A Final Report of

SAfety VEhicles using adaptive Interface Technology (Phase II: Task 9C):

Adaptive Safety Warning Countermeasures

Prepared By

Matthew R. H. Smith

Deborah L. Bakowski

Gerald J. Witt

Delphi Electronics & Safety

Phone: (765)-451-9816

Email: <matt.smith@delphi.com>

January 2008

**Table of Contents**

9.0. EXECUTIVE SUMMARY [3](#executive-summary)

9.1. PROGRAM OVERVIEW [7](#program-overview)

9.2. Introduction [8](#introduction)

9.2.1 Differential Location [12](#differential-location)

9.2.2 Differential Stimuli [12](#differential-stimuli)

9.2.3 Differential Timing [13](#differential-timing)

9.2.4 Alert Suppression [15](#alert-suppression)

9.2.5 Adaptation to Cognitive vs. Visual Distraction
[15](#adaptation-to-cognitive-vs.-visual-distraction)

9.2.6 Phase II Research Questions [17](#phase-ii-research-questions)

9.3. Experiment 3: FCW HMI DOWN SELECTION
[19](#experiment-3-fcw-hmi-down-selection)

9.3.1 Experiment 3 Method [21](#experiment-3-method)

9.3.2 Experiment 3 Results [26](#experiment-3-results)

9.3.3 Experiment 3 Discussion [30](#experiment-3-discussion)

9.4. Experiment 4: LDW DOWN SELECTION
[32](#experiment-4-ldw-down-selection)

9.4.1 Experiment 4 Method [33](#experiment-4-method)

9.4.2 Experiment 4 Results [35](#experiment-4-results)

9.4.3 Experiment 4 Discussion [37](#experiment-4-discussion)

9.5. Experiment 5: FCW ADAPTATION DOWN SELECTION
[38](#experiment-5-fcw-adaptation-down-selection)

9.5.1 Experiment 5 Method [39](#experiment-5-method)

9.5.2 Experiment 5 Results [41](#experiment-5-results)

9.5.3 Experiment 5 Discussion [46](#experiment-5-discussion)

9.6. CONCLUSIONS [47](#conclusions)

REFERENCES [50](#references)

#  

# 9.0. EXECUTIVE SUMMARY

The objective of Task 9 (Adaptive Safety Warning Countermeasures) is to
improve safety-warning systems by designing these systems to adaptively
respond to real-time measures of driver distraction. The adaptation
strategies emerging from Task 9 involve the modification of conventional
safety-warning countermeasures, including forward collision warning
(FCW) and lane departure warning (LDW), to utilize the instantaneous
visual attention allocation of the driver (assessed in Task 7: Visual
Distraction). Adaptive enhancements to safety warning countermeasures
attempt to either reduce nuisance alerts without sacrificing safety or
to provide earlier or more effective warnings when drivers need them
most. The results of the 100-Car Study (Dingus, Klauer, Neale, Petersen,
Lee, Sudweeks, Perez, Hankey, Ramsey, Gupta, Bucjer, Doersaph,
Jermeland, & Knipling, 2006), may predict that drivers who are attentive
to the forward roadway may not benefit greatly from warnings. Only one
out of fourteen lead-vehicle collisions occurred in the absence of
"inattention to the forward roadway". When drivers do require warning
systems (such as when they are distracted), these warnings may be
presented either earlier or in a more salient manner. Because Task 9
examines potential methods for adapting warning systems, it is critical
to the \"Adaptive Safety Warning Countermeasure\" branch of the SAVE-IT
program. Task 9 focused on optimizing the human machine interface for
adaptive and non-adaptive modes, and on the evaluation of different
adaptation strategies.

***Phase I Adaptation Strategy Research***

In Phase I, two experiments were conducted. The first was a
between-subjects evaluation of driver response times during cognitive
and visual distractions (from Tasks 7 and 5 respectively) in comparison
to a control group with no experimentally imposed distraction.
Consistent with the literature, the results demonstrated a much larger
effect of visual distraction than cognitive distraction. The largest
differences in response times emerged between the first and second
exposures, with drivers in the control group responding 1.32 s earlier
on the second exposure than the first. The second experiment compared
three adaptation strategies and a non-adaptive system in a
repeated-measures driving simulator experiment. Both FCW and LDW were
adapted according to the Differential Stimuli (suppressing the auditory
component when the driver's attention is forward), Differential Timing,
and Alert Suppression adaptations. Whereas the Differential Stimuli
adaptation modifies the nature of the driver vehicle interface, the
Differential Timing and Alert Suppression adaptations modify the
behavior of the alert-generation algorithm. The Differential Stimuli
adaptation provided a visual-plus-tone alert when the driver's attention
was non-frontal and a visual-only alert when the driver's attention was
frontal. Whereas the Alert Suppression adaptation simply prevents alerts
from being generated when the driver is attentive, the Differential
Timing adaptation modifies the likelihood that alerts will be generated,
by providing earlier alerts when the driver is distracted and later
alerts when the driver is not.

These adaptation strategies were implemented using both positive and
negative modifications of the warning systems. Whereas negative
adaptations diminish the warnings when the driver's attention is on the
road, positive adaptations accentuate warnings when the driver's
attention is away from the road. Whereas the primary goal of negative
adaptation is to improve driver acceptance by reducing nuisance alerts,
the primary goal of positive adaptation is to improve the effectiveness
of the warning systems.

In the second experiment of Phase I, rather than using the distraction
sensing methods that were still under development, the experiments used
the presence of the task as the basis for the adaptation. Half of the
participants experienced the adaptation in response to a visual
distraction task and half of the participants experienced the adaptation
in response to a cognitive distraction task. Whereas the results
provided some objective (e.g., reaction times) and subjective
(questionnaire responses) support for the adaptations in response to the
visual distraction task, they did not support the adaptation in response
to a cognitive distraction task.

***Phase II Human Machine Interface Research***

Phase II focused on selecting an appropriate human machine interface for
the FCW and LDW systems and providing further data for the selection of
an adaptation strategy. Due to the large discrepancy in the first and
subsequent exposures to imminent lead vehicle braking events, all
testing in Phase II was conducted between subjects. During the early
Phase II development, a new 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 to draw the driver's attention toward the center of the
roadway, and then quickly dissipates leaving nothing but the external
scene to communicate the threat. It attempts to remove the icon-viewing
step by exogenously drawing the driver's eyes toward the most likely
location of the threat in the visual field.

A between-subjects comparison revealed significantly faster accelerator
release times for the exogenous display in comparison to a HUD icon
display (see Figure 9.1). 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 and LDW[^1] systems.

***Phase II Adaptation Strategy Research***

In order to examine the benefit of providing FCW stimuli for attentive
drivers, a between-subjects design was used that factorially crossed
three display alternatives (visual, visual-plus-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: Alert Suppression and Differential Alert Modalities
(suppressing just the auditory component). Figure 9.2 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 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.

![](media/image1.png){width="4.211811023622047in"
height="2.5984251968503935in"}

***Figure 9.1.** 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.*

Likewise, attentive drivers appear not to require LDW alerts. The
attentive and unwarned participants responded to the simulated wind-gust
event so quickly (Steering reaction time = 0.67) that many of these
participants were not even aware that the disturbance had even occurred.
The maximum lateral disturbance was on average less than half a meter.
These data support the Alert Suppression strategy for LDW, although
adaptive LDW systems might also likely to benefit from driver impairment
monitoring[^2].

The positive adaptations of Differential Location and Differential
Timing were also evaluated for FCW in Phase II to determine whether
these adaptations could provide a detectable safety benefit. The
Differential Location adaptation attempted to expedite drivers' response
to the warning by displaying the FCW visual icon (redundantly with the
exogenous display) in the location of the driver's distraction (in this
case the center console). The concept was that a visual stimulus would
be more useful if it was located in the position of the driver's
attention. These data did not support this adaptation method, and even
showed a slight increase in accelerator release time that was not
statistically significant when the redundant console (1.51 s) was
compared to just an exogenous display (1.36 s). This result might be
anticipated by the conceptual diagram in Figure 9.2, which predicts a
temporal cost of displaying an icon away from the forward scene. The
Differential Location adaptation was therefore rejected as an adaptation
candidate. Another evaluation compared the responses of distracted
drivers for an early FCW alert (0.5 s after the lead vehicle braking
event) versus a nominal FCW alert (1.5 s after the braking event).
Drivers who experienced the earlier alert began braking 2.26 s after the
event compared with the late-alert drivers who began braking 3.11 s
after the event.

![](media/image2.png){width="5.865354330708661in"
height="3.2161417322834644in"}

***Figure 9.2.** Accelerator Release Times (s) as a function of type of
FCW stimulus and driver distraction state. The numbers along the bottom
of the bars represent the number of collisions over the number of
events.*

***Conclusions***

Whereas Task 9 did not provide support for using the center-console to
display FCW stimuli, it did support the timing strategy of providing
earlier warnings as a means of expediting responses to FCW events from
visually-distracted drivers. Furthermore, the adaptation strategies of
suppressing some (Differential Stimuli) or all (Alert Suppression)
warning stimuli when the driver is attentive appear unlikely to
compromise safety and may provide an effective means of improving the
driver acceptance associated with FCW and LDW systems. The final
selection of adaptation strategies and the human machine interface was
reserved for the on-road work of Task 11b (Adaptive Safety Warning
Countermeasures Data Fusion).

# 9.1. PROGRAM OVERVIEW

Driver distraction is a major contributing factor to automobile crashes.
The National Highway Traffic Safety Administration (NHTSA) has estimated
that approximately 25% of crashes are attributed to driver distraction
and inattention (Wang, Knipling, & Goodman, 1996). Recent estimates from
the 100-Car study suggest that distraction may contribute to more than
three quarters of all crashes (Dingus, Klauer, Neale, Petersen, Lee,
Sudweeks, Perez, Hankey, Ramsey, Gupta, Bucjer, Doersaph, Jermeland, &
Knipling, 2006). The issue of driver distraction may become more
critical in the coming years because increasingly elaborate electronic
devices (e.g., cell phones, navigation systems, wireless Internet and
email devices) are being brought into vehicles that may further
compromise safety. In response to this situation, the John A. Volpe
National Transportation Systems Center (VNTSC), in support of NHTSA\'s
Office of Vehicle Safety Research, awarded a contract to a diverse team
led by Delphi Electronics & Safety including Ford, the University of
Michigan Transportation Research Institute (UMTRI) and the University of
Iowa. The goal of this program was to develop, demonstrate, and evaluate
the potential safety benefits of adaptive interface technologies that
manage the information from in-vehicle systems based on real-time
monitoring of the roadway and the state of the driver. The contract,
known as SAfety VEhicle(s) using adaptive Interface Technology
(SAVE-IT), is designed to mitigate distraction with effective
countermeasures and enhance the effectiveness of safety warning systems.

The SAVE-IT program serves several important objectives. Perhaps the
most important objective is that of demonstrating a viable proof of
concept that is capable of reducing distraction-related crashes and
enhancing the effectiveness of safety warning systems. Program success
is dependent on integrated closed-loop principles that incorporate the
state of the driver. This closed-loop vehicle system is achieved by
measuring the driver's state, assessing the situational threat,
prioritizing information presentation, providing adaptive
countermeasures to minimize distraction, and optimizing collision
warning systems.

# 9.2. Introduction

On national scales, automotive crashes occur with a clockwork-like
regularity, incurring significant costs and excessive human suffering.
On the scale of the individual, severe crashes have the potential to
produce devastating consequences, ending or changing forever the lives
of the people involved. Automotive crashes are the most common cause of
death for Americans between the ages of 4 and 34 (Subramanian, 2006).
Yet on the scale of a single individual during any given mile traveled,
the chances of a collision are low. Police-reported crashes occur on
average 2.1 times every million miles or once every 32 years of driving
(derived from NHTSA, 2006). For a crash to occur, a confluence of
unfortunate circumstances must operate in unison.

As an extension of Heinrich's triangle (Heinrich, Petersen, & Roos,
1980), Dingus et al. (2006) argued that crashes are most often the
result of the driver failing to adequately respond to a precipitating
event due to various contributing factors. Figure 9.3 displays a simple
model of crash causation based on the observations of Dingus and
colleagues. The central theme of this model is that crashes are
typically caused when contributing factors, such as inattention,
interfere with an avoidance response to a precipitating event. When
precipitating events (such as a lead vehicle unexpectedly braking)
unfold in the absence of these contributing factors, the conflict is
usually resolved by the flexible and adaptive response of the driver,
resulting in what may be a scare, but is 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, into a collision.
Although inattention is the most common example, less frequent examples
of catalysts that can degrade driver responses include poor roadway
conditions, mechanical failure, driver impairment due to fatigue,
alcohol, or other factors.

![](media/image5.wmf){width="5.999305555555556in"
height="2.529166666666667in"}

***Figure 9.3.*** A simple model based on the 100-Car Study findings of
distraction as a catalyst for collision.

To the extent that this model approximates the reality of lead-vehicle
or single-vehicle crashes, it predicts that in the absence of a catalyst
such as visual distraction, drivers are unlikely to benefit from
warnings. If the driver is attending to the forward roadway at the
moment a precipitating event occurs, the driver acts like a
servomechanism, quickly detecting the event and responding
appropriately. In such a circumstance, there is little opportunity to
improve the process. Even in cases of mechanical failure or poor roadway
conditions, the circumstance may not be aided by a warning if the driver
is already aware of what is occurring. For example, if an unexpected
lead-vehicle braking event requires a 1-g braking maneuver, yet the road
surface or host-vehicle brakes only support 0.5 g, a warning may provide
little assistance. In such cases, the crash may already be unavoidable
the moment it has begun to unfold.

