The following is an unpublished article I made when I was working at the TNO
Human Factors Research Institute. It concerns an experiment performed in a car driving simulator in 1999.
In a driving simulator the
effects of time-on-task were measured on variables that measure drowsiness,
driving performance and steering behaviour. It was found that the fraction of
time during which the eyes are closed is a good measure of drowsiness that is
sensitive to the effects of time-on-task. Of all single variables that measure
driver performance and impairment, the percentage of time during which any part
of the vehicle exceeded one of the lane boundaries was the most strongly
affected by time-on-task. Also, with progressing drowsiness, the amplitude of
steering corrections increased towards larger values. This was caused both by
larger error corrections in response to larger errors and by an increase in
coarseness of the steering response. Large steering corrections proved to be
the single best indicator of progressing impairment by drowsiness and fatigue.
1.
INTRODUCTION
Falling
asleep at the wheel and drowsiness are considered important factors in accident
causation. Estimates of the involvement of these factors in accidents are
higher when the statistics are based on in-depth accident studies (10-25%)
compared to statistics based on general police databases (1-4%) (Horne &
Reyner, 1995). This indicates that, although the scope of the problem is not
clear, drowsiness and fatigue are significant risk factors. Because of this a
large number of studies on drowsiness, fatigue and sleepiness have been
reported in the literature. These studies differ widely in the variables used
to measure drowsiness and impaired driving.
Drowsiness is a
psychophysiological state that is assumed to result in an inability of the
driver to drive safely. Drowsiness is often measured by eyelid closures. The
percentage of time the eyes of the driver are 80 to 100% closed (PERCLOS) has
been used as a variable for measuring drowsiness. Dingus, Hardee and Wierwille
(1987) found that PERCLOS correlated better with driver impairment than other
measures of eyelid closures that were examined. The use of eyelid closures as
an indicator of drowsiness stems from the general observation that as people
get drowsy they close their eyes more frequently and during longer periods,
until they finally fall asleep. However, in an experiment by Wierwille, Lewin
and Fairbanks (1996) it was found that PERCLOS did not predict drifting off the
road very well. Since measures of eyelid closure were not sensitive enough,
they advised to monitor lane position as well in order to detect driver
impairment.
A
number of other studies have focussed on driver behaviour instead of driver
state. According to Bishop, Madnick, Walter and Sussman (1985), steering
activity becomes more coarse when driving for long periods of time: the number
of large steering movements increases while the number of smaller steering
amplitudes decreases. Seko, Kataoka and Senoo (1985) also found that with
reduced alertness caused by drowsiness, the number of steering corrections with
large amplitudes increases. This suggests that with progressing drowsiness the
number of lapses of attention increases resulting in longer periods during
which there are no steering corrections. This would result in drifting to the
edge of the lane which is corrected by a larger steering amplitude. This view
is consistent with the theory of ‘blocking’ as proposed by Bertelson and Joffe
(1963). They found that with progressing fatigue the occurrence of ‘blocks’ of
large reaction times increases. After a ‘block’, performance returns to normal
for a while. A blocking may express itself as a failure to commit a correcting
steering action in time which results in smaller safety margins to the lane
boundary, crossing the lane boundary or in moving off the road. In that case
the occurrence of an error (i.e. a smaller safety margin or a lane boundary
exceedance) is not the only indication of impaired performance because of
drowsiness. Also the increase in the number of large steering amplitudes is an
indication of error correction in response to a larger error. This error
correction response then evidences progressing impairment. Error correction may
then be defined as turning the steering wheel in the opposite direction, with a
large peak-to-peak amplitude, when the driver notices that the lane boundary is
about to be crossed or has been crossed. Error corrections prevent accidents to
occur and they may partly prevent effects of drowsiness on measures such as the
standard deviation of the lateral position (SDLP) or exceedance of the lane
boundaries. From this perspective it may be that effects on steering
amplitude-related measures as indicators of error correction show up earlier
than effects on lane position related variables. However, with progressing
drowsiness, error corrections may come too late resulting in increased swerving
or running off the road.
