**Delphi Corporation Response to NHTSA Request for Collision Warning
Interface Metrics (CWIM) Information**

**Docket Number: NHTSA-2007-0038**

**Public Release**

**Submitted to**

**Docket Management Facility**

**M-30, U.S. Department of Transportation,**

**West Building Ground Floor, Room W12-140, 1200**

**New Jersey Avenue, S.E.,**

**Washington, DC 20590**

**14 April, 2008**

**Advanced Collision Avoidance Systems Field Operational Test (ACAS FOT)
Program Summary**

The Department of Transportation (DOT) has funded several Field
Operational Tests (FOTs) to investigate the driver acceptance and
potential safety benefits of collision warning systems. One of the
largest of these FOTs was the Advanced Collision Avoidance Systems Field
Operational Test (ACAS FOT) program that investigated adaptive cruise
control (ACC) in combination with Forward Collision Warning
(FCW)^\[1\]^. This program was funded by NHTSA and was conducted by a
team consisting of General Motors, Delphi Corporation, and the
University of Michigan Transportation Research Institute (UMTRI).
Ninety-six drivers drove ACAS-equipped vehicles for their own personal
usage for a period of four weeks, spanning a total of 137,000 miles. The
FCW system used Delphi's forward-looking radar (FLR) to detect the
range, range-rate, and azimuth angle of several vehicles in front of the
driver. When it appeared that the driver's vehicle needed to brake hard
to avoid colliding with the lead vehicle, the ACAS system provided the
driver with an icon on a head-up display (HUD) in conjunction with a
series of rapid auditory tones.

Within the ACAS FOT program, Delphi led the Driver Vehicle Interface
(DVI) task that developed and tested several display candidates, and
finally selected the display that was used for the FOT phase. A new
display candidate was modified from the Dingus et al. Car Icon
Display^\[2\]^ into a version that more accurately represented a looming
stimulus but also contained a six-trapezoid scale (see the
"looming-plus-scale" display in Figure 1) ^\[3\]^. The car-icon was
simplified so that it could present a single color at any given time but
could change color as threat-level increased. This display sequence was
modified to allow the possibility of a purely looming display (no
scale), a purely scale display (car-icon remains the same size and is
used only as a reference), and the combination of scale and looming. The
displays presented in Figure 1 also include a one-stage, two-stage,
three-stage (looming), and five-stage (looming-plus-scale or scale)
display. At the end of the display sequence, an imminent icon is
presented which represents the red car-icon at the largest size, with a
yellow shape over it to indicate the collision threat. The same imminent
visuals were used across the conditions so that the different visual
displays differed only in what icons preceded the imminent level.

These different display candidates were compared in a between-subjects
driving-simulator experiment^\[3\]^. Eighty participants were provided
with the FCW alerts (displayed in Figure 1) that appeared on the Head-up
Display (HUD) in response to an imminent lead-vehicle-braking event.
Displays that mimicked the looming stimulus (expansion of the rear-end
vehicle icon) with at least two visual stages (cautionary and imminent)
led to significantly earlier brake reaction times (BRTs) than displays
that provided no looming effect. Both BRT and subjective questionnaire
responses suggest that a display featuring a "looming" stimulus is
likely to be an effective visual stimulus for communicating imminent
collision threat. Figure 2 displays the final display sequence that was
used in the FOT phase. Although the multiple-stage alerting scheme
requires a more expensive display technology, the data from this
experiment suggest that a multiple phase alert sequence may be more
effective than a single-stage alert. Perhaps a multiple-stage alerting
sequence of a more continuous nature, which maps an increasing threat
level, can avoid some of the difficulties surrounding using a single
moment in time, that participants may not agree on. Unlike Lane
Departure Warning (LDW) where there is a clear and tangible correlate
for the warning threshold (crossing the lane marker), in the case of
FCW, the threshold for alerting the driver is more arbitrary and likely
differs across the driving population. For this reason, a continuous
display that provides an increasingly intrusive stimulus as the threat
level increases, may be more appropriate for FCW systems.