To the extent that FCW or LDW alerts are not beneficial for attentive
drivers, suppressing warnings may be an effective strategy for reducing
nuisance alerts. In the context of the simple model shown in Figure 9.3,
such an adaptive warning system monitors the essential ingredients for a
collision, the precipitating event in the environment[^3] and the most
common contributing factor that interferes with a successful avoidance
response: driver distraction. By monitoring both environmental and
driver facets of a potential collision, adaptive warning systems may
provide alerts when drivers need them most while simultaneously reducing
the overall rate of alerts.

The CAMP workload metrics project (Angell, Anflick, Austria, Kochhar,
Tijerina, Biever, Diptiman, Hogsett, & Kiger, 2006) suggested that
auditory-vocal tasks can actually lead to a reduction in the standard
deviation of lane position (SDLP), supporting the observation that
cognitive distraction does not degrade lane keeping (Horrey & Wickens,
2006). The case for LDW may therefore be quite clear cut: in the absence
of either visual distraction or drowsiness, if the vehicle is departing
the lane, the driver is probably already aware of it. When a driver is
already aware of the lane departure, even if it is unintentional, they
still are unlikely to benefit from the warning.

In Phase I, two experiments were conducted. The first was a
between-subjects evaluation of driver response times during cognitive
and visual distractions (from Tasks 7 and 5 respectively) in comparison
to a control group with no experimentally imposed distraction.
Consistent with the literature, the results demonstrated a much larger
effect of visual distraction than cognitive distraction. The largest
differences in response times emerged between the first and second
exposures, with drivers in the control group responding 1.32 s earlier
on the second exposure than the first. The second experiment compared
three adaptation strategies and a non-adaptive system in a
repeated-measures driving simulator experiment. Both FCW and LDW were
adapted according to the Differential Stimuli (suppressing the auditory
component when the driver's attention is forward), Differential Timing,
and Alert Suppression adaptations. In Phase I, the Differential Location
adaptation was not specifically tested.

Table 9.1 displays the four adaptation strategies that were considered
for the SAVE-IT program, including Differential Location, Differential
Stimuli, Differential Timing, and Alert Suppression adaptations. Whereas
the Differential Location and Differential Stimuli adaptations modify
the nature of the human-machine interface, the Differential Timing and
Alert Suppression adaptations modify the algorithms that generate the
alerts. The Differential Location adaptation positions the visual
stimulus of the alert in the location of the visual distraction and the
Differential Stimuli adaptation provides a more urgent or
attention-capturing stimulus when the driver is distracted. Whereas the
Alert Suppression adaptation simply prevents alerts from being
generated, the Differential Timing adaptation modifies the likelihood
that alerts will be generated by providing earlier alerts when the
driver is distracted and later alerts when the driver is not.

***Table 9.1.** Adaptation Matrix displaying the adaptive safety warning
countermeasure strategies and how they behave as a function of the
attention being either forward or non-forward.*

+---------+-------------------+------------------+---------------------+
| ***Atte |                   | ***Negative      | ***Positive         |
| ntion-B |                   | Adaptation***    | Adaptation***       |
| ased*** |                   |                  |                     |
|         |                   |                  |                     |
| ***Ada  |                   |                  |                     |
| ptation |                   |                  |                     |
| Stra    |                   |                  |                     |
| tegy*** |                   |                  |                     |
+---------+-------------------+------------------+---------------------+
|         |                   | *Attention       | *Attention          |
|         |                   | Forward*         | Not-forward*        |
+---------+-------------------+------------------+---------------------+
|         |                   | *Goal: Improved  | *Goal: Improved     |
|         |                   | Acceptance*      | Safety*             |
+---------+-------------------+------------------+---------------------+
| Non-A   |                   | Nominal Alert    | Nominal Alert       |
| daptive |                   |                  |                     |
+---------+-------------------+------------------+---------------------+
| Modify  | Differential      | Nominal Alert    | Visual alert in     |
| Human-  | Location          |                  | location of         |
| Machine |                   |                  | driver's attention  |
| In      |                   |                  |                     |
| terface |                   |                  |                     |
+---------+-------------------+------------------+---------------------+
|         | Differential      | Less intrusive   | More intrusive or   |
|         | Stimuli           | or urgent        | urgent stimuli      |
|         |                   | stimuli          |                     |
+---------+-------------------+------------------+---------------------+
| Modify  | Differential      | Later Alert      | Earlier Alert (more |
| Al      | Timing            | (less likely)    | likely)             |
| gorithm |                   |                  |                     |
+---------+-------------------+------------------+---------------------+
|         | Alert Suppression | No Alert         | Nominal Alert       |
+---------+-------------------+------------------+---------------------+

***Note**. Whereas the manipulations in the "Negative Adaptation" column
are designed to increase acceptance, the manipulations in the "Positive
Adaptation" column are designed to improve the safety benefit.*

Most types of adaptation can be either positive or negative. Whereas
negative adaptations diminish the warnings when the driver is attentive,
positive adaptations accentuate or enhance warnings when the driver's
attention is directed away from the road. The primary goal of negative
adaptation is to improve driver acceptance by reducing the potential
nuisance of unnecessary and false alerts. The primary goal of positive
adaptation is to improve the safety benefit of the warning systems.
Although the primary goals are separate for negative and positive
adaptation, the dimensions of driver acceptance and safety benefit are
certainly not independent. For a system to achieve a safety benefit, the
driver must accept the alerts to some extent. Lees and Lee (in press)
demonstrated that drivers complied less with their FCW system when the
system was prone to false alerts. This "cry wolf" effect has been
consistently shown to undermine response to warning systems (Bliss,
Dunn, & Fuller, 1995; Bliss & Acton, 2003). Furthermore, it is likely
that if the system apparently fails to achieve a safety benefit, drivers
may be less likely to accept the system because they do not perceive the
system as being useful.

In Experiment 2, in addition to the Non-Adaptive baseline, each driver
experienced the Differential Timing, Differential Stimuli (with both
positive and negative adaptations), and Alert Suppression adaptations.
Rather than using the distraction sensing methods that were still under
development, the experiments used the presence of the distraction task
as the basis for the adaptation. Half of the participants experienced
the visual distraction task and half of the participants experienced the
cognitive distraction task. Whereas the results provided some objective
(e.g., reaction times) and subjective (questionnaire responses) support
for the visual distraction adaptations, they did not support the
cognitive-distraction adaptations[^4].

Table 9.2 displays the rankings of driving performance and subjective
responses to the FCW system for the three types of adaptive systems. The
rankings were ordered so that lower scores invariably correspond to more
favorable rankings and higher scores invariably correspond to less
favorable rankings. In addition to the three types of adaptation shown
in the table, the non-adaptive condition was also included, drawing most
of the "4" (least favorable) rankings. Note that these rankings do not
necessarily represent findings that were statistically significant, but
are just presented to show the trends observed in this data. The
different types of adaptation shown in Table 9.1 are discussed in more
detail in the remainder of this Section.

**Table 9.2.** The Effectiveness of Three Types of Adaptation as a
Function of Distraction Type.

+-----------+--------+--------+-----------+--------+--------+---------+
| **        | **     |        |           | **Cog  |        |         |
| Dependent | Visual |        |           | nitive |        |         |
| Measure** | D      |        |           | D      |        |         |
|           | istrac |        |           | istrac |        |         |
|           | tion** |        |           | tion** |        |         |
+-----------+--------+--------+-----------+--------+--------+---------+
|           | Differ | Differ | Alert     | Differ | Differ | Alert   |
|           | ential | ential |           | ential | ential |         |
|           |        | S      | Su        |        | S      | Supp    |
|           | Timing | timuli | ppression | Timing | timuli | ression |
+-----------+--------+--------+-----------+--------+--------+---------+
| D         | 2      | 3      | 1         | 1      | 2      | 3       |
| istracted |        |        |           |        |        |         |
| BRT       |        |        |           |        |        |         |
+-----------+--------+--------+-----------+--------+--------+---------+
| Attentive | 4      | 1      | 3         | 3      | 1      | 4       |
| BRT       |        |        |           |        |        |         |
+-----------+--------+--------+-----------+--------+--------+---------+
| Annoyance | 1      | 3      | 2         | 3      | 4      | 2       |
+-----------+--------+--------+-----------+--------+--------+---------+
| U         | 2      | 1      | 3         | 4      | 3      | 2       |
| sefulness |        |        |           |        |        |         |
+-----------+--------+--------+-----------+--------+--------+---------+
| Timing    | Best   | Ea     | Early     | Ea     | Early  | Late    |
|           |        | rliest |           | rliest |        |         |
+-----------+--------+--------+-----------+--------+--------+---------+
| Trust     | 2      | 1      | 3         | 4      | 2      | 1       |
+-----------+--------+--------+-----------+--------+--------+---------+
| Self-c    | 1      | 2      | 3         | 4      | 1      | 3       |
| onfidence |        |        |           |        |        |         |
+-----------+--------+--------+-----------+--------+--------+---------+
| P         | 2      | 1      | 3         | 3      | 1      | 2       |
| reference |        |        |           |        |        |         |
+-----------+--------+--------+-----------+--------+--------+---------+

***[Note.]{.underline}*** The numbers represent numerical rankings from
1 (most favorable) to 4 (least favorable) and do not necessarily
represent significant differences. The BRT rows represent the rankings
for the -5 m/s^2^ braking events. For example, the visually-distracted
drivers responded fastest to the alert suppression adaptation and
slowest to the non-adaptive warnings.

### 9.2.1 Differential Location

The Differential Location adaptation strategy modifies the placement of
the visual stimulus as a function of the driver's focus of attention. In
the SAVE-IT manifestation of this adaptation, when drivers were
distracted by the center-console application and the FCW or LDW systems
triggered an alert, the visual stimulus (an FCW or LDW icon) was
presented on the center-console. This icon temporarily replaced the
material that was being displayed and was presented redundantly with the
other visual and auditory stimuli only when the driver was already
distracted. For non-distracted drivers, only the conventional visual and
auditory warning stimuli appeared. The rationale for this adaptation is
that an alert placed in the path of the driver's glance is more likely
to acquire the driver's attention. The reason that warning icons should
not be indiscriminately positioned in the center-console is that
positioning an icon away from the frontal location could draw the
driver's attention away from the external threat. This adaptation
attempts to circumvent this problem by only presenting an icon in the
center-console when the driver's attention was already directed toward
that location. For distraction sources not located in the center
console, this type of adaptation would need to position the visual
warning elsewhere. As shown in Table 9.1, drivers who were attending to
the forward scene at the time of the alerting event received a nominal
(non-adaptive) warning. This type of adaptation was not evaluated in
Phase I of the SAVE-IT program, but was evaluated in Phase II.

### 9.2.2 Differential Stimuli

Like the Differential Location adaptation strategy, the Differential
Stimuli strategy operates by modifying the driver interface of the FCW
and LDW systems. The Differential Stimuli adaptation seeks to reduce
annoyance by suppressing the most intrusive component of the alert, when
the driver is attentive to the forward roadway. An auditory alert is
more likely to produce annoyance than a visual stimulus because it is
usually more intrusive than a visual stimulus and cannot be localized to
the driver, thus potentially interrupting conversations or undermining
the passenger's confidence in the driver. In the SAVE-IT manifestation
of this adaptation, a visual-only alert was provided for attentive
drivers and a visual-plus-tone alert was provided when drivers were
distracted. The reasoning behind this strategy is that drivers who are
already looking in the forward direction are likely to be able to detect
a visual alert located near the forward scene and thus are unlikely to
need an auditory stimulus. Whereas a distracted driver may require an
orientation-independent stimulus (such as an auditory alert) to
reacquire their attention, an attentive driver may be adequately alerted
from an orientation-dependent stimulus (such as a visual alert).

In Experiment 2, this strategy not only used the negative adaptation of
suppressing the auditory component of the alert, but also provided a
positive adaptation when the driver was distracted, by providing a voice
stimulus (saying "lead vehicle braking" or "drifting left" or "drifting
right") at a cautionary threshold. This adaptation was the most
noticeable adaptation that participants experienced, and was the only
method of adaptation that more than half of the participants in each
group noticed. This was likely due to the voice messages that
accompanied the cautionary alerts being more salient than the other
forms of adaptation.

During the distracted-driving segments where the positive side of the
adaptation functioned, participants reacted faster to the -3 m/s^2^
braking events during the Differential Stimuli drive than the Alert
Suppression drive. However, accompanying the cautionary FCW alerts with
the voice message (Differential Stimuli) was not as effective at
expediting the driver responses to the -5 m/s^2^ braking events as
having a more immediate imminent alert (Differential Timing). During the
non-distracted segments where the negative side of the adaptation
functioned, removing the audible component of the imminent FCW alerts
(Differential Stimuli) delayed driver responses less than delaying the
imminent alert (Differential Timing). When drivers were not engaged in a
distraction task, removing the auditory stimulus from the imminent
warnings did not appear to degrade driving performance. If this result
is valid, it may suggest a promising way of reducing annoyance without
sacrificing the benefit of potentially useful alerts.

