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80
Transportation Research Record: Journal of the Transportation Research Board,
No. 2018, Transportation Research Board of the National Academies, Washington,
D.C., 2007, pp. 80–86.
DOI: 10.3141/2018-11
(e) efficacy of novice (6, 7) and older driver training programs. Driv-
ing simulators are used most often when considerations of the safety
of participants is paramount. Contrary to studies in the field, on a sim-
ulator participants can be put in scenarios that do not compromise
their safety or the safety of other drivers.
The focus in this paper will be on the generalizability to the field of
simulator studies in which the driver is exposed to potential hazards
and eye movements are used as an index of drivers’ hazard anticipa-
tion and perception skills. Three examples of simulator studies in
which eye movements are used as a window into hazard anticipation
are discussed below.
First, eye movements were measured in a recent study of the
effect of additional cognitive load (such as cell phone conversations)
on drivers’ scanning patterns (8, 9). Drivers were asked to navigate
a virtual world and in the experimental conditions to simultaneously
execute tasks that increased either their verbal or spatial load. Results
indicated that the mean saccade distance is reduced by about 23%
for the verbal task and 33% for the spatial task when compared with
the control task (no additional load). Such simulator studies may
soon affect legislatures that must decide whether to ban cell phones
outright or to restrict their use in certain highly dangerous situations,
for example, work zones.
Second, eye movements have been used to determine whether older
drivers look appropriately for oncoming vehicles in scenarios in which
such vehicles may be hidden until the last several seconds before the
driver makes a turn (10). For example, in one scenario presented to
drivers in Romoser et al. (10), the participant driver approached a
T-intersection andwas instructedtoturn tothe rightat theintersection.
Just after the participant driver initiated the turn, a car rounded a
corner or came up over a hill from the left, potentially colliding with
the participant driver if he or she did not speed up appropriately. Con-
sistent with crash statistics, it was found that the older drivers made
the appropriate eye movements less often than younger drivers. Such
studies could eventually be central to the determination of whether
older drives are fit to drive.
Third, eye movements have been used to evaluate whether novice
drivers trained with a PC-based hazard anticipation program would
look for information in areas of the roadway that should reduce the
likelihood of a crash (11). For example, in one scenario presented to
drivers in Pollatsek et al. (11), a truck is parked in front of a cross-
walk (the truck crosswalk scenario, Figure 1). A pedestrian could
potentially emerge from behind the truck. The participant driver
should look to the right as he or she passes the truck. Indeed, the
trained drivers were more likely to do so. Studies such as these are
already changing the face of driver education.
Empirical Evaluation of Hazard Anticipation
Behaviors in the Field and on
Driving Simulator Using Eye Tracker
Donald L. Fisher, Anuj K. Pradhan, Alexander Pollatsek,
and Michael A. Knodler, Jr.
Eye behaviors have been used with driving simulators to evaluate the
effectiveness of novice and older driver training programs. Driving sim-
ulators are often favored when drivers must be placed in risky situa-
tions. Because there was no study of whether eye behaviors observed on
a driving simulator in risky scenarios were also observed in the field, the
authors had both trained and untrained novice drivers maneuver a con-
trolled set of 10 scenarios on a driving simulator. The scenarios were
similar to a set of scenarios that a different, matched set of trained and
untrained drivers had navigated in the field. Drivers in this simulator
study were trained with the same PC program used by drivers in the
field study. Five of the scenarios that the trained drivers saw on the sim-
ulator and in the field were similar to those seen in training on a PC
(near transfer); the other five were similar in concept to those in train-
ing but different in surface features (far transfer). A fixation on the
region of a scenario that had information relevant to identifying a risk
was scored as recognizing the risk. On the simulator, trained drivers
recognized the risk 41.7% more often than untrained drivers in the
near-transfer scenarios and 32.6% more often in the far-transfer scenar-
ios. In the field, trained drivers recognized the risk 38.8% more often
in the near-transfer and 20.1% more often in the far-transfer scenar-
ios. Both effects were highly significant, and the difference between
them was not close to significant. Thus results from tests on a simula-
tor have a close correspondence with those in the field.