![](media/image1.wmf){width="6.595138888888889in"
height="4.958333333333333in"}

***Figure 1.** The one-stage (1), two-stage (2), three-stage/looming
(L), scale (S), and looming plus scale (LS) displays used in the
experiment as a function of threat level. Note that the number of stages
does not include the "vehicle detected" icon, which is a status
indicator rather than an alert stage. The imminent stage flashed on and
off at 4 Hz.*

Several months after the final selection of the driver-vehicle interface
and the overall vehicle integration, the University of Michigan
Transportation Research Institute (UMTRI) began the FOT phase of the
ACAS program. The NHTSA report ^\[1\]^ suggests that although the ACC
system was well received, participants provided mixed feedback for the
FCW system as a whole. At the conclusion of the FOT, participants were
asked several questions about the FCW system in general and specific
questions on the interface^\[4\]^.

![](media/image2.png)

***Figure 2.** The final configuration of the visual display used in the
ACAS FOT. The sequence shows the four amber stages of "looming" between
the cyan-colored vehicle-detected icon and the red and yellow imminent
collision-warning icon. The imminent collision warning flashed at 4 Hz
and was accompanied with a series of audible tones.*

On a scale of 1 (very ineffective) to 7 (very effective), participants
rated the visual alerts as 6.2 and the auditory alerts as 6.5 for
"getting their attention quickly". On a scale of 1 (unacceptably
annoying) to 5 (not at all annoying), average annoyance ratings for the
visual cautionary alert, the visual imminent alert, and the auditory
imminent alert were between 3.8 and 3.9. These "luke warm" annoyance
responses may, in part, reflect the rate of unnecessary alerts. The two
most frequent comments for how to improve the FCW system were to reduce
the rate of nuisance alerts and to allow the system to be turned off
under certain circumstances^\[4\]^.

During periods of manual (non-ACC) driving, FCW alerts were experienced
at a rate of 14 alerts per 1000 miles. Analyses revealed that the alerts
roughly broke down into even thirds, including 36 percent of the alerts
resulting from out-of path events[^1], 32 percent of the alerts
resulting from transitioning-path events[^2], and 27 percent of the
alerts resulting from in-path[^3] vehicles. Determining which of these
alerts were useful is an inherently subjective task that is likely to
vary between different drivers' perspectives. Common types of nuisance
alerts included situations where the host vehicle approached a lead
vehicle that was vacating the lane (either turning or changing lanes) or
when the host vehicle was approaching the lead vehicle with an intention
to pass. By asking the drivers in the study to review alerts that were
issued during their own driving experiences, it was found that
usefulness ratings differing significantly between the three scenario
types described above ^\[4\]^. Whereas in-path events yielded alerts
that were judged to be useful 53 percent of the time, only 33 percent of
alerts that occurred in response to transitioning-path events were
judged to be useful. Out-of-path events produced alerts that were judged
to be useful only 14 percent of the time. Drivers' open-ended responses
suggested that the level of perceived risk in these situations was often
qualitatively different, even between in-path and transitioning-path
events.

Using Lees and Lee's^\[5\]^ terminology, this analysis suggests that a
large percentage of the alerts were either unnecessary (alerts
corresponding to situations judged as hazardous by the algorithm but not
by the driver) or false (alerts corresponding to random activation of
the system that does not correspond to a threat). Although there was not
a strong statistical link between the frequency of unnecessary alerts
and the driver acceptance measures, the qualitative data and driver
comments suggest that the nuisance alert rate may have been excessive
for some participants, leading to lower driver acceptance.

**SAfety VEhicle(s) using adaptive Interface Technology Program
(SAVE-IT) Program Summary**

The Volpe National Transportation Systems Center, in support of NHTSA\'s
Office of Vehicle Safety Research, awarded the SAVE-IT "SAfety
VEhicle(s) using adaptive Interface Technology" contract to a diverse
team led by Delphi Electronics & Safety including Ford, University of
Michigan Transportation Research Institute (UMTRI) and University of
Iowa. The goal of SAVE-IT was to develop, demonstrate, and evaluate the
potential safety benefits of adaptive interface technologies (see
<http://www.volpe.dot.gov/hf/roadway/saveit/index.html>). The
countermeasures fell into two major categories: Distraction Mitigation,
where the systems were designed to mitigate distraction directly, and
adaptive safety warnings, where the effectiveness and acceptance of
safety warning systems could be improved by adaptive interfaces.