Participants in the visual-distraction group rated the FCW system in the
Differential Stimuli drive as the most preferred, most trusted, and most
useful. On the less desirable side, this system was rated as the most
annoying and as having the earliest timing (which could potentially
annoying the driver), probably reflecting the fact that the voice
message accompanied the cautionary alert level. Participants in the
cognitive-distraction group also rated the FCW system in the
Differential Stimuli drive as the most preferred but most annoying
alternative.

The LDW system in the Differential Stimuli drive shared similar
undesirable characteristics (annoyance, earliest timing) as the FCW
system, however, unlike the FCW system, the Differential Stimuli
adaptation for the LDW system was not the most preferred alternative of
the visual-distraction group. The visual-distraction group did rate it
as the most trusted LDW adaptation and the most useful of the three
adaptation alternatives (but not as useful as having no adaptation at
all).

One potential benefit of the Differential Stimuli adaptation is that it
may be able to help reveal the underlying functionality of the warning
system. For example, rather than completely suppressing an LDW alert,
the adaptation might only suppress the more intrusive component, thereby
saving the driver from the annoyance but still providing some indication
(e.g., seat vibration) that the system detected the lane crossing. It
also allows for the possibility of providing some benefit for drivers
who are severely cognitively distracted if such drivers are able to
benefit from the warnings.

### 9.2.3 Differential Timing

The Differential Timing strategy provides earlier alerts for distracted
drivers and later alerts for attentive drivers. This strategy is based
on data demonstrating that distracted drivers require more time to
respond to an alert than attentive drivers (e.g., Lee, McGehee, Brown, &
Reyes, 2002; Smith & Zhang, 2004b). By providing differential
predictions for how quickly a driver will respond to the warning, an FCW
algorithm can provide distracted drivers with sufficient time to
respond. Many unnecessary alerts may also be avoided with a Differential
Timing strategy because attentive drivers are likely to respond to the
threatening conditions or the fleeting pseudo-threat is more likely to
dissipate before the later alert threshold is reached. In the Phase I
implementation of this adaptation, drivers would receive alerts up to
1.5 s earlier than the non-adaptive condition when drivers were
distracted and up to 0.5 s later than the non-adaptive condition when
drivers were not distracted. Experiment 2 demonstrated that the rate of
nuisance alerts could effectively be reduced by later timing when the
driver was attentive (negative adaptation), and that early alerts for
distracted drivers could reverse the negative effects of distraction
(positive adaptation). Lee et al.'s (2002) research also supports the
positive side of this adaptation because it demonstrated that earlier
warnings translated into significantly earlier responses.

In Experiment 2, the Differential Timing adaptation directly influenced
the timing of the alerts, but also indirectly influenced the number of
alerts that drivers received. Despite this influence, the timing
adaptation was relatively unnoticed. Only one quarter of the drivers in
the visual-distraction group claimed to notice the FCW and LDW
adaptations. Perhaps this can be explained because these participants
were distracted during the half of the drive that the timing effect
would have been most salient. The time-sharing of the visual-distraction
task may have made it difficult to notice the timing changes. In the
cognitive-distraction group, half of the participants claimed to notice
the FCW adaptation and only two of the twelve participants claimed to
notice the LDW adaptation.

Compared with the other methods of adaptation, the Differential Timing
adaptation had a large effect on driver-response times. During the
distraction task, participants responded significantly faster during the
Differential Timing drive than in the other drives. The negative side of
this adaptation produced the opposite effect for the visual distraction
group during the non-distraction segments, reflecting the delay in the
alerts when the driver was attentive.

Consistent with the other types of adaptation, the visual-distraction
group rated the Differential Timing FCW system as not annoying
(approximately 2 on a seven-point scale). They rated the Differential
Timing FCW system as being numerically[^5] the least annoying, having
timing closest to "just right", and inspiring the greatest
self-confidence. The cognitive-distraction group did not rate the
Differential Timing FCW system as favorably, rating the Differential
Timing FCW system as the earliest, least useful, least trusted, and
least inspiring of self-confidence.

Although the Differential Timing adaptation affected the timing and
occurrences of the LDW system, there was no measurable effect on
lane-keeping performance (average absolute TLC and SDLP) compared with
the other two adaptive systems. Participants in the visual-distraction
group rated the Differential Timing LDW system as significantly more
annoying than the non-adapted LDW system. The increase in the
sensitivity of the LDW system that led to an average of 17 additional
cautionary alerts per driver and additional imminent alerts for five of
the eleven drivers is likely to have been the cause. Despite the
increased annoyance and being rated as the least useful, the
visual-distraction group rated the Differential Timing adaptation as the
most preferred LDW alternative. The opposite occurred in the
cognitive-distraction group, with the Differential Timing adaptation
being rated as the least preferred LDW alternative.

### 9.2.4 Alert Suppression

Alert Suppression is perhaps the most simple and obvious negative
adaptation strategy, and directly governs whether the warning is issued.
When the driver is attentive, the alert is suppressed, but otherwise the
alert functions like a nominal warning system.

In Phase I, the effects of the Alert Suppression adaptation were quite
subtle. Only six out of the twelve drivers in the visual distraction
group claimed to notice the Alert Suppression adaptation, and only two
of twelve did in the cognitive distraction group. By design, this
adaptation suppressed all 69 FCW alerts that would have otherwise
occurred during the non-distracted segments of the two groups (visual
and cognitive distraction). The Alert Suppression adaptation had a
smaller effect on the LDW system, suppressing only one imminent LDW
alert for four out of 24 drivers that would have otherwise occurred
during the non-distracted segments.

The absence of imminent alerts did appear to slow the driver's response
during the -5 m/s^2^ braking events in both the visual and cognitive
distraction groups. This adaptation actually reversed the effect of both
types of distraction, with drivers responding faster to the -5 m/s^2^
braking events during the distracted segments than the non-distracted
segments.

The subjective ratings of the FCW and LDW systems during the Alert
Suppression drive tended to reflect the fact that few participants
noticed it. Compared with the other two drives, the visual-distraction
group rated Alert Suppression for both FCW and LDW as least useful,
least trusted, least inspiring of self-confidence, and least preferred.
The only positive indications were that the visual-distraction group
rated the Alert Suppression for the LDW system as being the least
annoying and having timing closest to "just right".

### 

### 9.2.5 Adaptation to Cognitive vs. Visual Distraction

Whereas the adaptations to visual distractions usually led to more
desirable ratings, adaptations to cognitive distractions did not.
Adapting the LDW system to cognitive distraction in particular appears
to be ineffective. Figure 9.4 displays the driver feedback for the
effectiveness, annoyance, usefulness, and trust rating scales averaged
over the three methods of adapting the FCW system for the visual and
cognitive groups. The visual-distraction group rated the three FCW
adaptations as significantly more effective, significantly less
annoying, significantly more trusted, and significantly more useful than
the cognitive-distraction group. This result suggests that adapting FCW
to visual-distraction is more effective than adapting FCW to
cognitive-distraction. This observation is mirrored by the fact that
participants in the cognitive-distraction group exhibited no clear
preference for any type of adaptation. Although it was not statistically
significant, the non-adaptive FCW system was rated by the
cognitive-distraction group as being the least annoying and most useful
when compared with the three adaptive alternatives.

***Figure 9.4.*** Responses to the effectiveness, annoyance, trust, and
usefulness scales averaged over the three adaptive drives (Differential
Timing, Alert Suppression, Differential Stimuli) as a function of
distraction group.

Adapting to cognitive distraction did not appear to be an effective
strategy for LDW either. The cognitive-distraction task did not degrade
lane-keeping performance and so drivers in the cognitive-distraction
group received few LDW alerts. This result is consistent with the
results of the Horrey and Wickens (2004) meta-analysis that found little
degradation of lane-keeping performance as a result of cell phone
conversations. This experiment did not support the hypothesis that an
LDW alert is more likely to be useful when the driver is distracted by a
purely cognitive task. Instead these data appear to suggest that LDW
systems should only adapt to the visual-manual rather than
auditory-vocal dimension of distraction.

Using non-contact automotive grade sensors, cognitive distraction is
more difficult to detect than visual distraction. Because of this and
because Phase I was unable to develop an effective set of FCW and LDW
adaptations for cognitive distraction, it was decided that Phase II
would focus on selecting adaptation methods for visual distraction.
Rather than implying that cognitive distraction is not important, Phase
I suggests that more subtle and sophisticated adaptation techniques
should be developed in the future to make use of this information when
it becomes available.

Experiment 2 provided relatively low annoyance ratings in most
circumstances. However, this may be misleading because when drivers are
exposed to real FCW and LDW systems on public roadways, they may be more
likely to experience the annoyance of inappropriate alarm activations.
As such, they may learn to appreciate a system that is capable of
reducing nuisance alerts. The benefit of the Alert Suppression method of
adaptation may have been especially underestimated. Despite the
relatively negative subjective results of the Alert Suppression
adaptation, on real roadways this method of adaptation may still be
useful for counteracting the potential annoyance of inappropriate LDW
activations (e.g., changing-lanes and intentional lane deviations).

### 9.2.6 Phase II Research Questions

Table 9.3 displays the research constraints for the evaluation of safety
compared with the evaluation of acceptance. Whereas the need to control
driver expectations suggests a between-subjects design for the question
of safety, the need to expose the driver to a representative system
experience demands an on-road evaluation. Because the on-road research
platform was not yet available, it was decided that the Phase II Task 9
activity would focus exclusively on safety-benefit. Whereas Phase I had
collected preliminary observations with respect to safety benefit and
driver acceptance by using a repeated-measures design, Phase II sought
to conduct a between-subjects analysis that focused exclusively on
safety benefit. The between-subjects methodology required a large number
of subjects, but could be streamlined to require only a short amount of
time per participant. By maximizing the effectiveness of the driving
simulator, this between-subjects methodology would provide useful data
to aid the down-selection of adaptation strategies. This evaluation
could examine whether the various negative adaptations compromise safety
benefit and whether the positive adaptations improve safety benefit over
non-adaptive warning systems. Although the selection of adaptation
strategies could not be made solely on the merits of safety benefit, the
questions surrounding driver acceptance could be evaluated when the
on-road research platform became available. The on-road exposures that
were to follow were conducted as a part of Task 11b (Safety Warning
Countermeasures Data Fusion) and provided the final data necessary to
select the adaptive systems that would be evaluated in Task 14
(Evaluation).

**Table 9.3.** The Research Constraints for Safety Benefit vs. Driver
Acceptance.

+---------------------+----------------------+------------------------+
| Research            | Safety Benefit       | Acceptance             |
| Constraints         |                      |                        |
+---------------------+----------------------+------------------------+
| Central Question    | Which system         | Which system is least  |
|                     | compromises safety   | annoying, most         |
|                     | the least or         | acceptable, most       |
|                     | provides the         | preferred, or provides |
|                     | greatest safety      | the greatest           |
|                     | benefit?             | perception of benefit? |
+---------------------+----------------------+------------------------+
| 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  | the 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)*               |                        |
+---------------------+----------------------+------------------------+

The experiments of Phase II were as follow:

-   **Experiment 3 (FCW HMI Down Selection)**: Test different visual and
    auditory display stimulus alternatives.

-   **Experiment 4 (LDW Down Selection)**: Compare different HMI and
    adaptation candidates for LDW .

-   **Experiment 5 (FCW Adaptation Down Selection)**: Investigate the
    effectiveness of different adaptation strategies for alerting
    distracted versus attentive drivers.

Although most of the evaluations could be packaged into a
fully-factorial design, there were several additional conditions that
needed to be evaluated in addition to the three major factorial designs.
The modular between-subjects design allowed several experimental
conditions to be added as a series of t-tests, that were tailored to the
evaluation of specific SAVE-IT concepts.

# 9.3. Experiment 3: FCW HMI DOWN SELECTION

![](media/image6.png){width="1.75in"
height="1.4895833333333333in"}During the period between the two research
phases, several observations were made about the effectiveness of icons
for alerting drivers. The Society of Automotive Engineering (SAE)
compared the comprehension of different active safety icons. The test
procedure provided the icon to participants as an image on paper and
asked participants what they believed the symbol meant. Figure 9.5
displays the icon that had the highest comprehension for FCW. For this
evaluation, the concept of "best" must be considered to be relative,
because 55 % of participants missed one or more major informational
elements of the best-performing icon and 5 % even misunderstood the icon
to such an extent that their understanding may have led them to engage
in an unsafe action. Of the four icons shown to participants, one led to
89 % of participants missing one or more major informational elements
and another was misunderstood by 28 % to an extent that they might
engage in an unsafe action.

***Figure 9.5.** SAE committee's best understood icon for FCW.*

These results were collected in a circumstance that granted them more
time than a real collision conflict would. In the real instance, an icon
of lower contrast might be rapidly flashed at the driver, frequently
requiring a response within a second or two. With such a short duration
in which to comprehend the icon, drivers might have insufficient time to
digest what the icon is attempting to convey, including the arrow that
suggests which direction the vehicles are traveling (rather than what
the appropriate action is that should be taken). Thus, in the brevity of
a real collision-conflict event, the comprehension of previously unseen
icons could be even worse.