This
reasoning assumes that safety margins to the lane boundary are perceived by the
driver and acted on by a correcting steering action. This principle has been
demonstrated by Godthelp (1988) in a study where drivers were instructed to
generate correcting steering actions when vehicle heading could still be
corrected comfortably to prevent a crossing of the lane boundary. Safety
margins were defined by the concept of Time-to-Line Crossing (TLC). This
represents the time available until any part of the vehicle reaches one of the
lane boundaries. This coupling of perception and action has also been
demonstrated for driving in curves by Van Winsum and Godthelp (1996) and for
the way drivers change lanes by Van Winsum (in press).
Another group of
studies has focussed on the effects of fatigue on driver performance instead of
psychophysiological measures and measures of driver control behaviour. The
standard deviation of the lateral position (SDLP) and exceedance of the lane
boundaries are generally referred to as indicators of the quality of driving
behaviour and, thus, driver performance. It has been found that SDLP increases
with time on task (Riemersma, Sanders, Wildervanck & Gaillard, 1977; De
Waard & Brookhuis, 1991). SDLP measures swerving and control over lateral
position. Another measure of driver performance is the proportion of time that
any part of the vehicle exceeds the lane boundary. This has been referred to as
‘LANEX’ by Wierwille, Lewin and Fairbanks (1996). LANEX is a strong indicator
of driver impairment. The minima of TLC are used in the present study as an
additional measure of driver performance. These minima represent the safety
margins to the lane boundaries that are maintained by the driver. Smaller TLC
minima indicate poorer lateral control and suggest poorer driver performance
and progressing impairment.
In summary, three
types of variables are used in the present study. Variables that measure driver performance are LANEX, SDLP and
TLC minima. Other variables measure drowsiness.
These are related to eyelid closures, especially PERCLOS (i.e. the percentage
of time that both eyes are closed). The third type of variable relates to driver behaviour or more specifically to
steering behaviour of the driver.
The experiment
was performed in the driving simulator of the TNO Human Factors Research
Institute.
2.
METHOD
Eighteen
paid subjects participated in the experiment. Age ranged between 24 and 70
years. All subjects had held their
drivers licence for more than 5 years and the annual kilometrage exceeded 5000
km. The experiment was performed in the driving simulator of the TNO Human
Factors Research Institute, described in detail in Hogema and Hoekstra (1998),
and Hoekstra, Van der Horst and Kaptein (1997).
For
the detection of eyelid closures, EOG was measured for both eyes with
electrodes attached just above and below each eye in line with the pupil. By
this procedure the signal is affected only by artefacts caused by vertical eye
movements and not by eye movements in the horizontal plane. The electrodes were
connected to a physiological amplifier with a timeconstant of 10 s. The
amplifier gave an output between –5 and +5 Volts. This was fed directly into
the A/D converter of the simulator computer, where it was sampled and stored
together with the driver behaviour data. This ensured a fixed synchronization
in time between all signals.
All subjects were
informed about the purpose of the experiment and that it took three hours of
continuous driving. Subjects were free to stop driving at any moment without
negative consequences. They were requested to stop if they were feeling
uncomfortable or if they were too tired to continue to drive safely. After the
instructions, the electrodes for EOG measurement were attached and the signal
was tested with an oscilloscope. During the drive, speed was controlled by a
cruise control that was set at a constant speed of 80 km/h. This was expected
to facilitate the occurrence of drowsiness because of the relatively low speed
in a visually boring environment. All subjects drove continuously for three
hours during the daytime on a monotonous road under simulated evening lighting
conditions. No other traffic was encountered. The road was a standard two-lane
road with a lane width of 3.1 m, broken centerline and continuous edgelines. It
consisted of straight and curved segments with a continuous horizontal radius
of 2000 m that turned either to the left or to the right over an angle of 45ยบ.
Subjects were instructed to drive in the right lane without exceeding the lane
boundaries. A mild side wind with varying force was simulated in order to
necessitate a minimum amount of steering effort.