In order to conduct this research within the constraints of the \$6M
budget, the SAVE-IT program carefully evaluated the relative strengths
and weaknesses of the three testing venues: driving simulator, test
track, field testing. In addition to the cost constraint, a reasonable
production solution must address the constraints of both crash reduction
effectiveness and acceptance. Table 1 displays the research constraints
for the evaluation of crash reduction effectiveness compared with the
evaluation of acceptance^\[6\]^. Whereas the need to control driver
expectations and to place the driver at a high risk of collision
suggests a between-subjects design for the question of crash reduction
effectiveness, the need to expose the driver to a representative system
experience virtually demands an on-road evaluation. In order to directly
measure whether one system provides a safety benefit over another system
or whether a system compromises safety in some way, drivers must be
placed in a circumstance where there is a high risk of collision,
because without any collisions, there is no room for any improvement in
safety. A reasonable-fidelity driving simulator likely provides the most
effective environment for efficiently placing drivers at a high risk of
virtual collision, without any real risk to the driver.

When a driver is placed in a surprise circumstance where there is a high
risk of collision, the event likely changes the driver's behavior to
subsequent events. For this reason, an evaluation of crash reduction
effectiveness is best conducted on a between-subjects basis, where each
driver experiences only one surprise-collision event. Although the
between-subjects methodology requires a large number of subjects, the
design may potentially be streamlined to require only a short amount of
time per participant. Expanding on the driving simulator work that
Delphi conducted in leading the Driver Vehicle Interface task of the
ACAS FOT program, the SAVE-IT program further refined the
between-subjects testing methodology to provide an efficient and
sensitive protocol for measuring the effectiveness of different
collision warnings. In this context, this between-subjects methodology
was used to measure the differences between various human machine
interfaces and different methods of adapting warnings^\[6\]^.

**Table 1.** The Research Constraints for Crash Reduction Effectiveness
vs. Driver Acceptance.

+---------------------+----------------------+------------------------+
| **Research          | **Crash Reduction    | **Acceptance**         |
| Constraints**       | Effectiveness**      |                        |
+---------------------+----------------------+------------------------+
| Central Question    | Which system         | Which system is        |
|                     | compromises safety   | preferred, least       |
|                     | the least or         | annoying, most         |
|                     | provides the         | acceptable, or is      |
|                     | greatest safety      | perceived as the       |
|                     | benefit?             | safest?                |
+---------------------+----------------------+------------------------+
| Exposure            | Expose drivers to    | Expose drivers to a    |
| Requirements        | real or simulated    | representative         |
|                     | threatening          | experience that        |
|                     | scenarios where      | balances favorable and |
|                     | there is an apparent | unfavorable aspects of |
|                     | danger of collision  | a system               |
+---------------------+----------------------+------------------------+
| Control             | Control driver       | Facilitate adequate    |
| Requirements        | expectations         | driver comprehension   |
|                     |                      | of the system          |
+---------------------+----------------------+------------------------+
| Participant         | Between-subjects     | Span representative    |
| Requirements        | design               | demographics           |
+---------------------+----------------------+------------------------+
| Dependent Measures  | Objective            | Subjective Measures    |
|                     | Performance          |                        |
|                     |                      | *(Annoyance, perceived |
|                     | *(Reaction times,    | effectiveness, buy     |
|                     | time to collision,   | likelihood,            |
|                     | collision rates and  | preference)*           |
|                     | velocities)*         |                        |
+---------------------+----------------------+------------------------+
| Number of           | More participants    | More time per          |
| Participants vs.    |                      | participant            |
| Amount of Time per  |                      |                        |
| Participant         |                      |                        |
+---------------------+----------------------+------------------------+
| Ideal Research      | Driving Simulator    | On-road                |
| Facility            |                      |                        |
|                     | *(Unless millions of | *(Only real roadways   |
|                     | miles of             | provide the necessary  |
|                     | naturalistic data    | richness and           |
|                     | can be collected, an | complexity of the real |
|                     | experiment must      | environment that is    |
|                     | create an apparent   | difficult to           |
|                     | danger of collision  | anticipate a priori or |
|                     | without actual       | to simulate)*          |
|                     | risk)*               |                        |
+---------------------+----------------------+------------------------+