To aggravate the difficulty of icons further, the constraints of
automotive production may prevent a Head-up display (HUD) from being
used to display the icon, requiring that the icon be placed in the
instrument cluster. Such a location may lead the icon to be unnoticed,
because after drivers hear the audible alert, they are most likely to
direct their visual attention to the forward scene in order to detect
the threat with their own perceptual systems. For this reason,
instrument-cluster icons are likely to go unnoticed, providing little
benefit over an auditory-only alert.

Figure 9.6 displays a conceptual diagram of some of the observations
made during the ACAS FOT program and the first phase of SAVE-IT. When a
distracted driver is not warned, it may take a large amount of time
before the driver glances back and detects the collision threat. When a
visual icon is displayed in a sub-optimal location, the distraction may
be interrupted, however, an additional step is added where the driver
must look at and try to interpret the warning icon. Because drivers may
not completely trust the message, they most likely will need to see the
threat with their own eyes before they fully respond. Although
presenting an icon on the HUD may expedite the transition between
viewing the icon and the forward scene, the icon-viewing step is still
an addition to the process. The temporal cost of adding this stage may
be justified by the interruption of distraction, however, it is an
additional stage that may require several hundred milliseconds.

![](media/image7.wmf){width="5.996527777777778in"
height="2.270138888888889in"} *Conceptual diagram of a distracted driver
responding to an imminent event.*

If drivers do not blindly trust warnings and must seek confirmation with
their own eyes, the ideal type of visual display would be one that does
not add an additional stage but instead just interrupts the driver's
distraction and draws their visual attention to the appropriate location
in the visual field. In the case of both FCW and LDW, the conflicts are
most likely to be evident near the focus of expansion[^6] of the optic
flow. For this reason, rather than displaying an icon, an alternative
strategy is to create a disturbance in the visual field near the focus
of expansion to exogenously attract the driver's eyes to the location
where they are most likely to detect the conflict with their own
perceptual system. Such a display might be referred to as an exogenous
display because rather than convincing the driver that their eyes need
to look in a given direction (endogenous control of attention), the
visual field draws the drivers eyes to this location (exogenous control
of attention; see Theeuwes, 1991). Ideally, such an exogenous display
should be displayed briefly so that the occurrence outside, rather than
the visual disturbance itself, becomes the focus of attention.

To test this exogenous display concept, a simple display was developed.
This display created a point source of light (using a high-intensity red
LED) on the vehicle dash that reflected up onto the windshield on the
driver's centerline and near the horizon. Although similar in concept to
a HUD, this exogenous display differs in that it is positioned higher
than a traditional HUD and does not require complex optics because there
is no graphical image. Because of its simplicity, this display avoids
many of the production disadvantages of the HUD including:

1.  Low contrast ratio due to the need to filter the light source in
    producing an image

2.  Inability to see the display when the driver wears polarized
    sunglasses

3.  Difficulty to integrate because of large size (due to complex
    optics)

4.  High cost due to complexity

Like traditional HUDs, the exogenous display must control the brightness
as a function of the ambient illumination in order to both provide a
bright display that competes with daylight and avoids temporarily
blinding drivers at night. In order to quickly capture the driver's
attention, the red LED was programmed to flash three times at 5 Hz,
using a 50 % duty cycle.

Another strategy for counteracting the problem of alert comprehension
was to use an auditory stimulus with more of an associated meaning, or
an auditory icon (see Graham, 1999). Whereas the ACAS FOT program used a
series of tones that had no inherent meaning, an alternative approach
was to use a sound that drivers could sassociate with the appropriate
response, such as a car-horn sound emerging from the forward direction.
Graham found a small but detectable benefit for brake reaction time in
using auditory icons in comparison to simple tones in a
repeated-measures experiment. The car-horn sound was ranked as the most
appropriate auditory stimulus for conveying the event of a vehicle
pulling out into the host vehicle's path. Because of the
well-established mapping between stimulus and response, the auditory
icons seemed to trigger a nearly automatic response, which also led to
inappropriate responses (braking in response to false alerts) more
frequently than the speech or tonal stimuli. Balz, Robinson, and Casali
(1999) also observed a reaction time benefit for auditory icons
(including a car-horn sound for a side warning and tire-skidding sound
for a forward collision warning) in a repeated-measures experiment in
the context of commercial vehicles. Some pilot testing for this
experiment also revealed that the car-horn sound provided a reasonable
balance across the variables of urgency, understanding, and annoyance.

Experiment 3 was designed to compare the exogenous display concept
against a traditional HUD icon and explore the potential interactions
that this display might have with differing auditory stimuli.

## 9.3.1 Experiment 3 Method

***Participants***

One hundred male employees at Delphi Electronics and Safety Systems in
Kokomo, Indiana, participated in the study. Only male employees were
used because previous studies had shown a lower rate of motion sickness
for males compared to females and the Phase I research had not revealed
any relevant performance differences between the responses of males or
females. Furthermore, males were far more prevalent in the subject
population. Employees were informed about the study by email using a
random selection process. Participants were randomly assigned (10
participants per condition) to one of the nine conditions in the 3
(visual stimulus) x 3 (auditory stimulus) experiment. All participants
were between the ages of 35 and 55 years old. They were required to
drive an average of at least 12,000 miles per year, have a valid U.S.
driver's license, and have normal or corrected-to-normal vision. To be
eligible for the study, participants could not have participated in
similar experiments, had to agree not to take driving-impairing drugs at
least 4 hrs before their scheduled study, and could not work on the
products that were being tested in the experiments. Participants were
paid for their participation with a \$30 gift certificate to the "Best
Buy" store.

***Apparatus***

Equipment used in this experiment included a fixed-base driving
simulator at Delphi Electronics and Safety Systems in Kokomo, Indiana.
The simulator utilized DriveSafety™ software and projected the simulated
environment on a 1024x768-pixel 50-deg-diagonal forward field-of-view
image located at the front bumper of the vehicle cab. To simulate the
feel of the road, steering feedback was presented with a force-feedback
torque motor. The vehicle cab consisted of the front half of a 1995
Pontiac Bonneville exterior (with doors and roof removed). The simulator
included a speaker that produced the auditory warning, which was located
directly in front of the driver on the dashboard and presented the
auditory component of the warnings at approximately 70 dB. To simulate a
HUD for the HUD-based icon visual stimulus, the icon shown in Figure 9.7
was displayed directly on the main driving simulator screen, and spanned
approximately 1 deg^2^. Both the exogenous and HUD icon visual displays
were presented on the driver's centerline, however, whereas the HUD-icon
was presented just above the vehicle hood, the exogenous display was
presented just below the horizon for most drivers. Although the HUD-icon
was presented using the brightest red color available on the projector
screen, the exogenous display was considerably brighter, mirroring the
real-world brightness of an exogenous display in comparison with
conventional HUD images.

![](media/image8.png){width="1.054861111111111in"
height="0.7548611111111111in"}

***Figure 9.7.** FCW icon presented in the location of a HUD.*

A HoMedics BK 4500 Back Massager was used for the haptic-seat condition.
This consumer product is a car-seat overlay with offset motors used to
create vibration. It was modified to accommodate the needs of the
experiment by adjusting it to provide vibration in four locations in the
bottom of the seat when the warnings were issued. The motors used to
provide the vibration on the back of the seat were disabled. The seat
vibration was issued as a series of four bursts at 4 Hz with a 50 % duty
cycle.

***Design***

A 3 x 3 between-subjects factorial design was used, where the three
types of visual stimuli used to alert the driver (exogenous display,
HUD-icon display, and no visual stimulus) were factorially crossed with
the three types of auditory stimuli used to alert the driver (car-horn
sound, tonal sound, and no auditory stimulus). The tonal sound that was
used was the FCW sound used in the ACAS FOT program (which was a series
of four 725-Hz beeps at 4 Hz with a 50 % duty cycle). Both sounds
spanned a 1-s duration.

In addition to the nine conditions in the factorial design, a condition
was added to evaluate the effectiveness of a haptic (seat vibration)
stimulus compared to an auditory stimulus. This condition used a
combination of the exogenous display and the haptic seat vibration.

The following dependent measures were analyzed:

*[Response-Time Measures:]{.underline}*

1.  Glance Reaction Time (GRT): The time interval between the lead
    vehicle braking event and the driver's eyes being returned to the
    forward roadway. Due to missing video data, there were nine missing
    values for GRT and GAT.

2.  Accelerator Release Time (ART): The time interval between the lead
    vehicle braking and the driver completely releasing the accelerator
    pedal.

3.  Brake Reaction Time (BRT): The time interval between the lead
    vehicle braking and the driver beginning to depress the brake pedal.

4.  Half Brake Time (HBT): The time interval between the lead vehicle
    braking and the driver depressing the brake half of the maximum.

*[Transition-Time Measures:]{.underline}*

5.  Glance-to-Accelerator Time (GAT): The time interval between the
    glance return and the accelerator release (ART -- GRT).

6.  Pedal Transition Time (PTT): The time interval between the
    accelerator release and the beginning of the brake depression (BRT
    -- ART).

7.  Brake Travel Time Half (BTT): The time interval between the driver
    beginning to depress the brake pedal and the brake pedal being
    depressed half of the maximum (HBT -- BRT).

*[Threat-Severity Measures:]{.underline}*

8.  Minimum Time-to-collision (TTC): The minimum first-order (including
    only position and velocity rather than accelerations)
    time-to-collision between the lead-vehicle-braking event and when
    the host vehicle is stopped. In cases where drivers crossed the
    plane of the lead vehicle, the TTC was zero.

9.  Collision Velocity (CV): The differential velocity of the lead and
    host vehicles at the moment that the host vehicle crosses the
    longitudinal plane of the rear bumper of the lead vehicle. In the
    rare cases where the driver steered around the lead vehicle,
    collision velocity was still calculated when the front of the host
    vehicle crosses the plane of the lead vehicle. In cases where
    drivers stopped before crossing the plane, the collision velocity
    was zero.

Eight of the nine variables were time measures (s), with the one
exception being collision velocity (m/s). In addition to the MANOVA
variables, the rate of collisions for each group was also recorded.

The pilot testing in Phase I demonstrated that the variability in
reaction times and driver behaviors at the time of the braking event
required that the data of some participants needed to be rejected. To
increase the sensitivity of the between-subjects experiment, and to
avoid the necessity of a much larger number of participants, several
rules were set up a priori for the removal of data under certain
circumstances. Before testing began, it was decided that data from an
event would be rejected if it violated any one of the following
criteria:

1.  Speed was not maintained between 60 and 70 mph at the time of the
    braking events.

2.  The driver did not glance down at the scrolling text before
    responding to the braking event.

3.  The reaction time was two standard deviations outside the mean for
    the condition.

The first criterion was necessary primarily because of the lead vehicle
enforcing the headway. Because the lead vehicle would speed up or slow
down in order to achieve the specified headway, it was observed that
some drivers rejected the targeted headway and tried to close the gap by
speeding up or open the gap by slowing down. Furthermore, some drivers
were not comfortable with the task of reading the scrolling text and so
did not glance away just prior to the braking event, requiring the
second rejection criterion. Participants who violated the first and
second criteria were replaced with additional participants, however,
participants violating the variability criterion (criterion 3) were
removed without replacement after all testing was complete. Of the
hundred subjects who participated in the study, the data of five
participants (no more than one per condition) were removed as a result
of the third rejection criteria and were not replaced.

***Procedure***

Upon their arrival at the driving simulator at Delphi Electronics and
Safety, participants signed informed-consent forms and received a \$30
Best Buy gift certificate. After participants were shown how to adjust
the seat height and steering wheel position in the simulator, the
overhead room lights were dimmed. The researcher reminded all
participants that if they felt any discomfort during the experiment they
were to stop driving and alert the researcher immediately. All
participants were then asked to press the brake pedal once, which
prompted an instruction slide to appear on the rear-projection screen in
front of them. Every participant saw the same experimental agenda that
indicated that there would be four drives lasting 7 min, 15 min, 15 min,
and 15 min respectively (see Figure 9.8). In actuality, each participant
drove two drives, lasting 10 min and approximately 5 min, respectively.
The purpose of this deception was to reduce as much expectation as
possible that the lead vehicle was going to brake suddenly, creating an
imminent braking situation.

The slide on the screen indicated that the purpose of the study was to
"investigate how FCW systems may be designed to reduce the effects of
driver distraction" and that a FCW system works by using a radar that
"measures the range and closure rate of the vehicle in front and
calculates the threat of imminent collision" suggesting that the driver
needs to "brake immediately to avoid colliding with the lead vehicle".
The researcher also read the agenda, purpose, and FCW explanation aloud.
Participants were then instructed to press the accelerator pedal once to
activate the FCW stimuli.

![](media/image9.png){width="4.0in" height="3.033333333333333in"}

***Figure 9.8.** Experimental agenda shown to the participants on the
driving simulator screen.*

Next, the researcher pointed out the console screen to all participants
and briefly explained that scrolling text would appear there to offer
instructions and navigation information. Participants were then
instructed to press the accelerator pedal one time to begin Drive 1. The
researcher left the room at this time. As participants drove, in-vehicle
text messages presented information about the appropriate speed to
travel at, which highway exits to take, and what turns to make. When no
instructions were present, the center-console was completely black,
however, when the text was about to appear, the screen would light up
green, presenting a black bar in the center of the bottom half of the
screen on which the text would scroll across.