Driving simulator scenarios are becoming key elements in state and
federal efforts to probe (a) how using electronic devices in the car
affects drivers’ situation awareness, especially as that affects their
safety (1); (b) whether patients with performance decline (e.g., aging,
mild cognitive impairment, traumatic brain injury, neurodegenerative
disorders such as Alzheimer’s and Parkinson’s diseases, or sleep dis-
turbances) are fit to drive (2, 3); (c) acute and chronic effects of many
medications such as analgesics, antidepressants, psychostimulants,
antidepressants, and cancer chemotherapy agents (4); (d) effective-
ness of alternative signs, signals, and pavement markings (5); and
D. L. Fisher and A. K. Pradhan, Department of Mechanical and Industrial Engineering,
220 Engineering Laboratory, 160 Governor’s Drive; A. Pollatsek, Department of
Psychology, Tobin Hall, 135 Hicks Way; and M. A. Knodler, Jr., Department of
Civil and Environmental Engineering, 216 Marston Hall, 130 Natural Resources
Road, University of Massachusetts, Amherst, MA 01003. Corresponding author:
A. K. Pradhan, apradhan@ecs.umass.edu.
Fisher, Pradhan, Pollatsek, and Knodler 81
In summary, results from studies such as those above are finding
their way into state (12) and national policies (13), federal guide-
lines (14), and business (15) and foundation (16) efforts. Yet in none
of these studies on the driving simulator in which the scenarios were
potentially hazardous has it been confirmed that participants would
behave similarly on the open road. Perhaps participants are simply
less vigilant in the driving simulator because hazards cannot harm
them there. Thus, for example, in the truck crosswalk scenario (Fig-
ure 1) the participant in a simulator experiment may not be as vigi-
lant as he or she might be on the open road because there would be
no real-world consequences if a simulated pedestrian emerged from
behind the truck.
At this point it is critical to know whether the eye behaviors of
drivers in the simulator in a potentially hazardous scenario closely
resemble the behaviors that one would observe on the open road in a
similarly hazardous scenario. Obviously it is difficult to collect infor-
mation on all hazardous scenarios in both environments. Still, there
are scenarios that allow for the collection of relevant data. Given such
data, there are then two ways to approach the issue of generalization.
First, one can ask whether the relevant dependent variables on the
simulator and in the field are absolutely identical. In this case, the val-
ues of the relevant dependent variable will be eye behaviors. Second,
when evaluating the efficacy of training programs (or devices designed
to make driving safer), one can ask whether the difference between
the simulator results and the field results is similar. One can ask both
questions overall (average over all the different scenarios) as well as
on a scenario-by-scenario basis.
A study on the driving simulator is now being undertaken that
replicates, in part, a study that was previously undertaken by the
authors in the field. Specifically, 10 of the scenarios that were built
for the driving simulator for the study and reported below are sim-
ilar to (but not identical to) the situations to which drivers were
exposed in the field (17–19). In both experiments trained and
untrained drivers between the ages of 18 and 21 were evaluated pri-
marily by measuring the percentage of scenarios in which each
driver looked at a critical area: an area that had information rele-
vant to reducing the likelihood of a crash. In each study, the trained
and untrained drivers were compared on these measures. In the fol-
lowing there will be (a) a brief description of the experimental
design of the field study, (b) a fuller description of the design of the
simulator study, and then (c) a comparison of the findings of the two
studies.
FIELD STUDY: METHOD
The field study is described in depth by Pradhan et al. (19); briefer dis-
cussions are provided by Fisher et al. (17) and Pollatsek et al. (18).
The most important details are discussed below.
Participants
The 24 participants were all recruited from the student body of the Uni-
versity of Massachusetts–Amherst campus. They were between 18 and
21 years old and all had held a valid U.S. driver’s license for at least
1 year. The 12 male and 12 female participants were separately, ran-
domly assigned to the trained group or the untrained group, so that
there were six male and six female participants in each group. Because
of difficulties with the eye-tracker calibration, it was not possible to
recruit participants wearing eyeglasses; thus all participants either had
normal vision or vision corrected to normal with contact lenses. The
mean ages of the control group and the experimental group were 19.27
and 19.87, respectively, with standard deviations of 0.64 and 0.75.