Drivers who are exposed to an imminent event and who often receive a
limited and biased exposure of the system in the driving simulator are
unlikely to be able to provide representative subjective feedback.
Because collision reduction effectiveness was only one of the
constraints, the acceptance of the systems was examined in another
venue, on-road field testings. Acceptance often varies greatly across
individuals and across demographics, such as age or gender. For this
reason, the subject pool must span a wider range of participants than
the crash reduction effectiveness, where the effects of these
demographics are less pronounced. In on-road field-testing, the driver
and system must be exposed to a representative experience that balances
both favorable and unfavorable aspects of the system. Field-testing is
also a valuable tool for exposing the system to a wide range of
circumstances that may test important aspects of the system-environment
performance that have not been considered. Although field testing was
used almost exclusively in the ACAS FOT (Advanced Collision Avoidance
Systems Field Operational Test) program, the testing provided relatively
little information about crash reduction effectiveness. The reason for
this is that, given collisions are only expected at a rate of
approximately one every half million miles, the mileage of ACAS FOT was
insufficient to expect any collisions. Unless field testing spans
millions of miles, such as the 100 Car Study^\[7\]^, the results will
provide relatively little information about crash reduction
effectiveness. Although field testing, when it can be done on a
sufficiently large scale, is clearly the ultimate test of effectiveness,
in most cases testing on the scale of millions of miles is not
practical. Furthermore it clearly does not allow for an iterative design
process (with respect to the crash reduction effectiveness). Field
testing on the scale of the ACAS FOT program, which included over one
hundred thousand miles of testing^\[4\]^, although relatively irrelevant
to crash reduction effectiveness, provided a large amount of useful
information about acceptance and how forward collision warning operates
on real roadways.

In human subject testing, the test track represents a compromise in many
respects between driving simulator research and on-road testing. The
environment is more realistic than the driving simulator and the
fidelity of the simulation cannot be called into question like it can in
driving simulator research. Because of the level of control, the
research can also push slightly deeper into the question of crash
reduction effectiveness. In some circumstances, methods can be developed
on the test track where the driver believes that they are at risk when
in reality there is actually little risk to the driver. One of the
difficulties with human subject research on the test track is that
drivers are often at a heightened level of arousal and readiness,
because they may not feel at ease in the test-track environment. The
need to communicate with traffic control and other vehicles in
combination with the responsibility of driving a real vehicle in a novel
environment can sometimes be overwhelming for participants, preventing
them from relaxing or adopting realistic driving behaviors. For example,
it may be quite difficult to distract drivers at a high level. Exposing
drivers to test conditions over long periods of time may help to reduce
this limitation. In the SAVE-IT program, the test track exposures were
primarily used to rapidly educate drivers with respect to the SAVE-IT
system features prior to experiencing the system in the field.

One of the tasks within the SAVE-IT program was to further develop the
human machine interfaces of the Forward Collision Warning (FCW) and Lane
Departure Warning (LDW) systems. During the SAVE-IT development, a
method of visually alerting drivers was developed in an attempt to
remove some of the limitations of conventional visual alerts. This
"exogenous display" used quick red flashes of light in the center of the
driver's forward field of view to draw the driver's attention toward the
center of the roadway. The flashes were intentionally brief so that it
would provide little for the driver to glance at before quickly
dissipating, leaving nothing but the external scene to communicate the
threat^\[6\]^. This method for warning the driver attempts to remove the
icon-viewing step of traditional warning systems, in order to more
rapidly draw the driver's eyes toward the most likely location of the
threat in the visual field. The ACAS FOT program revealed that drivers
still needed to assess the threat for themselves when they were
warned^\[1\]^, implying that an effective warning draws the driver's
attention to the location of the threat as rapidly as possible.