Coordinate
positions were stored as well as steering wheel angle, lateral position and EOG
recordings of both eyes with a frequency of 10 Hz. Time-to-Line Crossing (TLC)
was computed off-line according to the method described in Van Winsum,
Brookhuis and De Waard (in press). Time-on-task (TOT), i.e. the effect of
progressing time that is assumed to result in fatigue and drowsiness, was
treated as a within-subjects factor as follows. The first 20 minutes of each
run was not analyzed since this period was used to familiarize the subjects
with driving in the simulator. After this, the remaining time was divided into
5 sequential blocks of equal duration. Usually these blocks each covered a
period of 32 minutes. This means that there are 5 TOT blocks. Effects of
fatigue and drowsiness on task performance are expressed as an effect of TOT.
To evaluate time-on-task effects the dependent variables were averaged over
each block and divided by the average value for the first block where
appropriate. In this way all variables have the same units and can be compared
directly in terms of the sensitivity to TOT effects. If it is assumed that
drowsiness increases with increasing time on task, then the variables with the
strongest statistical effect of TOT are most useful as indicators of
drowsiness. This procedure of dividing the average by the data of the first
time block can only be used if the data on the first block can never be zero.
Therefore, not all variables are suitable for treatment by this procedure.
EOG data were
filtered off-line and the filtered signal was subtracted from the raw EOG
signal to allow peak detection analysis by a computer program that detected
eyeblinks and eyelid closures. These were transformed into the following
indicators of drowsiness:
-
PERCLOS, i.e. the fraction of time during which both
eyes are closed
-
BLINK, i.e. the blinkfrequency
For each TOT block, the average value was
divided by the value of the first block.
The following
indicators of driver performance were computed:
-
LANEX, i.e. the fraction of time during which any of
the wheels exceeds the right lane boundary.
-
SDLP, i.e. the standard deviation of lateral position,
with respect to the first block.
TLC minima to the left and
right lane boundaries were determined and only minima of less than 20 s were
analyzed. These minima were used to compute:
-
TLC1.0, i.e. the percentage of TLC minima
smaller than 1.0 s.
The following
indicators of steering behaviour were computed:
-
SDST, i.e. the standard deviation of steering wheel angle,
with respect to the first block,
-
P3-6, i.e. the power of fast steering movements (in
the domain of 0.3-0.6 Hz) as a fraction of all steering activity < 0.6 Hz,
with respect to the first block.
-
STAMP, i.e. the average of peak-to-peak steering
amplitudes, with respect to the first block,
-
STDIS, i.e. the fraction of larger peak-to-peak
steering amplitudes. This was computed as follows: for the first block the
distribution of all peak-to-peak steering amplitudes was computed and the 80th
percentile value (i.e. that value for which 80% of all values are smaller and
20% of all values are larger) was determined. Then, for all subsequent time
blocks the percentage of values that was larger than the initial 80th
percentile value was computed and divided by 20 (i.e. the percentage larger
than the value in the first block). Figure 1 gives a graphical illustration of
this principle. There are two bell-shaped distributions of peak-to-peak
steering amplitudes. The left distribution refers to the first block, while the
right distribution represents block i. The idea then is that as drowsiness
progresses, the distribution of steering amplitudes shifts towards larger
values (to the right). The vertical line represents the value of steering
amplitudes, during the first block, that separates the 20% largest values from
the 80% lowest values. The sum of the striped and black area represents the
percentage of values that is larger than P80 of the first block. STDIS then is
the sum of the black and striped area divided by the black area.
The following
types of analyses were conducted:
1)
The effect of time on task was tested with analysis
of variance using a within-subjects repeated measurement design. The aim of
these analyses was to evaluate the relative sensitivity of the dependent
variables to effects of drowsiness.
2) For
each block the magnitude of the TLC minima was related to the correcting
steering wheel action in the opposite direction. This was realized as follows.