Design
Training Program
Participants in the experimental group were trained on PCs using Ver-
sion 3 of the Risk Awareness and Perception Training (RAPT-3) pro-
gram developed at the University of Massachusetts Amherst. RAPT-3
was designed to illustrate different categories of not-so-obviously
hazardous scenarios and to train drivers to focus their attention on crit-
ical regions that, if scanned, reduced the likelihood of a crash. The
training program contained nine driving scenarios, in each of which
there was an inherent risk of a collision with another vehicle or
pedestrian. RAPT-3 is described in more detail in the discussion of
the method used for the simulator study.
Field Driving Route
The route driven by participants was a 16-mi course plotted in and
around Amherst, Massachusetts, that included major arterials; a vari-
ety of intersections; and covered rural, residential, city, and highway
driving situations. It was designed to include 10 situations of interest
(scenarios) that would be analyzed. They were all embedded natu-
rally in the driving course so that the participant had no indication
that these were the primary areas of interest to the researchers. Five
of the open road scenarios, the near-transfer scenarios, were similar
in concept to five of the scenarios that were in the RAPT-3 training
program; the remaining five open road scenarios, the far-transfer
scenarios, were different from the scenarios that were seen during
training. Twelve measures were extracted from 10 scenarios, five from
the near-transfer and seven from the far-transfer scenarios.
Apparatus
A portable lightweight eye tracker (Mobile Eye developed by Applied
Science Laboratories) was used to collect the eye-movement data for
each driver during the on-road drives. It has a lightweight optical sys-
tem consisting of an eye camera and a color scene camera mounted
on a pair of safety goggles (see Figure 2). The images from these two
Participant
Driver
FIGURE 1 Plan view of truck parked in
front of crosswalk scenario. (Pedestrian
may emerge from behind front of truck
parked on right shoulder. Participant drivers
should look to right as they pass truck.)
82 Transportation Research Record 2018
cameras are interleaved and recorded on a remote recording system,
thus ensuring no loss of resolution. The interleaved video can then be
transferred to a PC on which the images are separated and processed.
The eye movement data are converted to a crosshair, representing the
driver’s point of gaze, which is superimposed on the scene video
recorded during the drive. This provides a record of the driver’s point
of gaze on the driving scene while on the on-road driving course. The
remote recording system is battery-powered and capable of recording
up to 90 min of eye and scene information at 60 Hz in a single trial.
Each participant drove the field test with a four-door sedan with
automatic transmission (a 2002 Chevy Prizm or a 2000 Chevrolet
Cavalier). The vehicles were rented from a local area driving school
and had a secondary braking system that could be operated by a cer-
tified driving instructor (who was sitting in the front passenger seat
solely for safety reasons).
Procedure
The RAPT-3 training program took about 30 to 45 min to complete.
Participants in the control group did not take part in the training pro-
gram. The trained and the control drivers were fitted with the eye
tracker and the necessary calibration process was carried out, which
took about 5 min. The participant then drove through the course with
the driving instructor in the front passenger seat and a researcher in the
backseat. The researcher provided the participant with directional
information at appropriate points in the course. The drive through the
entire course took about 45 to 55 min to complete. To control for time-
of-day effects and traffic conditions, the drives were all at 9 a.m. or
10 a.m. on weekdays. The eye-tracking system recorded the point of
gaze data, which served as the primary dependent measure, along
with a video record of the driver’s view of the roadway during the
entire drive.
SIMULATOR EXPERIMENT: METHOD
The simulator experiment used the same PC training program as
the field experiment (RAPT-3). Furthermore, the simulator test
(which had 18 scenarios) contained 10 scenarios that were similar
to the 10 field scenarios, five of which were classified as near-
transfer simulator scenarios and five of which were classified as
far-transfer simulator scenarios. Details follow.
