences in the subject, there
could be a division regarding DFD system types, i.e., intrusive versus nonintrusive ones [14]. Examples of intrusive
systems are those that measure blood pressure (BP) [15],
[16] or analyze electroencephalography plots [17], [18].
These systems require the user to wear potentially uncomfortable sensors that he or she may reject, and the system
could thus be bypassed if the sensors are not worn or
attached to the driver.
There is a large body of work on nonintrusive DFD systems, and they are commonly based on vision systems [19],
[36]. Any DFD system should be user-friendly so that the
user does not try to avoid it. A nonintrusive system could fit
better into the user-friendly category since the user would
not notice it. There is also a need to have more robust algorithms that fit the needs of future products and that could be
implemented into a component [24], [33] but would not disturb the customer.
There are already many companies developing DFD,
including Siemens VDO (now Continental) [37], Denso [38],
IBM [39], Bosch [39], Smart Eye [40], and Scania [41]-[44].
The most recently developed systems use different techniques,
such as monitoring how brake and accelerator pedals are used;
how the steering wheel is handled; lane departure [41], [42],
[45]; and the driver's brain waves [18], [46], [47] as well as
monitoring by means of sequential images of the face [19]-
[36] and other methods, which have been already summarized
by several works [2], [6]-[9], [29], [48], [49].
This article is intended to provide current information to
researchers and developers of DFD systems. The information
presented here covers three paramount efforts in the development of DFD systems: academic, governmental, and corporate. Thus, the reader will see the complete picture of this
area in just one article.



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