A between-subjects comparison in the driving simulator revealed
significantly faster accelerator release times for the exogenous display
in comparison to a HUD icon display (see Figure 3)^\[6\]^. Although both
types of display appeared to be effective at reducing the number of
crashes (5 or 6 versus 12), the exogenous display appeared to expedite
driver responses by almost half a second compared with the HUD icon. The
simplicity and low cost of the exogenous display is likely to offer a
more favorable alternative for production implementations when compared
to a HUD. For this reason the exogenous display was selected as the
visual display interface for both the FCW system of the SAVE-IT program.

![](media/image3.png){width="4.211811023622047in"
height="2.5984251968503935in"}

***Figure 3.** Comparison of accelerator release times for the exogenous
display (left), HUD display (middle), and no display (right). Because
the effects of an auditory and visual stimulus appeared to be additive
(non-interacting), the conditions shown here were collapsed across the
presence of an auditory stimulus, so that half of the subjects in each
condition experienced the auditory stimulus in addition to the visual
display shown above. The numbers at the top of the figure show the
number of crashes over the number of events.*

The fundamental principle behind adaptive warnings is that drivers who
are attentive do not benefit from warnings. In order to evaluate this
principle, a between-subjects design was used that factorially crossed
three display alternatives (visual, visual-plus-auditory tone, and no
warning) with the presence or absence of visual distraction. This design
was used to investigate the effect on safety of two negative adaptation
strategies: suppressing the auditory component of the alert and
suppressing the alert completely. Figure 4 displays the accelerator
release times of these conditions and reveals that whereas the FCW
alerts clearly benefited distracted drivers, they provided little
benefit for attentive (non-distracted) drivers. This result suggests
that, given attention to the forward roadway can be accurately assessed,
the strategies of either suppressing FCW completely (suppress strategy)
or suppressing the auditory component of the FCW stimulus (auditory
strategy), are unlikely to significantly compromise safety. In
practicality, however, because affordable attention-monitoring
technologies tend to measure head pose rather than eye gaze, the
strategy of total alert suppression during attentive episodes may not be
advisable. Although it was beyond the scope of the SAVE-IT program,
driver impairment (e.g., drowsiness) would also need to be monitored.

![](media/image4.png){width="6.38740157480315in"
height="3.583464566929134in"}

***Figure 4.** Accelerator release times and driving-simulator crash
rates of distracted drivers versus attentive drivers in the SAVE-IT
program. Drivers were provided with either a visual-and-auditory,
visual-only, or no FCW alert. The bold numbers show the number of
crashes / the number of events.*

As an extension of Heinrich's triangle, Dingus et al. ^\[7\]^ argued
that crashes are most often the result of drivers failing to adequately
respond to a precipitating event due to various contributing factors.
When precipitating events (such as a lead vehicle unexpectedly braking)
occur in the absence of the contributing factors, they are usually
resolved by the flexible and adaptive response of the driver, resulting
in what may be a near-miss, but usually not a collision. However, when
contributing factors, such as visual distraction are added, they act as
a catalyst for a crash by interfering with the driver's response,
converting a mere incident, or near-miss, into a collision. Other
examples of catalysts that can degrade driver responses might be poor
roadway conditions, mechanical failure, and driver impairment due to
fatigue, alcohol, or other factors. To the extent that this simple model
approximates the reality of lead-vehicle or single-vehicle crashes, it
would predict that, in the absence of a catalyst such as visual
distraction, drivers are unlikely to benefit significantly from
warnings. If the driver is attending to the forward roadway at the
moment a precipitating event occurs, the driver usually detects the
event and responds appropriately. In such a circumstance, there is
usually little opportunity to improve the process. A collision warning
would only present information to which an attentive driver is already
aware. Even if the driver is visually-attentive but is likely to react
slowly due to other factors, the warning system may still be unable to
hasten the process, because ultimately drivers must confirm the threat
for themselves before applying the brake^\[1\]^. The data presented in
Figure 4 support this conclusion, showing that in this driving-simulator
study, drivers who were attentive to the forward scene released the
accelerator at approximately the same time regardless of whether or how
they were warned^\[6\]^. Although this situation was inherently
threatening, with a collision rate of between 10 to 15 percent, the
collision warning system was unable to improve the response of
visually-attentive drivers. Only when the drivers were distracted was
there a statistically significant benefit of providing a warning. This
benefit was not only apparent across accelerator release times, brake
reaction times, and glance reaction times, but also demonstrated by a
clear trend in crash rates.