Participants
A total of 12 participants, six men and six women, were all recruited
from the student body of the University of Massachusetts–Amherst
campus. They were between 18 and 21 years of age and all had held
a valid U.S. driver’s license for at least 1 year. Equal numbers of men
and women were assigned to the trained and untrained groups. Again,
because of difficulties with the eye-tracker calibration, no participants
were recruited who wore eyeglasses. The mean ages of the control
and the experimental group were 20.28 and 19.71, respectively, with
standard deviations of 0.57 and 0.85.
Equipment: Driving Simulator and Eye Tracker
An advanced fixed-base driving simulator was used for the driver
evaluation. The simulator has a fully equipped 1995 Saturn sedan
placed in front of three screens on which the virtual environment is pro-
jected. The screens subtend 135 degrees horizontally, and the virtual
world is displayed on each screen at a resolution of 1,024 ×768 pixels
at a frequency of 60 Hz (see Figure 3). Participants sit in the car and
operate the controls, moving through the virtual world according to
their inputs to the car. The sound is controlled by another computer,
the Acoustetron, which consists of two mid- to high-frequency
speakers located on the left and right sides of the car and two sub-
woofers located under the hood of the car. The system provides real-
istic road, wind, and other vehicle noises with appropriate direction,
intensity, and Doppler shift. The same eye tracker was used in this
study as was used in the field study.
Experimental Design and Procedure
Training Program
As in the field study, participants in the simulator study were trained
with RAPT-3. Here details of the training program are discussed in
greater depth. Recall that RAPT-3 contained nine scenarios, each of
which included a potential risk. In the first set of risks, the risks were
due to vehicles or pedestrians being hidden from view until the last
moment, either because of the geometry of the roadway or the pres-
ence of an obscuring vehicle or object. In the second set, risks were
due to visible elements, either cars that plausibly might change lanes
Safety Glasses
Scene Camera
Optics
Reflecting Mirror
FIGURE 2 ASL mobile eye tracker.
FIGURE 3 University of Massachusetts–Amherst driving simulator.
Fisher, Pradhan, Pollatsek, and Knodler 83
abruptly or pedestrians that might enter a crosswalk suddenly and
cause a lead car to brake suddenly. The scenarios were selected from
a set used in previous studies (11, 17), but because perspective
views had to be photographed, safety issues made it necessary to
select only those that did not directly involve any moving vehicle as
the inherent risk in a scenario. In addition, to portray several of the
scenarios accurately, some staging with other vehicles was necessary
so that all elements in the scenario would appear in the snapshots.
The hidden sidewalk scenario described below should illustrate the
general idea of the training.
In this scenario, the driver is approaching an intersection with a
stop sign. There is a pedestrian crosswalk at the intersection located
after the stop line. The stop line and crosswalk are themselves rela-
tively distant from the intersection with the road on which cross traf-
fic travels. On the right just beyond the stop line there is a high hedge
that hides a sidewalk that emerges onto the crosswalk (see Figure 4).
The risk is that a bicyclist or a pedestrian, hidden behind the hedge,
could potentially emerge onto the crosswalk. The scenario is one that
is difficult for drivers to predict as hazardous. When the test course
was set up, this particular test intersection in downtown Amherst was
studied. Of the 20 drivers observed, all failed to stop at the stop line
and look to the right as they passed by the bushes, instead proceeding
directly up to the boundary with the crossroad.
The training program (RAPT-3) started with instructions and an
initial practice section to familiarize participants with the displays and
the tasks they were to perform. That was followed by the three main
sections of the training: pretest, training, and posttest. In the pretest
each scenario was presented as a sequence of snapshots displaying
the driver’s view from a vehicle traversing through a particular
driving situation. A scenario contained five to 12 snapshots depend-
ing on the length and complexity of the situation (a snapshot used to
train the hidden sidewalk scenario is presented in Figure 5). Each
snapshot was displayed for 3 s. Participants used the mouse to click
on areas of each snapshot in which they would have to pay particular
attention if they were actually driving through the scenario (see red
circles in Figure 5). The coordinates of the click and response time for
it were internally recorded by the program. In this section, participants
received no feedback on their performance.