TLC minima to the right lane boundary result in a path-correcting turning of
the steering wheel to the left, while TLC minima to the left lane boundary
result in a path-correcting turning of the steering wheel to the right. All TLC
minima to either the left or the right were detected together with the accompanying
correcting peak-to-peak steering amplitude to the opposite direction. Then the
TLC minima were categorized into groups according to the magnitude of the TLC
minima. For testing the relation between TLC minima and steering corrections,
the following groups were distinguished:
1 =TLC minima >0.0 and <=1.5 s; 2 =TLC minima >1.5 and <=3.0 s
3 =TLC minima >3.0 and <=4.5 s; 4 =TLC minima >4.5 and <=6.0 s
5 =TLC minima >6.0 and <=7.5 s; 6 =TLC minima >7.5 and <=9.0 s
7 =TLC minima >9.0 and <=10.5 s; 8 =TLC minima >10.5 and <=12.0 s
9 =TLC minima >12.0 and <=13.5 s; 10 =TLC
minima >13.5 and <=15.0 s
A smaller TLC minimum can be considered as a
larger error that is compensated by a larger steering correction (larger
peak-to-peak steering amplitude in the opposite direction). Analyses of
variance were applied to test whether the sensitivity of the steering response
to TLC information changes as a function of time on task.
Figure 1. Distributions of peak-to-peak
steering amplitudes. STDIS is computed as the sum of the black and striped area
divided by the black area.
3. RESULTS
Table 1 gives an
overview of the effects of time on task on the different dependent variables.
It can be seen that all variables related to driver performance, drowsiness and
steering behaviour are strongly affected by time of driving: performance
deteriorates with increased driving time, and drivers become more drowsy while
their steering becomes more coarse. The steering amplitude related variables,
i.e. STDIS and STAMP, have the largest effect of time on task. This means that
the effect of time-on-task on these variables is the most reliable, as
indicated by the F-statistic. When comparing STDIS and STAMP, the effect size
of time-on-task is the largest for STDIS. This can also be seen in figure 2.
These results indicate that, of all variables examined in the present
experiment, the fraction of large peak-to-peak steering amplitudes (STDIS) is
the best indicator of drowsiness-related driving impairment.
Table 1. Effects of time on task on the
dependent variables (F-statistics), together with average values
Time on task block
|
|||||||
Type
|
Dependent variable
|
Effect of time on task df=68,4
|
1
|
2
|
3
|
4
|
5
|
Driver
performance
|
|||||||
LANEX
|
8.81 **
|
0.02
|
0.05
|
0.06
|
0.08
|
0.08
|
|
TLC1.0
|
7.34 **
|
4.48
|
6.33
|
6.14
|
6.78
|
7.14
|
|
SDLP relative
|
8.30 **
|
1.00
|
1.17
|
1.29
|
1.31
|
1.38
|
|
Drowsiness |
|||||||
PERCLOS
|
5.90 **
|
1.00
|
1.24
|
1.63
|
1.91
|
2.14
|
|
BLINK
|
9.51 **
|
1.00
|
1.20
|
1.43
|
1.48
|
1.58
|
|
Steering
behaviour
|
|||||||
STDIS
|
14.60 **
|
1.0
|
1.38
|
1.62
|
1.77
|
1.83
|
|
STAMP
|
15.25 **
|
1.00
|
1.09
|
1.18
|
1.22
|
1.25
|
|
SDST
|
11.49 **
|
1.00
|
1.09
|
1.11
|
1.20
|
1.20
|
|
P3_6
|
9.00 **
|
1.00
|
1.04
|
1.08
|
1.13
|
1.12
|
|
** p < .001
From
the previous analyses it appeared that with progressing drowsiness driver
errors increased which resulted in more frequent crossing of the lane
boundaries and smaller TLC minima. This may explain the shifting to larger
steering corrections with time-on-task, since larger errors may be corrected by
larger corrections. In order to test this, the magnitude of the TLC minima was
related to the correcting steering wheel action in the opposite direction for
each block according to the procedure described in paragraph 2. Analyses of
variance were applied to test whether the sensitivity of the steering response
to TLC information changes as a function of time on task. In these analyses the
time-on-task effects of block 1 vs block 5 were tested. The effect of
time-on-task on the amplitude of the steering corrections was significant
(F(17,1)=30.43, p<.001), while the effect of TLC minimum on the amplitude of
the corrective steering actions was significant as well (F(153, 9)=135.89,
p<.001). This is illustrated in figure 3.
Figure 2. Variables related to steering
behaviour as a function of time on task.