At the completion of Drive 1, the scrolling text indicated that "the
next drive is also practice but will have a greater emphasis on
navigation, so pay close attention for important instructions." All
participants were told that Drive 2 would end when they "park next to
the red SUV". In actuality, Drive 2 ended 5 min after it began when the
lead vehicle decelerated at a rate of -5 m/s^2^. Pilot testing in Phase
I had revealed that deception was necessary to reduce the driver's
expectations that the lead vehicle was going to brake. Although the
instructions suggested that the center console was present to provide
instructions, the major purpose of the center console was to provide a
severe visual distraction for the driver. Five hundred msec before the
lead-vehicle-braking event began in Drive 2, scrolling text emerged on
the center console, telling the driver "when you get to the next
intersection, take the west (2nd) highway exit". Other than the presence
of the scrolling text, no clues were present in the simulation to allow
the driver to predict that the lead vehicle would brake at the time it
did. For several minutes prior to the braking event, the lead vehicle
began stabilizing the distance between itself and the host vehicle, by
adjusting speed as necessary to achieve the target time headway of 2 s.

## 9.3.2 Experiment 3 Results

A between-subjects MANOVA was conducted on the nine dependent measures
for the 3 x 3 factorial combination of visual and auditory stimuli,
excluding the haptic seat condition. The nine dependent measures that
were recorded included Glance Reaction Time (GRT), Accelerator Release
Time (ART), Brake Reaction Time (BRT), Half Brake Time (HBT),
Glance-to-Accelerator Time (GAT), Pedal Transition Time (PTT), Brake
Travel Time Half (BTT), Minimum Time-to-collision (TTC), Collision
Velocity (CV). Because such a large set of interdependent variables were
used in this analysis, a correlation matrix was created and is displayed
in Table 9.4.

***Table 9.4.*** Dependent Measures Correlation Matrix.

<table>
<colgroup>
<col style="width: 6%" />
<col style="width: 8%" />
<col style="width: 11%" />
<col style="width: 10%" />
<col style="width: 11%" />
<col style="width: 11%" />
<col style="width: 10%" />
<col style="width: 11%" />
<col style="width: 8%" />
<col style="width: 10%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="2"></td>
<td>Threat Severity</td>
<td colspan="4"><p>Response</p>
<p>Time</p></td>
<td colspan="3"><p>Transition</p>
<p>Time</p></td>
</tr>
<tr class="even">
<td colspan="2"></td>
<td>TTC</td>
<td>GRT</td>
<td>ART</td>
<td>BRT</td>
<td>HBT</td>
<td>GAT</td>
<td>PTT</td>
<td>BTT</td>
</tr>
<tr class="odd">
<td rowspan="4"><blockquote>
<p>Response</p>
<p>Time</p>
</blockquote></td>
<td>GRT</td>
<td><strong>-.578**</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>ART</td>
<td><strong>-.686**</strong></td>
<td><strong>.699**</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>BRT</td>
<td><strong>-.894**</strong></td>
<td><strong>.658**</strong></td>
<td><strong>.813**</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>HBT</td>
<td><strong>-.904**</strong></td>
<td><strong>.569**</strong></td>
<td><strong>.733**</strong></td>
<td><strong>.886**</strong></td>
<td></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td rowspan="3"><blockquote>
<p>Transition</p>
<p>Time</p>
</blockquote></td>
<td>GAT</td>
<td><strong>-.535**</strong></td>
<td>.256*</td>
<td><strong>.870**</strong></td>
<td><strong>.655**</strong></td>
<td><strong>.599**</strong></td>
<td></td>
<td></td>
<td></td>
</tr>
<tr class="even">
<td>PTT</td>
<td><em>-.150</em></td>
<td><em>-.176</em></td>
<td><strong>-.444**</strong></td>
<td><em>.116</em></td>
<td><em>.077</em></td>
<td><strong>-.458**</strong></td>
<td></td>
<td></td>
</tr>
<tr class="odd">
<td>BTT</td>
<td><em>.083</em></td>
<td>-.247*</td>
<td>-.256*</td>
<td><strong>-.347**</strong></td>
<td><em>.128</em></td>
<td><em>-.182</em></td>
<td><em>-.092</em></td>
<td></td>
</tr>
<tr class="even">
<td><blockquote>
<p>Threat Severity</p>
</blockquote></td>
<td>CV</td>
<td><strong>-.657**</strong></td>
<td><strong>.517**</strong></td>
<td><strong>.777**</strong></td>
<td><strong>.826**</strong></td>
<td><strong>.746**</strong></td>
<td><strong>.692**</strong></td>
<td><em>-.053</em></td>
<td>-.258*</td>
</tr>
</tbody>
</table>

\* Statistically significant at the 0.05 level (2-tailed)

\*\* Statistically significant at the 0.01 level (2-tailed)

As expected, the TTC measure was negatively correlated with most of the
other dependent measures, especially HBT and BRT, demonstrating that
later responses led to more threatening situations (lower TTCs).
Likewise, the CV measure was positively correlated with most of the
response measures, especially ART and BRT, showing that longer reaction
times led to higher collision velocities. The transition measures (GAT,
PTT, and BTT) show some interesting trends. Whereas GAT was positively
correlated with reaction times (ART, BRT, and HBT), the later
transitions (PTT and BTT) tended to be negatively correlated with
reaction times. This suggests a compensatory mechanism of responding
more quickly as the situation became more threatening. Whereas quick
responders had more time to transition through the later stages of the
response, drivers who reacted later needed to transition through the
braking stages as quickly as possible. For example, drivers who released
the accelerator (ART) or glanced back (GRT) later tended to transition
to the brake more quickly (PTT), evidenced by the negative correlations
(-0.44 and -0.46 respectively). Furthermore, drivers who began
depressing the brake (BRT) later tended to transition from zero to fifty
percent braking capacity (BTT) more quickly (evidenced by the negative
correlation of --0.35). The negative correlation of PTT with ART tends
to result in ART being a more sensitive dependent measure than BRT,
because the compensatory nature of the response diminishes the
between-group differences. The large correlations (near 0.9) between
some of the dependent measures reveal the redundancy in this large set
of variables. For example, HBT appears to provide little additional
information beyond BRT and both of these variables appear to be captured
by the TTC measure. The interval between the return glance and the
accelerator release (GAT) also appears to be somewhat redundant with
ART.

Table 9.5 displays the MANOVA results for the complete set of nine
dependent measures, resulting from the 3 (visual) x 3 (auditory)
factorial combination, excluding the add-on haptic condition. Whereas
the visual and auditory independent measures revealed significant
effects across all of the response time measures, they tended not to
affect the transition measures significantly. The only exception to this
was the Glance-to-Accelerator Time, which was affected significantly by
the Auditory Stimuli and the Visual Stimuli effect approached
statistical significance. Table 9.4 suggests that this measure is likely
to be more sensitive than the other transition measures because whereas
Glance-to-Accelerator Time is highly correlated with Accelerator Release
Time, the other two transitions (PTT and BTT) reveal the underlying
compensatory nature of the response. Because Glance-to-Accelerator Time
is the only transition-time variable that is sensitive, and it appears
to be relatively redundant with Accelerator Release Time, it might be
concluded that the transition-time variables provided relatively little
additional information to this analysis and might therefore be omitted
from further analyses (Experiments 4 and 5).

***Table 9.5.*** MANOVA Table for Experiment 4: Visual and Audible
Stimuli

<table>
<colgroup>
<col style="width: 4%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="2" rowspan="2"><p>Dependent</p>
<p>Measure</p></td>
<td colspan="4">Visual Stimuli</td>
<td colspan="4">Audible Stimuli</td>
<td colspan="4">Interaction</td>
</tr>
<tr class="even">
<td>df</td>
<td>F</td>
<td>p</td>
<td>ε<sup>2</sup></td>
<td>df</td>
<td>F</td>
<td>p</td>
<td>ε<sup>2</sup></td>
<td>df</td>
<td>F</td>
<td>p</td>
<td>ε<sup>2</sup></td>
</tr>
<tr class="odd">
<td rowspan="4"><blockquote>
<p>Response Time</p>
</blockquote></td>
<td>GRT</td>
<td>2,76</td>
<td>9.86</td>
<td>&lt;.001</td>
<td>.225</td>
<td>2,76</td>
<td>12.52</td>
<td>&lt;.001</td>
<td>.269</td>
<td>4,76</td>
<td>2.10</td>
<td>.090</td>
<td>.110</td>
</tr>
<tr class="even">
<td>ART</td>
<td>2,85</td>
<td>11.68</td>
<td>&lt;.001</td>
<td>.233</td>
<td>2,85</td>
<td>25.35</td>
<td>&lt;.001</td>
<td>.397</td>
<td><em>4,85</em></td>
<td><em>1.85</em></td>
<td><em>.127</em></td>
<td><em>.088</em></td>
</tr>
<tr class="odd">
<td>BRT</td>
<td>2,85</td>
<td>5.03</td>
<td>.009</td>
<td>.116</td>
<td>2,85</td>
<td>21.59</td>
<td>&lt;.001</td>
<td>.359</td>
<td>4,85</td>
<td>2.05</td>
<td>.096</td>
<td>.096</td>
</tr>
<tr class="even">
<td>HBT</td>
<td>2,85</td>
<td>4.87</td>
<td>.010</td>
<td>.112</td>
<td>2,85</td>
<td>22.85</td>
<td>&lt;.001</td>
<td>.372</td>
<td>4,85</td>
<td>4.26</td>
<td>.004</td>
<td>.181</td>
</tr>
<tr class="odd">
<td rowspan="3"><blockquote>
<p>Transition Time</p>
</blockquote></td>
<td>GAT</td>
<td>2,76</td>
<td>2.94</td>
<td>.060</td>
<td>.080</td>
<td>2,76</td>
<td>13.79</td>
<td>&lt;.001</td>
<td>.288</td>
<td><em>4,76</em></td>
<td><em>1.61</em></td>
<td><em>.182</em></td>
<td><em>.086</em></td>
</tr>
<tr class="even">
<td>PTT</td>
<td><em>2,85</em></td>
<td><em>1.70</em></td>
<td><em>.191</em></td>
<td><em>.048</em></td>
<td><em>2,85</em></td>
<td><em>1.24</em></td>
<td><em>.295</em></td>
<td><em>.035</em></td>
<td><em>4,85</em></td>
<td><em>0.89</em></td>
<td><em>.474</em></td>
<td><em>.050</em></td>
</tr>
<tr class="odd">
<td>BTT</td>
<td><em>2,85</em></td>
<td><em>1.92</em></td>
<td><em>.154</em></td>
<td><em>.054</em></td>
<td><em>2,85</em></td>
<td><em>0.39</em></td>
<td><em>.677</em></td>
<td><em>.011</em></td>
<td><em>4,85</em></td>
<td><em>1.07</em></td>
<td><em>.377</em></td>
<td><em>.059</em></td>
</tr>
<tr class="even">
<td rowspan="2"><blockquote>
<p>Threat Severity</p>
</blockquote></td>
<td>TTC</td>
<td><em>2,85</em></td>
<td><em>2.98</em></td>
<td><em>.056</em></td>
<td><em>.072</em></td>
<td>2,85</td>
<td>31.54</td>
<td>&lt;.001</td>
<td>.450</td>
<td>4,85</td>
<td>2.66</td>
<td>.039</td>
<td>.121</td>
</tr>
<tr class="odd">
<td>CV</td>
<td>2,85</td>
<td>6.85</td>
<td>.002</td>
<td>.151</td>
<td>2,85</td>
<td>8.23</td>
<td>&lt;.001</td>
<td>.176</td>
<td>4,85</td>
<td>2.42</td>
<td>.055</td>
<td>.112</td>
</tr>
</tbody>
</table>

Table 9.6 displays the means and standard errors for the 3 (visual) x 3
(auditory) factorial combination of visual and auditory stimuli and the
exogenous-plus-haptic condition. The means of the exogenous-plus-haptic
condition appear to be quite similar to those of the exogenous-only
condition across the dependent measures, and post-hoc t-tests did not
reveal any statistically significant differences (p \> 0.1). The sample
size was small resulting in a test of relatively low sensitivity,
however, the absence of a statistically significant result in
combination with the numerical similarity of the means suggests that the
addition of the haptic stimulus to the exogenous display provides little
benefit. The means of the nine conditions in the factorial combination
of visual and auditory stimuli are displayed in Figure 9.9 across the
four dependent measures (GRT, ART, BRT, and TTC).