Generally, the snapshots were straight ahead from the car, but in
situations in which it was necessary for a driver to look to the left
or the right (e.g., at an intersection), the participant could click on
buttons provided on the left or right margins of the snapshot, which
would show the corresponding left or right views. The side views
materialized only for situations in which the driver would need to
have a view of the left or right; in other cases, the side view buttons
were still displayed but clicking on them did not change the view.
The training came next. The user was first shown a top-down
schematic view of a scenario accompanied by explanations about the
risky aspects of the particular scenario. For example, the explanation
that accompanied Figure 4 appears below:
This is an example of a situation in which a potential risk is obscured
by bushes. In this scenario, there is a crosswalk as indicated by the pave-
ment striping. On the left, you can easily see approaching pedestrians
or bicyclists. On the right, however, any approaching pedestrian or bicy-
clist is hidden by the bushes.
1. It is clear from the diagram that when you are at Position 1 you
cannot see to the right. As you approach the stop sign, you, the driver,
FIGURE 4 RAPT-3 training program for hidden sidewalk scenario.
1
2
FIGURE 5 Hidden sidewalk snapshot.
84 Transportation Research Record 2018
should glance frequently at the area where the crosswalk/sidewalk dis-
appears behind the bushes so that you can slow immediately if some-
one were to appear in the crosswalk or sidewalk.
2. When you are passing the actual crosswalk, you should look far
to the right, turning your head if necessary. A bicyclist riding fast might
not see you and could be at risk if you were to accelerate through the
crosswalk without looking to the right.
After these explanations, the user was presented again with the
sequence of perspective view snapshots for that scenario and was given
up to four opportunities to correctly identify the areas of risk on the
sequence of snapshots using the mouse. If the user could successfully
identify the areas, then the program moved on to the next scenario. If
not, the user was taken back to the training part of the scenario with the
schematic view and corresponding explanations (until the fourth time,
after which the next scenario was automatically presented).
Finally, in the posttest section, the user was once again presented
with the nine sequences of photographs and asked to use mouse
clicks to identify areas of potential risk. As in the pretest, the click
coordinates and response times were recorded for this section and
no feedback was provided to the user. (The response times were not
used in the scoring, however.)
The training program was presented on a laptop computer running
Microsoft Windows XP, and a mouse was used as the pointing device.
The program was developed using Macromedia Director and was
designed to operate on any Microsoft Windows operating PC.
Although the program was a single executable file and can be
deployed on CD-ROMs or over the Internet, it was administered on
the same computer in the driving laboratory to all trained participants.
Driving Simulation
Participants in both groups were evaluated on the simulator. They
were given written instructions and verbal instructions with respect
to driving in the simulator at the beginning of the session. They were
then fitted with the eye-tracking system, and the calibration process
was completed. Once the eye tracker had been calibrated, drivers
were asked to drive a practice scenario. This scenario was designed
to contain all elements of the virtual environment that a driver would
experience during the actual experimental scenarios, including
intersections, traffic situations, and other elements. This practice
scenario also served to accustom the participant to the specific
handling characteristics of the vehicle used in the driving simulator.
Participants were encouraged to drive the practice scenario as many
times as necessary until they were comfortable with the handling of
the vehicle, especially left and right turns and braking situations.
There were 18 experimental scenarios in the driving simulation.
These scenarios were laid out in three blocks of six scenarios each. The
blocks were counterbalanced evenly within the groups. The 18 sce-
narios were composed of nine of the scenarios that were used for the
PC training, that is, the near-transfer scenarios, and nine other scenar-
ios that were different from those used in the training, the far-transfer
scenarios. The far-transfer scenarios were used to test for possible gen-
eralization of the PC training. The databases were designed such that
each block contained three near-transfer and three far-transfer scenar-
ios. Participants drove through the three blocks with a rest between
blocks. During the entire drive, eye movements of participants and
various vehicle parameters were constantly being recorded.