**Semi-Autonomous Vehicles Research**

Another relevant research activity in which Delphi is engaged is
investigating the human factors issues surrounding semi-autonomous
vehicles. This research will addresses a period of time where
limited-ability autonomous vehicles will be available and permitted on
public roads. These systems will require that the driver perform a
supervisory role and be ready to intervene when a situation develops
that is beyond the automation capability. These situations may be ones
that the system can recognize; however, there may be situations where
the driver must intervene without a warning from the system. The
proposed program will develop and test strategies intended to help
drivers maintain adequate situational awareness when operating vehicles
that have limited-ability autonomous driving capability. The proposed
project will develop adaptive, human centered concepts for helping
drivers maintain the necessary situation awareness. The research will
identify effective mechanisms for interaction between driver and
automation, and will also identify potential misconceptions and develop
countermeasures to avoid them.

Understanding the issues of semi-autonomous vehicles will be a crucial
step in addressing the next generation of alerts. As the industry
continues to deploy increasing levels of automation, the driver will
increasingly assume a supervisory role that may require a different
nature of alert. Thus, the interaction between collision warnings and
autonomous systems will become increasingly important. Already on
roadways today, FCW interacts with adaptive cruise control. For example,
in the ACAS FOT program, the warning algorithm was different when
adaptive cruise control was engaged. Rather than providing warning based
on the constraints of the driver's reactive capabilities, the alerts
were based on the braking authority of the vehicle. When the
circumstance required more braking than the ACC system had the authority
to deliver, the driver would receive an alert that they needed to
intervene. As the role of automation expands in the next decades, it is
this type of "driver intervention required" alert that will become
increasingly important.

**Specific Item Discussion**

In addition to the three federally-funded human factors programs that
are described here, there are many others that provide insight into
these questions. These three programs: ACAS FOT, SAVE-IT, and HF-LAAV
provide information and insight relating most specifically to the items
A1, B1, B2, and B3. In conclusion, we will briefly address these items
directly.

***(A1)** **What techniques, metrics, and criteria are now being used by
vehicle manufacturers for developing and evaluating the human factor
aspects of interface design and operation of ACWS at various stages of
product development? What tools are being used (simulators, tracks,
etc.) to evaluate DVIs? Are there ''lessons learned'' regarding their
use, practicality, or acceptance? What measures and procedures are the
most predictive of relevant safety parameters?***

The SAVE-IT program identified a research strategy that was summarized
in Table 1. Due to the need of efficiently producing imminent collision
situations, and the fact that imminent collision events change
subsequent driver responses, the SAVE-IT research strategy utilized a
refined between-subjects (one event per subject) driving simulator
protocol to measure the crash reduction effectiveness of different
warning systems. The efficiency of the test protocols developed in the
SAVE-IT program, allowed the between-subjects protocol to become
practical. Furthermore, unlike repeated measures designs that are
typically used in human factors evaluation, this protocol allows a
building-block approach because conditions measured in previous research
can be directly compared to new conditions. It is likely that the
driving-simulator-based reaction time measures, including accelerator
release time and brake reaction time will be the best predictors of
actual safety performance. Although the crash rates are less sensitive
and are likely not a good measure of the absolute crash rates that can
be expected in real circumstances, driving-simulator-based crash rates
may provide some indication about how well one system may reduce crashes
relative to another system. To determine how these driving-simulator
metrics map to crash rates in real driving, additional work must be
conducted that includes both extensive field operational testing of
sufficient size that numerous collisions are expected, and
driving-simulator replications of the real crashes that are observed.