***Table 9.6.*** Means and Standard Errors for the Different Conditions

<table>
<colgroup>
<col style="width: 4%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 8%" />
<col style="width: 9%" />
</colgroup>
<tbody>
<tr class="odd">
<td colspan="2">Auditory Stimuli</td>
<td colspan="3">None</td>
<td colspan="3">Tonal</td>
<td colspan="3">Car-Horn</td>
<td>Haptic</td>
</tr>
<tr class="even">
<td colspan="2">Visual Stimuli</td>
<td>None</td>
<td>HUD icon</td>
<td>ExD</td>
<td>None</td>
<td>HUD icon</td>
<td>ExD</td>
<td>None</td>
<td>HUD icon</td>
<td>ExD</td>
<td>ExD</td>
</tr>
<tr class="odd">
<td rowspan="4"><blockquote>
<p>Response</p>
<p>Time</p>
</blockquote></td>
<td>GRT</td>
<td><p>1.40</p>
<p>(0.11)</p></td>
<td><p>1.41</p>
<p>(0.09)</p></td>
<td><p>1.12</p>
<p>(0.10)</p></td>
<td><p>1.52</p>
<p>(0.10)</p></td>
<td><p>1.19</p>
<p>(0.10)</p></td>
<td><p>0.86</p>
<p>(0.10)</p></td>
<td><p>0.97</p>
<p>(0.10)</p></td>
<td><p>0.93</p>
<p>(0.10)</p></td>
<td><p>0.83</p>
<p>(0.10)</p></td>
<td><p>1.17</p>
<p>(0.11)</p></td>
</tr>
<tr class="even">
<td>ART</td>
<td><p>2.77</p>
<p>(0.17)</p></td>
<td><p>2.16</p>
<p>(0.17)</p></td>
<td><p>1.89</p>
<p>(0.17)</p></td>
<td><p>2.36</p>
<p>(0.17)</p></td>
<td><p>1.93</p>
<p>(0.17)</p></td>
<td><p>1.36</p>
<p>(0.18)</p></td>
<td><p>1.32</p>
<p>(0.18)</p></td>
<td><p>1.34</p>
<p>(0.18)</p></td>
<td><p>1.15</p>
<p>(0.18)</p></td>
<td><p>1.63</p>
<p>(0.18)</p></td>
</tr>
<tr class="odd">
<td>BRT</td>
<td><p>3.21</p>
<p>(0.17)</p></td>
<td><p>2.82</p>
<p>(0.17)</p></td>
<td><p>2.52</p>
<p>(0.17)</p></td>
<td><p>3.07</p>
<p>(0.17)</p></td>
<td><p>2.54</p>
<p>(0.17)</p></td>
<td><p>2.38</p>
<p>(0.17)</p></td>
<td><p>1.96</p>
<p>(0.17)</p></td>
<td><p>1.87</p>
<p>(0.17)</p></td>
<td><p>2.10</p>
<p>(0.17)</p></td>
<td><p>2.54</p>
<p>(0.18)</p></td>
</tr>
<tr class="even">
<td>HBT</td>
<td><p>3.67</p>
<p>(0.15)</p></td>
<td><p>3.30</p>
<p>(0.15)</p></td>
<td><p>3.13</p>
<p>(0.15)</p></td>
<td><p>3.59</p>
<p>(0.15)</p></td>
<td><p>3.04</p>
<p>(0.15)</p></td>
<td><p>2.87</p>
<p>(0.16)</p></td>
<td><p>2.47</p>
<p>(0.16)</p></td>
<td><p>2.27</p>
<p>(0.16)</p></td>
<td><p>2.89</p>
<p>(0.16)</p></td>
<td><p>3.09</p>
<p>(0.16)</p></td>
</tr>
<tr class="odd">
<td rowspan="3"><blockquote>
<p>Transition</p>
<p>Time</p>
</blockquote></td>
<td>GAT</td>
<td><p>1.39</p>
<p>(0.17)</p></td>
<td><p>0.75</p>
<p>(0.15)</p></td>
<td><p>0.96</p>
<p>(0.16)</p></td>
<td><p>0.93</p>
<p>(0.15)</p></td>
<td><p>0.76</p>
<p>(0.16)</p></td>
<td><p>0.50</p>
<p>(0.16)</p></td>
<td><p>0.35</p>
<p>(0.15)</p></td>
<td><p>0.42</p>
<p>(0.15)</p></td>
<td><p>0.32</p>
<p>(0.15)</p></td>
<td><p>0.46</p>
<p>(0.17)</p></td>
</tr>
<tr class="even">
<td>PTT</td>
<td><p>0.45</p>
<p>(0.17)</p></td>
<td><p>0.66</p>
<p>(0.15)</p></td>
<td><p>0.60</p>
<p>(0.16)</p></td>
<td><p>0.68</p>
<p>(0.15)</p></td>
<td><p>0.60</p>
<p>(0.16)</p></td>
<td><p>0.92</p>
<p>(0.16)</p></td>
<td><p>0.81</p>
<p>(0.15)</p></td>
<td><p>0.53</p>
<p>(0.15)</p></td>
<td><p>0.95</p>
<p>(0.15)</p></td>
<td><p>0.90</p>
<p>(0.16)</p></td>
</tr>
<tr class="odd">
<td>BTT</td>
<td><p>0.41</p>
<p>(0.12)</p></td>
<td><p>0.48</p>
<p>(0.10)</p></td>
<td><p>0.58</p>
<p>(0.11)</p></td>
<td><p>0.52</p>
<p>(0.11)</p></td>
<td><p>0.52</p>
<p>(0.11)</p></td>
<td><p>0.50</p>
<p>(0.11)</p></td>
<td><p>0.51</p>
<p>(0.11)</p></td>
<td><p>0.39</p>
<p>(0.11)</p></td>
<td><p>0.79</p>
<p>(0.11)</p></td>
<td><p>0.56</p>
<p>(0.12)</p></td>
</tr>
<tr class="even">
<td rowspan="2"><blockquote>
<p>Threat Severity</p>
</blockquote></td>
<td>TTC</td>
<td><p>0.56</p>
<p>(0.47)</p></td>
<td><p>1.01</p>
<p>(0.47)</p></td>
<td><p>1.93</p>
<p>(0.47)</p></td>
<td><p>0.86</p>
<p>(0.47)</p></td>
<td><p>2.08</p>
<p>(0.47)</p></td>
<td><p>2.73</p>
<p>(0.49)</p></td>
<td><p>4.16</p>
<p>(0.49)</p></td>
<td><p>4.90</p>
<p>(0.49)</p></td>
<td><p>3.44</p>
<p>(0.49)</p></td>
<td><p>2.56</p>
<p>(0.55)</p></td>
</tr>
<tr class="odd">
<td>CV*</td>
<td><p>9.9</p>
<p>(1.49)</p>
<p>7/10</p></td>
<td><p>3.9</p>
<p>(1.49)</p>
<p>6/10</p></td>
<td><p>2.5</p>
<p>(1.49)</p>
<p>3/10</p></td>
<td><p>7.0</p>
<p>(1.49)</p>
<p>5/10</p></td>
<td><p>0.8</p>
<p>(1.49)</p>
<p>1/10</p></td>
<td><p>1.4</p>
<p>(1.57)</p>
<p>2/9</p></td>
<td><p>0.0</p>
<p>(1.57)</p>
<p>0/9</p></td>
<td><p>0.0</p>
<p>(1.57)</p>
<p>0/9</p></td>
<td><p>1.2</p>
<p>(1.57)</p>
<p>1/9</p></td>
<td><p>2.95</p>
<p>(1.56)</p>
<p>3/9</p></td>
</tr>
</tbody>
</table>

\*CV cell also displays: number of drivers who crashed / number of total
drivers in the condition.

LSD pairwise comparisons for the visual stimulus variable revealed that
drivers reacted significantly faster to the exogenous display than the
HUD-icon for both GRT (p \< 0.01) and ART (p = 0.02). They also revealed
that drivers responded to the HUD-icon faster than when there was no
visual stimulus for ART (p = 0.02), BRT (p = 0.02), and TTC (p = 0.04).
Likewise, the exogenous display yielded significantly faster responses
than no visual display for all of the dependent measures shown in Figure
9.9 (GRT: p \< 0.01, ART: p \< 0.01, BRT: p \< 0.01, TTC: p = 0.04).
Figure 9.9 also displays the larger effect of the auditory stimulus. LSD
pairwise comparisons across the auditory stimulus variable revealed that
the car-horn was consistently superior to the tonal warning (p \< 0.01
for GRT, ART, BRT, and TTC) and no auditory stimulus (p \< 0.01 for GRT,
ART, BRT, and TTC), and that the tonal warning also yielded faster ARTs
than no auditory display (p \< 0.01).

![](media/image10.png)

***Figure 9.9.*** Means and Standard Errors plotted across all
combinations of visual and auditory stimuli for the Glance Reaction Time
(GRT), Accelerator Release Time (ART), Brake Reaction Time (BRT), and
Minimum Time-to-collision (TTC) dependent measures.

The interaction between the visual and auditory stimuli approached
significance for most of the response-time measures and reached
statistical significance for the TTC measure (see Table 9.5). Figure 9.9
suggests that whereas the visual stimulus effect was clearly exhibited
with no auditory stimulus[^7] or with the tonal stimulus[^8], it
appeared to reach a floor (GRT, ART, BRT) or ceiling (TTC) when combined
with the car-horn stimulus[^9], where the visual displays provided
little additional benefit. The car-horn appears not to benefit from a
visual counterpart. The exogenous display also appeared to be somewhat
effective as a lone stimulus, and was significantly superior to no
warning for the ART (p \< 0.001) and BRT (p = 0.004) measures.

## 9.3.3 Experiment 3 Discussion

This pattern of results supports for the following conclusions:

1.  The car-horn stimulus is more effective than the tonal stimulus
    under these experimental conditions and may not benefit from the
    addition of a visual stimulus (without a visual display).

2.  There is little evidence to suggest that the haptic-seat stimulus
    provides any additional benefit over an exogenous-only display.

3.  The exogenous display is at least as effective as the HUD-icon, and
    appears to provide faster accelerator release times.

The first conclusion represents the most dominant effect in the
experiment. Whereas the tonal stimulus alone produced no detectable
benefit, the car-horn appeared equally effective with or without a
visual counterpart. This result might be important for providing a
cost-effective human-machine interface for forward collision warning and
provides additional support for using an auditory icon for collision
warning. However, whereas the use of a tonal stimulus for collision
warning has been tested on real roadways in the ACAS FOT program and in
real production vehicles, a stimulus such as the car horn must be
evaluated more comprehensively before it is adopted. Using an auditory
stimulus that has already been learned may produce faster reaction times
in these circumstances, however, the natural mapping that produces this
benefit may also have the potential to cause confusion. For example,
when hearing the car horn, drivers may perceive the sound as being an
activation of their real car horn. This perception might lead to
annoyance especially when it is associated with nuisance or unnecessary
alerts. Graham (1999) suggests another potential difficulty, where the
horn may be misunderstood as the vehicle in the next lane or blind spot
sounding its horn. Because nuisance alerts often occur during lane
changes, this misunderstanding could be quite startling if the driver
believes that there may be a previously-unseen vehicle in the blind
spot. During the on-road testing of Task 11 (Data Fusion), this actually
occurred and the driver[^10] perceived the alert as being overly
startling. If this misunderstanding caused the driver to look at the
blind spot during an actual threat, the alert could actually be
counterproductive. This concern can only be adequately evaluated in the
context of real driving and was not evaluated in this experiment. For
this reason, although the car-horn stimulus appears to be a promising
candidate for further research, the SAVE-IT program proceeded with the
more conventional tonal alert.

The second conclusion is somewhat surprising but suggests that a
visual-plus-tone alert is likely to be more effective than a
visual-plus-haptic alert. This result mirrors our previous findings in
the ACAS FOT program (Smith, 2002) where we evaluated a different haptic
seat stimulus for an FCW system, prior to the field operation test.
Perhaps a vibration of larger magnitude could be more effective. Seat
vibration may also be a more effective candidate for an LDW system,
because like the car-horn, it can capitalize on the mappings of the
natural environment, where rumble strips produce a vibration that may
resonate in the seat. Unlike the car-horn, the vibration of the LDW
system being confused with its naturally-occurring counterpart may not
be problematic. In the lane departure scenario, the stimulus of rumble
strips and the action required (a counteracting steering correction) are
identical, thus preserving the stimulus-response mapping.

These results also provide some support for the conceptual diagram in
Figure 9.6, which predicts a benefit of an exogenous display over a
conventional icon-based display. Because the exogenous display does not
require the imaging complexities of a HUD, it is also an attractive
proposition for reducing the cost of the human-machine interface. The
exogenous display appears to present lower risk of driver confusion than
the car horn and it was thus selected as the visual stimulus for the
SAVE-IT program.

# 9.4. Experiment 4: LDW DOWN SELECTION

Up to this point in the SAVE-IT program, there had been little work
conducted to guide the implementation of Lane Departure Warning (LDW).
The experiment that is described in this section was designed to examine
the effectiveness of different warning stimulus alternatives and to
examine whether LDW is likely to provide benefit in the absence of
visual distraction. Similar in scope to the ACAS FOT, a second field
operational test, the RDCW ROT (the Road Departure Crash Warning System
Field Operational Test: Emery, Srinivason, et al., 2005, LeBlanc et al.,
2006) was carried out to evaluate the effectiveness of LDW. This program
was conducted using a similar design (78 drivers for four weeks) to
investigate a LDW and curve speed warning system (CSW) (Emery,
Srinivason, et al., 2005, LeBlanc et al., 2006). The CSW alerted drivers
of the need to reduce vehicle speed when approaching a sharp curve.
Unlike most LDW systems that are emerging on production vehicles, this
system was more elaborate and featured not only sensing of the lane
boundary but also radar sensors to detect whether there was an obstacle
beyond the lane boundary. The driver-vehicle interface for LDW used
auditory cues for lateral movements of the vehicle over either solid
painted lane markers or movements over dashed markers with an obstacle
near that lane boundary. Haptic (seat vibration) cues were designed to
give an impression similar to that of traveling over rumble strips. This
alert occurred when the driver traveled over a dashed boundary with no
detected obstacle. Common alerts, that might be considered to be
unnecessary, included alerts where the driver knowingly strayed over the
lane marker without consequence, or where the driver intentionally
changed lanes without using the turn signal. Although the LDW alert rate
per distance traveled was approximately six times greater for the RDCW
FOT than it was for the ACAS FOT, these drivers provided more favorable
acceptance ratings than the ACAS FOT drivers had for the FCW system
(LeBlanc et al., 2006).