Of the 18 scenarios that were presented to participants in the
driving simulator, 10 of the scenarios were similar to the scenarios
in the field study, five near-transfer and five far-transfer. The scenar-
ios were all embedded in the three blocks with stretches of regular
driving between scenarios. The experimental scenarios were all
located and designed in such a manner that it would not be obvi-
ous to participants that they were traversing an area of interest to the
researcher.
RESULTS: SIMULATOR AND FIELD
The key data in both studies were whether a participant fixated on
the appropriate region during a scenario within an appropriate time
window (e.g., whether a participant fixated on the region of the cir-
cle on the right in the scenario shown in Figure 5 in a time window
such that there would be adequate time to react to the presence of
a hazard if it appeared). The data (i.e., the record of the crosshair
indicating the fixation point against the view of the scene) were
scored by several independent raters who were “blind” to which
group the participant was in. There were few “judgment calls”
because the difference between fixating appropriately and contin-
uing to fixate straight ahead was usually close to 10 degrees of
visual angle.
Averaged over all 18 scenarios in the new simulator study, there
was a 37.4% overall effect of the RAPT-3 training, t(22) =4.12,
p< .001 (see Table 1). The training effect was 42.2% for near trans-
fer and 32.6% for far transfer, t(22) =4.88, p< .001, and t(22) =2.88,
p< .01, respectively. Although the training effect appeared to be
somewhat larger for the near-transfer scenarios, the 9.6% difference
between the two training effects was far from significant, t(22) =1.06,
p> .10. These training effects were somewhat larger than those
observed in the field study; the overall training effect there was
27.1%, and the near- and far-transfer effects were 38.8% and 20.1%,
respectively. All three training effects in the field study were also sig-
nificant, and again, although the near-transfer effect was larger than
the far-transfer effect, the difference was only marginally significant
(p< .10).
To summarize the above, results of the two studies appear to be
quite similar. However, the training effect appears to be somewhat
larger in the simulator data for the far-transfer tests. The compar-
ison above, however, compares all 18 scenarios in the simulator
study with the 10 scenarios in the field study. Thus, a better com-
parison would be of the data from the scenarios that the two studies
shared and for which the very similar scoring criteria were used.
Although 10 of the scenarios in the simulator study had been
devised to be similar to the scenarios in the field study, when the
data were being scored, it was realized that two of the far-transfer
scenarios were really not comparable between the studies—the
TABLE 1 Percentage of Time Critical Region Was Scanned in
Simulator and Field Studies as Function of Training
Group Difference
Test Trained Untrained (effect of
Study Condition (%) (%) training) (%)
Simulator Near transfer 77.9 35.7 42.2
study Far transfer 76.8 44.2 32.6
Average 77.4 40.0 37.4
Field study Near transfer 79.2 40.4 38.8
Far transfer 58.3 38.2 20.1
Average 64.4 37.3 27.1
Fisher, Pradhan, Pollatsek, and Knodler 85
reason being that the field geometries of these two particular sce-
narios differed just enough from their counterparts in the simula-
tor to make it difficult to compare them using the same objective
scoring rules. However, the pattern of data from this “purified” set
of eight scenarios is quite similar to that of the overall data above
because the near-transfer effect was about the same in the field and
simulator studies and the far-transfer advantage was somewhat
bigger in the simulator study. The near-transfer effects averaged
over the five scenarios were 37.4% (71.0% versus 33.6%) for the
field study and 41.3% (70.6% versus 29.3%) for the simulator
study; the far-transfer effects averaged over the three remaining
scenarios were 26.5% (51.7% versus 25.2%) for the field study and
41.7% (77.8% versus 36.1%) for the simulator study. To assess
whether any of these differences between the field study and simu-
lator study were reliable, another analysis was done using the
means for individual participants on one of the following: (a) all
eight scenarios, (b) the five near-transfer scenarios, or (c) the far-
transfer scenarios. In these “purified” analyses, the 95% confi-
dence intervals for the differences in the training effect (simulator
study minus field study) were 12.4% ±25.0% over all eight sce-
narios, 3.9% ±26.4% for the near-transfer scenarios, and 28.9% ±
36.6% for the far-transfer scenarios.