The results show
that the magnitude of the correcting steering action is strongly related to the
magnitude of the error, since smaller TLC minima are associated with larger
correction peak-to-peak steering amplitudes in the opposite direction. However,
in addition the effect of time-on-task of steering amplitude is highly
significant, despite the fact that it has been controlled for TLC minima. This
means that although the increase in larger steering corrections with
time-on-task can partly be explained by the increase in errors (small TLC minima)
with progressing time-on-task, there still is a substantial increase in the
magnitude of peak-to-peak steering corrections which cannot be explained by
larger errors. This suggests that as drowsiness increases, steering reactions
to a movement of the car towards the lane boundaries become larger.
Figure 3. Peak-to-peak
steering amplitude as a function of TLC minima for the first and the last
time-on-task blocks.
4. CONCLUSIONS AND DISCUSSION
In an experiment
performed in the TNO driving simulator the effects of drowsiness on several
measures of driving performance, drowsiness and steering behaviour were
studied. The method used was prolonged driving under monotonous environmental
conditions. The results reveal significant effects of time-on-task on variables
that measure drowsiness. These variables were based on eyelid closures derived
from EOG measurements. Both the fractions of time during which both eyes were
closed (PERCLOS) and the frequency of eyeblinks (BLINK) were significantly
affected by time-on-task. This suggests that the experimental setup resulted in
the desired effect of inducing drowsiness in the subjects. In accordance with
the literature, PERCLOS appears to be a valid indicator of drowsiness. This
variable is easy to compute from the data of an eyelid monitor and may be a
useful variable for driving impairment detection systems.
Driver
performance was measured by means of variables that were derived from lateral
position. Of all performance-related variables, the fraction of time during
which any part of the vehicle exceeds one of the lane boundaries (LANEX) was
the most sensitive to effects of time-on-task. Because of this, it is
recommended to include this variable in systems that are aimed at driver
impairment detection. Also, in accordance with the literature, the standard
deviation of lateral position (SDLP) was significantly affected by time-on-task
as were the TLC minima.
The results of
the steering related variables show that the fraction of large peak-to-peak
steering amplitudes that are larger than the 80th percentile value
during the initial period of driving were the most sensitive to the effects of
time-on-task. This indicates that the distribution of steering amplitudes
shifts towards larger values with progressing time. This variable appears to be
more sensitive than all other variables that were measured in this experiment,
driver performance and drowsiness-related variables included. It is therefore
recommended to include this variable in systems for detecting drowsiness-related
driver impairment.
In a final
analysis it was evaluated how the relation between the magnitude of the errors,
measured by the TLC minima, and the magnitude of the correcting steering wheel
movements to the opposite direction was affected by time-on-task. The shift to
larger steering corrections with progressing time-on-task may partly be
explained by the larger errors that are committed when drowsiness increases. It
appeared that the amplitude of the correcting steering response is indeed
strongly related to the momentary TLC minimum: a smaller TLC minimum is
accompanied by a larger steering correction in the opposite direction. However,
when corrected for the level of the TLC minima, steering corrections still
increase significantly with time-on-task. This cannot be explained by larger
errors with progressing fatigue. The results suggest that the larger steering
corrections that occur with higher levels of drowsiness are both the result of
larger errors and of increased coarseness of the steering responses. The
mechanism responsible for this effect is unclear. A possible explanation may be
that with progressing drowsiness drivers tend to look at a point on the road
closer in front in an attempt to reduce visual input. This may then result in
poorer lateral control. There is experimental evidence for the idea that
lateral control performance deteriorates when the driver has less preview, as
is the case when the driver looks at a point closer in front of the vehicle.
For example, Tenkink (1988) studied the effects of sight distances of 27, 37
and 183 meters on lateral control performance. He found that a smaller sight
distance resulted in a larger SDLP at a given speed. The hypothesis that drowsy
drivers look at a point closer in front of the vehicle is consistent with the
results of Kaluger and Smith (1970). They found, in a study of driver fatigue,
that drivers looked closer in front of the vehicle after several hours of
driving. Mourant and Rockwell (1972) have described this compensating strategy
of fatigued drivers as a regression towards the visual scan behaviour of novice
drivers, who also are characterized by looking closer in front of the car
compared to experienced drivers. Alternatively, this change in visual scanning
strategy may be an attempt to reduce the amount and complexity of visual input.
However, this hypothesis needs to be tested in further research.
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