David LeBlanc (personal communication, January 2007), who worked on both
FOTs, suggests a hypothesis for why the RDCW FOT yielded higher
acceptance ratings for LDW than the ACAS FOT had yielded for FCW, in
spite of the much higher alert rate. His hypothesis is that LDW is based
on a criterion that drivers were able to understand and accept (crossing
lane makers), compared with the FCW system that could potentially leave
drivers confused[^11]. Whereas lane markers provide an unambiguous
threshold that is visible to the driver for LDW, there is no immediately
visible or universally agreed-upon threshold for FCW. Opinions about
when an alert is warranted are likely to vary far less across drivers
for LDW compared with FCW, and drivers can accurately predict when LDW
alerts are likely to occur.

To investigate the effectiveness of different HMI candidates further, an
experiment was conducted within the SAVE-IT program. Three warning
stimuli were evaluated in the context of driver distraction in
comparison to no warning and to each other. The three warning conditions
and the no-warning condition were included in this experiment when the
driver was distracted. In addition to these four conditions, the no
warning condition was also applied to an attentive-driver condition.
This condition was collected primarily to examine whether there was any
room for additional improvement when the driver was attentive to the
forward scene. The primary hypothesis for this study is that a driver
whose visual system is oriented to the forward scene will immediately
detect a lateral disturbance and respond with a successful counteracting
response. This experiment provided a cursory evaluation of this
hypothesis by including the attentive baseline condition. Thus, the five
conditions of this **Warning Situation** variable that were evaluated
were:

1.  **D_No**: Visually distracted driver with no warning

2.  **N_No**: Non-distracted driver with no warning

3.  **D_ExAud**: Visually distracted driver with the exogenous display
    plus non-directional rumble-strip audio

4.  **D_HuAud**: Visually distracted driver with the HUD-icon plus
    non-directional rumble-strip audio

5.  **D_ExHap**: Visually distracted driver with the exogenous display
    plus directional haptic seat-vibration

One of the differences between LDW and FCW system is that events that
can trigger an LDW system are far more common than events that can
trigger a reasonably tuned FCW system. The fact that LDW alerts were six
times more prevalent than FCW alerts across the FOTs and that LDW alerts
are relatively easy to comprehend, suggests that LDW may not require
much explanation. Whereas a naïve driver may need to have the FCW system
explained to some extent, the LDW system might be reasonably understood
within the first few hours of exposure. To evaluate this hypothesis, the
five conditions above were factorially crossed with a **Training**
variable, such that half of the drivers in each condition experienced an
explanation of the LDW and a demonstration of the HMI prior to driving,
and the other half of the drivers were provided with no description or
demonstration of the lane departure warning system. An additional
hypothesis stemming from the higher frequency and more understandable
LDW alerts, was that the HMI would have less effect on the driver's
response, because the driver would be likely to quickly learn a
reasonable HMI. A small set of HMI candidates were selected to provide a
cursory evaluation of a broad range of alternatives.

## 9.4.1 Experiment 4 Method

***Participants***

Fifty male employees at Delphi Electronics and Safety Systems in Kokomo,
Indiana, participated in the study and were recruited, screened, and
paid using the same protocol established in Experiment 3.

***Apparatus***

In all aspects, except those pertaining to the warning systems, the
apparatus was identical to that of Experiment 3. Except for the end of
the experiment, LDW alerts were triggered whenever the driver crossed
over a lane marker. In order to precisely control the timing of alerts
at the end of the experiment and to reduce the variance around driver
responses, the final alert was triggered half a second after the lateral
disturbance began, regardless of the lane position. Experiment 4
utilized the same exogenous display as that used in Experiment 3. The
non-directional auditory tone produced a rumble-strip-like sound from
the forward speaker by providing a series of four 125-msec, 100-Hz
tones, at 4 Hz with a 50 % duty cycle. The directional seat-vibration
stimulus utilized the same seat-massager apparatus that was used in
Experiment 3, except that whereas the FCW alerts vibrated both sides of
the seat, LDW vibrated only on the side of the alert. The HUD was
simulated using the same method as Experiment 3, and presented the icons
shown in Figure 9.10 to the driver. When the driver crossed the lane on
the right side, the right lane marker of the icon would flash on and
off, and when the driver crossed the lane on the left side, the left
lane marker would flash on and off.

![](media/image11.png){width="1.4840277777777777in"
height="1.0590277777777777in"}

***Figure 9.10.** LDW icon presented in the location of a HUD. Left LDW
alerts would flash between the icon displayed on the left and middle,
and right LDW alerts would flash between the middle and right icons.*

***Design***

A 5 (Warning Situation) x 2 (Training) between-subjects factorial design
was used, where the five Warning Situations (D_No, N_No, D_ExAud,
D_HuAud, and D_ExHap) were factorially crossed with the two types of
Training (trained vs. untrained). Five participants were randomly
assigned to each one of the ten conditions. This small set of subjects
would unlikely provide sufficient power to explore an interaction,
however might provide sufficient power to examine the main effects. The
dependent measures included **Steering Reaction Time** (SRT), which was
defined as the time (s) between the imposed lateral disturbance and the
driver's corrective steering action and the **Maximum Lateral Error**
(MLE), defined as the lateral distance (m) from the center of the lane
to the maximum lateral distance away. When the driver reached a lateral
distance of 1.8 m, the driving simulator ceased to continue providing
lane position information and so the maximum lateral error variable was
bounded on the high end by 1.8 m.

***Procedure***

Other than the warning systems, training, and final event, the procedure
was identical to that of Experiment 3 and used the same scrolling text
instructions to both guide and distract the driver prior to the lateral
disturbance. The same deception of training drives and the same
distraction of the scrolling text instructions were used. In the trained
condition, the instructions were similar to those of Experiment 3,
except they informed participants that "the LDW system uses vision
processing to detect lane markers in images that are captured from a
forward-looking camera and warns the driver when the vehicle is about to
depart the lane". In the case of untrained drivers, they only viewed the
agenda portion of the slide, describing the upcoming drives, and were
not presented with the purpose of the study, the description of the LDW
system, or the demonstration of the LDW HMI.

During the course of the experiment, there were several instances in
which the driver was required to make a lane change. Because the
driving-simulator vehicle cab did not include a turn signal, every lane
change led to an LDW alert. Unlike Experiment 3, this meant that drivers
were exposed to several LDW events prior to the final imminent event. At
the same location that was used in Experiment 3 (approximately 5 min
into the second drive), the distracting instructions appeared at the
bottom of the center console area. Three quarters of a second later, the
actual steering input to the driving simulator was replaced with a
17-deg clockwise steering-wheel input that simulated a lateral
disturbance. This disturbance continued until the driver engaged in a
sudden corrective response, which was quantified as a 40 deg/s
counter-clockwise steering-wheel input.

Because speed was not as crucial for the warning event, the speed
rejection criterion (Criterion 1) established in Experiment 3 was not
applied in this experiment. Although it was planned that both the
distraction and variance rejection criteria (Criteria 2 and 3) would be
applied to this experiment, no participants violated the variance
criterion and so only the distraction criterion led to rejections. The
subjects who failed to be distracted at the moment of the alert were
replaced.

## 9.4.2 Experiment 4 Results

A between-subjects MANOVA was conducted on Steering Reaction Time (SRT)
and Maximum Lateral Error (MLE) for the 5 (Warning Situation) x 2
(Training) factorial combination. Consistent with the hypothesis of the
self-explanatory nature of LDW, training did not significantly affect
SRT, F(1,49) = 0.176, p = 0.68, or MLE, F(1,49) = 0.02, p = 0.90. The
means of the trained vs. untrained differed by only 40 msec and 2 cm for
SRT and MLE, respectively. Although there was insufficient power for
providing a reasonable test of the interaction, the small F-values
\[F(4,49) = 0.91 and 0.82 for SRT and MLE respectively\] might suggest
that these variables were unlikely to interact.

As expected, the Warning Situation did impact both the SRT, F(4,49) =
12.70, p \< 0.001, and MLE, F(4,49) = 6.85, p \< 0.001, variables. These
differences are plotted in Figure 9.11. Pairwise comparisons revealed
that the only statistically-significant differences (p \< 0.05) within
MLE, were between the distracted and non-distracted conditions. In each
case, the differences between the distracted conditions and the one
non-distracted condition were significant using an alpha level of 0.005.

![](media/image13.png){width="6.427777777777778in"
height="5.574305555555555in"}

***Figure 9.11.** Steering Reaction Time (s) and Maximum Lateral Error
(m) as a function of the single non-distracted-driver Warning Condition
N_No (No Warning), and the four distracted-driver Warning Conditions
D_HuAud (HUD icon plus Auditory), D_ExAud (Exogenous display plus
Auditory), D_ExHap (Exogenous display plus Haptic), and D_No (No
Warning). The boxes around the conditions represent groups that were not
statistically different (p \> 0.05) in LSD pairwise comparisons.*

The SRT measure was more sensitive than the MLE measure, revealing
pairwise-comparison differences not only between the distracted and
non-distracted conditions (all statistically-significant at the p \<
0.005 level), but also within the distracted drivers between each of the
warned conditions and the condition in which the driver was not warned
(all at the p \< 0.001 level). The differences between the three warned
conditions did not approach statistical significance (all with p-values
greater than 0.5).

## 9.4.3 Experiment 4 Discussion

This experiment provided some support for both hypotheses related to LDW
alerts being relatively self-explanatory: that training was unnecessary
and that HMI would have little effect on the driver's response to the
alerts (within reason). This finding does not necessarily that HMI is
completely irrelevant, because the HMI alternatives that were used
represent a relatively narrow range of stimuli. Each of the three
alternatives attempted to simulate rumble strips with either an auditory
or haptic stimulus. At the extreme, it is likely that an ineffective HMI
could be designed. However, at least within the relatively narrow set of
HMI candidates tested in this experiment, the three tested candidates
were equally effective. As a result, the SAVE-IT program is relatively
unconstrained to select from these alternatives. Due to the apparent
acceptance of the auditory and haptic stimuli used in the RDCW FOT
program, the SAVE-IT program selected alerting stimuli that included
both auditory and haptic stimuli, with the one exception that the
auditory stimulus provided directionality. By providing the auditory and
haptic stimuli in synchronization with each other, such that each was
pulsed at 4-Hz, the stimuli produced a more compelling simulation of
rumble strips, which seemed more like a single multi-modal stimulus than
two separate stimuli. In addition to the auditory and haptic stimuli,
the exogenous display was presented, however, in order to keep the
visual stimulus brief, the exogenous LDW visual stimulus was not
synchronized with the auditory and haptic stimuli and flashed 3 times at
5 Hz (like the FCW exogenous display). This combination of stimuli was
subjectively evaluated on real roadways within the Task 11 (Data Fusion)
task.

The swiftness of driver responses to the lateral disturbance supports
the hypothesis that visually-attentive drivers do not benefit from a LDW
system. As predicted by the conceptual diagram in Figure 9.3, in the
absence of a "contributing factor" (such as visual distraction), the
precipitating event (the lateral disturbance) was counteracted so
quickly that most participants did not even realize that it had
occurred. The average maximum lateral error for attentive drivers was
less than 0.5 m. This immediate and effective response from attentive
and alert drivers leaves little room for improvement and supports the
alert-suppression strategy for LDW. Although in a real system, the
alerts might also adapt to the driver's state of drowsiness or
impairment, these driver states are beyond the scope of the SAVE-IT
program. Thus visual distraction provides the only input as to whether
alerts are suppressed. Although the case for LDW was relatively clear,
further experiments were needed to determine the most effective
adaptation for FCW systems. Experiment 5 was designed to fulfill this
research need.

# 9.5. Experiment 5: FCW ADAPTATION DOWN SELECTION

In Phase I, an experiment was conducted to investigate the different FCW
adaptation candidates in a repeated-measures experiment. However,
because of the multiple imminent-braking events and the resulting
elevation of expectency, the safety benefit or cost of the different
alternatives was difficult to quantify. In Phase II, an experiment was
designed to investigate the different adaptation candidates for FCW
using a between-subjects methodology. This required that one set of
drivers experienced one half of the adaptation (positive) and another
set of drivers experience the other half (negative). Although the
between-subjects methodology is less sensitive, it avoids exposing each
driver to multiple imminent events. Instead, each driver is surprised
only once with the lead-vehicle-braking event and only across subjects
can the pattern of results be understood.