Finally, a comparison was made of the training effects in the
field and on the simulator, scenario by scenario, for the near-
transfer scenarios. (The focus was on just the near-transfer sce-
narios because there were only three comparable far-transfer
scenarios.) The training effects corresponded quite well in each
scenario (see Figure 6). The standard error of the difference
between each pair of data points in Figure 6 is about 16%. The
averages of the individual scenario data are somewhat different
from the average near-transfer data reported above because the lat-
ter were first averaged over participants for each scenario and there
are some missing data.
DISCUSSION OF RESULTS
As the cost of driving simulators has come down, their use has
increased dramatically. They can provide information on drivers’
anticipation of hazards that is not as easy to gather in the field.
Results of these simulator studies are even now informing national
and state policies. Yet, there has been no study to date that makes it
possible to determine whether the hazard anticipation behaviors
observed on a driving simulator are also observed in the field.
The study reported above indicates that there is a close correspon-
dence between the effect of training on hazard anticipation behaviors
of drivers in the field and on a simulator and a reasonably close cor-
respondence in their overall level of hazard anticipation. The corre-
spondence is rather remarkable given the large number of differences
between the environment in the laboratory (the driving simulator
study) and the environment on the road (the field study), not the least
of which is the real danger present when a driver is actually maneu-
vering a vehicle on the open road. Not only is there a close similar-
ity overall between the hazard anticipation behavior of trained and
untrained novice drivers in the field and on the simulator, but this cor-
respondence extends to actual scenarios. The scenarios in the simu-
lator and the field were, for the most part, not chosen so that they
were identical to one another down to the smallest detail. Again, the
correspondence is remarkable.
Several caveats are, of course, in order. First, the hazard anticipa-
tion behavior of only two groups of novice drivers was examined,
those who received and those who did not receive hazard anticipation
training. Other groups of drivers may perform differently in the field
and on the driving simulator. Second, there are many other eye behav-
iors that one could have measured besides hazard anticipation behav-
ior. Again, these behaviors may differ in the field and on the driving
simulator. And third, only drivers who did not wear glasses took part.
We recommend that the study be repeated with drivers who do wear
glasses (the eye tracker can now accommodate such a population).
0
10
20
30
40
50
60
70
Incoming
Vehicle from
Left Fork
Right Turn
with Reveal
Point
Left Turn
with Reveal
Point
Hidden
Sidewalk Abrupt Lane
Change
Scenario
Percent Scanning Critical Region
Simulator
Field
FIGURE 6 Percentage scanning critical area in simulator and field studies as
function of scenario.
86 Transportation Research Record 2018
Having said this, these results are still encouraging in general and
especially for this group of drivers. Specifically, newly licensed
drivers are more likely to be involved in a crash than drivers with
more driving experience. This cohort suffers 9.3 fatal crashes per
100 million vehicle miles as compared with 1.4 for drivers between
45 and 54 years of age (20). Recent analyses of police accident
reports suggest that it is poor hazard anticipation skills that are a
major cause of the inflated crash rates of the newly licensed drivers
(21). It has been confirmed on a driving simulator that newly
licensed drivers’ hazard anticipation skills are indeed compromised
(22). Further studies indicate that training on the PC-based program
described above (RAPT) can dramatically improve novice drivers’
hazard anticipation skills on the simulator (11) and in the field (17),
and both immediately after training and 3 to 5 days after training
(23). Finding a very close correspondence on the simulator and in the
field between the hazard anticipation skills of trained and untrained
novice drivers suggests that one can continue to gather critical infor-
mation in risky scenarios on a driving simulator about the causes of
crashes and the procedures needed to remediate those crashes without
putting the novice driver at risk.
ACKNOWLEDGMENTS
Portions of this research were funded by grants from the Link Founda-
tion for Simulation and Training, the National Highway Traffic Safety
Administration, and the National Science Foundation. The authors
thank the Pioneer Valley Driving School for its help with the research
and especially Tom Marlow for his help in keeping the driving
simulator always operating at its fullest potential.
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sponsored publication of this paper.