Although the SAVE-IT driving simulator protocol was effective in
comparing the effectiveness of different alert candidates, due to the
brief exposures per subject, it provided little information about
acceptance. To address the acceptance constraint, SAVE-IT utilized field
testing, where drivers were exposed to the systems for a more extended
period of time under naturalistic conditions. Although the field
exposures in SAVE-IT were relatively brief, ideally, drivers should be
exposed to systems over a sufficient duration that they experience both
the positive and negative consequences of the system, and so that
drivers have time to overcome the novelty of the system and begin to
respond to the product with a consumer mind set. Testing over these
programs has demonstrated that human subjects usually begin with the
opinion that more warning is better (e.g., if there is any chance of
danger, you should warn the driver immediately). Only after they have
begun to understand the tradeoff between crash reduction effectiveness
and nuisance, can they provide acceptance data that is likely to predict
the success of a given product.

***(B1) What criteria should be used to determine the most sensitive,
reliable, relevant, and useful metrics?***

Field operational testing on the scale of the DOT field operational
tests (except perhaps the 100 Car Study) is insensitive to differences
in crash reduction potential. For this reason, driving simulator
protocols are likely to be more effective for measuring crash reduction
effectiveness. If more than one exposure per subject is used, the later
exposures will be contaminated by the fact that drivers are more likely
to be ready for the event. Because crashes occur in the worst cases, the
test protocol must find a way to lull drivers into a state of diminished
arousal that more closely approximates that of real world crashes. This
lower level of arousal and expectation is more difficult to achieve on
test tracks and test tracks are also less efficient. For this reason, we
believe that single-exposure response time metrics to imminent driving
simulator events are likely to provide the best way to distinguish
between the effectiveness of different collision warning systems. For a
system to be effective, it must also be acceptable to drivers.

***(B2) If consumers are annoyed or otherwise dislike the system, they
may turn it off or not purchase it. How should consumer acceptance or
driver annoyance be evaluated with respect to their influence on system
effectiveness?***

Although it is more time consuming, field testing is the preferred
method to expose drivers to a representative system experience and have
drivers enter a consumer mind set. Because of the habitual thought
patterns and the familiar experience involved in driver commuting, it is
likely that exposures during driver commutes may accelerate drivers
entering the appropriate mind set. One method for evaluating the
relationship between system effectiveness and acceptance is allowing
drivers to turn off the system during testing. In the ACAS FOT program,
driver could adjust the driver vehicle interface to remove the entire
pre-imminent (cautionary) phase of the warning. Had the majority of
drivers turned the cautionary phase off, we would have concluded that
the effectiveness of the cautionary phase would be likely to be negated
by drivers not accepting the system. However, the fact that the majority
of drivers left the cautionary phase active, suggests that most drivers
perceived that the benefit outweighed the annoyance.

***(B3) Driver response to ACWS can vary from person to person. Even the
same person can vary in performance depending on their state of mind,
e.g., drowsy or distracted. What subsets of the population need to be
included in developing criteria for CWIM? How should their needs and
capabilities be integrated into the assessment?***

The ACAS FOT program demonstrated that acceptance varies greatly over
driver age groups. We also know from a wealth of studies that age
affects virtually every parameter that is relevant to responding
effectively to a warning, including reaction times, visual acuity,
hearing sensitivity, tactile sensitivity, and useful field of
view^\[8,9\]^. Over the next decades, the relative size of the elderly
population will continue to expand^\[10\]^, and more research is
required to make sure that warnings adequately address the needs of the
elderly. It may be appropriate to provide different production software
configurations for the warning systems so that a given warning system
can be placed in a mode that addresses the needs of the older driving
population.

SAVE-IT focused on the differences between distracted and attentive
drives and how warnings may be adapted to improve the acceptance by
diminishing the warnings during attentive episodes (negative adaptation)
and increasing the warnings during distracted episodes. The results of
this program reveal some encouraging results for adaptive warnings,
demonstrating that adaptive warnings can help resolve the tradeoff
between annoying attentive drivers with unnecessary alerts while
providing sufficient warning for distracted drivers. Although drowsiness
was beyond the scope of the SAVE-IT program, more research is required
to examine methods for alerting drowsy drivers. In severe cases, where a
driver may be in a micro-sleep, traditional methods for alerting drivers
may be unsuccessful in breaking through to the driver's awareness.

**Concluding Remarks**

Delphi Corporation has conducted a large amount of research in the area
of collision warning systems, however, even when this research and the
research of many other informative programs, such as the 100 Car Study,
are considered, there are still questions in the area of CWIM that need
to be addressed. We believe that a key area in need of development is
the validation of efficient driving simulator test protocols for
measuring crash reduction potential. SAVE-IT has provided some initial
steps in the process of developing efficient driving simulator research
protocols, however, more work remains. As warning systems further
penetrate the market, unless standards are put in place, there will be a
wide range of approaches adopted by different OEMs. In order to avoid
confusion and a resulting response delay, there will be a growing need
for standardization of the human machine interface. More research is
required, balancing the constraints of cost, crash reduction potential,
and acceptance before such a standard can be created.

**References**

1\. National Highway Transportation Safety Administration (2005).
*Automotive Collision Avoidance System Field Operational Test: Final
Program Report* (No. DOT HS 809 886). Washington, DC: National Highway
Traffic Safety Administration.

2\. Dingus, T.A., McGehee, D.V., Manakkal, N., Jahns, S.K., Carney, C.,
& Hankey, J.M. (1997). Human factors field evaluation of automotive
headway maintenance/collision warning devices, *Human Factors, 37(2),*
216-229.

3\. Smith, M. R. H. (2002). *Automotive Collision Avoidance System Field
Operational Test: Warning Cue Implementation Summary Report.* Washington
D.C.: U.S. Department of Transportation, report no. DOT HS-809-462.
<http://www.nrd.nhtsa.dot.gov/departments/nrd-12/pubs_rev.html>.

4\. Ervin, R., Sayer, J., LeBlanc, D., Bogard, S., Mefford, M., Hagan,
M., Bareket, Z & Winkler, C. (2005). *Automotive collision avoidance
system (ACAS) field operational test methodology and results*. (US DOT
HS 809 901). Washington, D.C.: National Highway Traffic Safety
Administration.

5\. Lees, M. N. & Lee, J. D. (2007). The influence of distraction and
driving context on driver response to imperfect collision warning
systems, *Ergonomics*, 50(8), 1264-1286.

6\. Smith, M. R. H., Bakowski, D. L., & Witt, G. J. (In preparation).
*Safety Vehicle(s) using adaptive Interface Technology (SAVE-IT) Task 9
Final Report: Safety Warning Countermeasures.*
<http://www.volpe.dot.gov/hf/roadway/saveit/docs/safetywarn_rep.doc>.

7\. Dingus, T. A., Klauer, S. G., Neale, V. L., Petersen, A., Lee, S.
E., Sudweeks, J., Perez, M. A., Hankey, J., Ramsey, D., Gupta, S.,
Bucher, C., Doerzaph, Z. R., Jermeland, J., & Knipling, R. R. (2006).
*The 100-Car Naturalistic Driving Study: Phase II - Results of the
100-Car Field Experiment*, NHTSA DTNH22-00-C-07007.

8\. Hu P.S., Trumble, D.A., Foley, D. J., Eberhard, J. W., & Wallace, R.
B. (1998). Crash risks of older drivers: a panel data analysis.
*Accident Analysis and Prevention, 26(1),* 107-112.

9\. Marottoli, R. A. (2007). *Enhancement of driving performance among
older drivers*. Report prepared for AAA Foundation for Traffic Safety,
Washington, DC 20005.
<http://www.aaafoundation.org/pdf/EnhancingSeniorDrivingPerfReport.pdf>

10\. National Highway Transportation Safety Administration (2005).
*Traffic Safety Facts 2005: Older population.* (No. DOT HS 809 662).
Washington, DC: National Highway Traffic Safety Administration.

[^1]: Out-of-path events were defined as situations wherein the target
    was never in the host vehicle's lane, and included mostly stationary
    objects, such as bridges and other objects either above the roadway
    or on side of the road.

[^2]: Transitioning-path events were defined as situations wherein a
    moving vehicle was is in the same lane as the host vehicle for some
    period of the conflict and out of the host vehicle's lane for
    another period.

[^3]: In-path events were defined as those wherein the host and lead
    vehicles occupied the same lane throughout the conflict. Almost all
    of the objects in this category were moveable targets (objects that
    the radar had previously seen to move).