To create a set of conditions that could be informative about the
Differential Stimuli (suppressing the auditory component of the alert
when the driver is attentive) and the Alert Suppression (suppressing the
entire alert when the driver is attentive) candidates, **Driver
Distraction** (distracted vs. non-distracted) was factorially crossed
with **Warning** (Visual-plus-tone, Visual only, and no warning).
Because Experiment 3 supported the use of the exogenous display and
because the car-horn seemed to raise issues that might be tangential to
the goals of the SAVE-IT program, the exogenous display and ACAS FOT
tones were used to span the visual and auditory modalities. Table 9.7
displays the 2 x 3 factorial combination of conditions and describes how
the different cells in this matrix represent the behavior of the
different adaptation strategies.

***Table 9.7.*** Between-subject Adaptation Matrix for Experiment 5.

+----------+---------------+---------------------+---------------------+
| **Distr  | **No          | **Visual only**     | *                   |
| action** | Warning**     |                     | *Visual-plus-tone** |
+----------+---------------+---------------------+---------------------+
| **Di     | *No FCW       | *Not using auditory | Non-adaptive        |
| stracted | system*       | even for distracted |                     |
| D        |               | drivers*            | Differential        |
| rivers** |               |                     | Stimuli             |
|          |               |                     |                     |
|          |               |                     | Alert Suppression   |
+----------+---------------+---------------------+---------------------+
| **Non-Di | Alert         | Differential        | Non-adaptive        |
| stracted | Suppression   | Stimuli             |                     |
| D        |               |                     |                     |
| rivers** |               |                     |                     |
+----------+---------------+---------------------+---------------------+

Whereas Cell D2 represents the system behavior of both a non-adaptive
system and the Differential Stimuli and Alert Suppression adaptations
for the distracted drivers, the three adaptation strategies behave
differently for the non-distracted drivers and span Cells N0, N1, and
N2. Cell D1 is designed to help answer the questions of whether
suppressing the auditory tone only during non-distracted episodes is a
beneficial negative adaptation or whether the auditory tone just never
provides any benefit. Likewise, Cell D0 is designed to help answer
whether suppressing the alert is useful only when the driver is not
distracted, or whether the alerts should always be suppressed regardless
of the driver state. To examine the behavior of these different
adaptations across different driving conditions, this 2 x 3 matrix was
also factorially crossed with two **Headway** conditions (1-s vs. 2-s
time headways), creating a set of twelve conditions. For the 2-s headway
conditions, the distracted-driver conditions in Table 9.7 (Cells D0, D1
and D2) are identical to that of Experiment 3 and have already been
collected.

The twelve conditions that resulted from the factorial crossing of 2
(Distraction) x 3 (Warning) x 2 (Headway) provided an evaluation of two
the negative adaptation candidates (Differential Stimuli and Alert
Suppression) in comparison with a non-adaptive system. However, the two
major positive adaptation candidates were not represented in this set
(Differential Location and Differential Timing). To satisfy the need for
testing these two adaptation candidates, three additional conditions
were added to allow for t-test comparisons. Two conditions were added to
evaluate the positive side (earlier alerts) of the Differential Timing
adaptation.

The braking events that were developed in Experiment 3 and used in
Experiment 5 did not provide much room for earlier warnings, because the
2-s headway required that if the alert was issued 0.5 s after the
lead-vehicle-braking event[^12], the alert would only provide the driver
with approximately a 1.5-s reaction time[^13]. For this reason, a less
extreme event was simulated to allow a 2.5-s reaction time. To achieve
this, the lead vehicle had to brake at a rate of 3.7 m/s^2^ rather than
5 m/s^2^. If the FCW algorithm assumed that the host driver would brake
at 5 m/s^2^, an alert that was presented 0.5 s after the 3.7 m/s^2^
braking event would correspond to an assumed 2.5-s brake-reaction time.
By using this reduced braking rate, more room was provided for an
earlier warning. To compare this early warning to a more nominal
warning, another condition was added that used the 3.7-m/s^2^ braking
rate, except with the alert onset delayed by 1.5 s rather than 0.5 s.
The t-test between these two conditions would thus reveal the benefit of
warning distracted drivers earlier.

To evaluate the Differential Location adaptation, one condition was
added. This condition was identical to Cell 1 (exogenous-plus-tone) with
a 2-s headway and 5-m/s^2^ lead-vehicle-braking rate, except that the
FCW icon that was used in Experiment 3 was simultaneously displayed on
the center-console area. This icon filled the entire center-console
visual space of 7.5 H x 4.5 V inches and flashed at a rate of 4 Hz.
Comparing this condition to the same condition without the console-icon
allowed the evaluation of whether this addition benefited distracted
drivers.

## 9.5.1 Experiment 5 Method

***Participants***

One hundred and fifty employees at Delphi Electronics and Safety Systems
in Kokomo, Indiana, participated in the study and were recruited,
screened, and paid using the same protocol established in Experiment 3.
Ten participants were randomly assigned to each of the fifteen
conditions. As mentioned above, three of the conditions in this
experiment overlapped with Experiment 3 and include the same data set.

***Apparatus***

In all aspects, except those pertaining to the warning systems, the
apparatus was identical to that of Experiment 3, except that the
center-console was programmed to display the FCW icon for the
Differential Location add-on condition.

***Design***

The design included the 2 (Distraction) x 3 (Warning) x 2 (Headway)
between-subject factorial crossing of conditions and also three add-on
conditions that were designed to provide t-test comparisons. These
add-on conditions included the following:

1.  **Early Warning (2DE)**: The drivers were visually distracted and
    received the visual-plus-tone stimuli 0.5 s after the lead vehicle
    began braking at a rate of 3.7-m/s^2^ at a 2-s headway, simulating
    an algorithm that assumes a 2.5-s brake-reaction time.

2.  **Nominal Warning (2DN)**: The drivers were visually distracted and
    received the visual-plus-tone stimuli 1.5 s after the lead vehicle
    began braking at a rate of 3.7-m/s^2^ at a 2-s headway, simulating
    an algorithm that assumes a 1.5-s brake-reaction time.

3.  **Console-icon (2DC)**: The drivers were visually distracted and
    received a console-icon in addition to the visual-plus-tone stimuli
    0.5 s after the lead vehicle began braking at a rate of 5-m/s^2^ at
    a 2-s headway. This condition was added to provide a comparison to
    the Cell D1 in Table 9.7 for the 2-s headway condition to evaluate
    the potential benefit of the Differential Location adaptation.

Due to the redundancy revealed in Experiment 3 of the large set of
dependent measures a smaller set of measures was analyzed and included
the following five measures (defined in Section 9.3.1):

*[Response-Time Measures:]{.underline}*

1.  Accelerator Release Time (ART)

2.  Brake Reaction Time (BRT)

3.  Half Brake Time (HBT)

*[Threat-Severity Measures:]{.underline}*

4.  Minimum Time-to-collision (TTC)

5.  Collision Velocity (CV)

***Procedure***

Other than the addition of the 1-s headway conditions and the three
add-on conditions, the procedure was identical to that of Experiment 3.
The distance between host and lead vehicles was controlled in the same
manner as Experiment 3 just prior to the braking event for both the 1-
and 2-s headway conditions.

## 9.5.2 Experiment 5 Results

Table 9.8 displays the results of the 2 x 3 x 2 MANOVA, presenting the
degrees of freedom, F-values, and levels of significance across the five
dependent measures, that included Accelerator Release Time (ART), Brake
Reaction Time (BRT), Half-brake Time (HBT), Minimum Time-to-collision
(TTC), and Collision Velocity (CV). The table is shaded to highlight the
significant effects, and it appears to support the Experiment 3 finding
that ART is one of the more sensitive measures. Whereas main effects
were prevalent across the dependent measures for Distraction, Warning,
and Headway, the two-way interactions in most cases did not reach
significance. Although the Distraction x Warning interaction was
statistically-significant for ART and CV, it only approached statistical
significance for BRT and TTC. Similarly, the Warning x Headway
interaction reached statistical significance only for the Accelerator
Release Time variable. All five dependent measures suggest that
Distraction and Headway do not interact. The three-way interaction of
Distraction x Warning x Headway only approached statistical significance
for the most sensitive measure (ART).

***Table 9.8.*** MANOVA Table for Experiment 5: Distraction (D) x
Warning (W) x Headway (H)

  ------------- -------- --------------- --------------- --------------- ------------ ---------------
  **Measure**   **df**   **ART**         **BRT**         **HBT**         **TTC**      **CV**

  Distraction   1,115    47.59\*\*\*\*   37.23\*\*\*\*   12.91\*\*\*\*   8.26\*\*\*   18.30\*\*\*\*
  (D)                                                                                 

  Warning (W)   2,115    10.02\*\*\*\*   5.43\*\*        9.16\*\*\*\*    3.93\*       9.54\*\*\*\*

  Headway (H)   1,115    26.45\*\*\*\*   71.48\*\*\*\*   88.89\*\*\*\*   7.58\*\*     *1.66*

  D x W         2,115    6.43\*\*\*      2.87 (p = 0.06) *1.13*          2.61 (p =    8.93\*\*\*\*
                                                                         0.08)        

  D x H         1,115    *0.06*          *0.06*          *0.07*          *0.63*       *0.19*

  W x H         2,115    4.67\*          *2.11*          *0.32*          *0.05*       *2.01*

  D x W x H     2,115    2.98 (p = 0.06) *1.34*          *0.80*          *0.63*       *0.27*
  ------------- -------- --------------- --------------- --------------- ------------ ---------------

Table presents F-values, and displays p-values where approaching
statistical significance (0.05 \< p \< 0.10)

\* p \< 0.05, \*\* p \< 0.01, \*\*\* p \< 0.005, \*\*\*\* p \< 0.001
(Significant effects are shown in larger font and are shaded)

Table 9.9 displays the means and standard errors for the fifteen
conditions (1D0, 1D1, 1D2, 2D0, 2D1, 2D2, 1N0, 1N1, 1N2, 2N0, 2N1 and
2N2) and the three add on conditions (2DE, 2DN, and 2DC). The three-way
interaction for ART that approached statistical significance (p = 0.06)
is displayed in Figure 9.12, and suggests that the only case in which
warnings were effective was for distracted drivers in the 2-s Headway,
wherein warnings were extremely effective. The effect of warning was
more prevalent in the 2-s cases[^14], and in the 2-s cases, where the
nature of the warning effect appeared to vary between distracted and
non-distracted drivers. Whereas pairwise comparisons revealed
statistically significant differences between the different types of
warnings in the 2-s Headway conditions, there were no statistically
significant differences between different warnings in the 1-s Headway
condition for either distracted or non-distracted drivers (ART: p \>
0.1). Within the 2-s Headway condition, the warning effect produced the
expected pattern of results for the distracted drivers, where the
visual-plus-tone condition was responded to earlier than the visual-only
condition (ART: 2D1 \< 2D2, p = 0.02) and both of these conditions were
responded to earlier than the no-warning (ART: 2D2 \< 2D0 and 2D1 \<
2D0, p \< 0.01). For the non-distracted drivers in the 2-s Headway
condition, the unwarned drivers responded as quickly as the warned
drivers (ART: 2N2 \~ 2N0 and 2N1 \~ 2N0, p \> 0.25). Pairwise
comparisons revealed that the only difference that was statistically
significant (2N) was that between Visual-plus-tone and Visual-only (ART:
2N2 \< 2N1, p = 0.03). The greater effectiveness of warnings at the 2-s
Headway was reflected in the statistically significant Warning x Headway
interaction.

***Table 9.9.*** Means and Standard Errors for the Fifteen Conditions

+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
| Headway | Warning         |     | ART | BRT | HBT | TTC | CV  | Co |
| /       |                 |     |     |     |     |     |     | l. |
| Dist    |                 |     |     |     |     |     |     | /  |
| raction |                 |     |     |     |     |     |     | N  |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
| 1-s     | V               | 1D2 | 1   | 1   | 2   | 1   | 0   | 1  |
|         | isual-plus-tone |     | .44 | .85 | .13 | .45 | .83 | /8 |
| Dis     |                 |     |     |     |     |     |     |    |
| tracted |                 |     | (0. | (0. | (0. | (0. | (1. | 1  |
|         |                 |     | 17) | 16) | 18) | 51) | 32) | 3% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | Visual- only    | 1D1 | 1   | 2   | 2   | 0   | 3   | 6/ |
|         |                 |     | .57 | .05 | .48 | .65 | .04 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 6  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | No Warning      | 1D0 | 1   | 2   | 2   | 0   | 6   | 8/ |
|         |                 |     | .76 | .18 | .60 | .25 | .35 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 8  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
| 2-s     | V               | 2D2 | 1   | 2   | 2   | 2   | 1   | 2  |
|         | isual-plus-tone |     | .36 | .34 | .87 | .73 | .40 | /9 |
| Dis     |                 |     |     |     |     |     |     |    |
| tracted |                 |     | (0. | (0. | (0. | (0. | (1. | 2  |
|         |                 |     | 16) | 15) | 17) | 48) | 25) | 2% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | Visual- only    | 2D1 | 1   | 2   | 3   | 1   | 2   | 3/ |
|         |                 |     | .89 | .52 | .13 | .93 | .52 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 3  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | No Warning      | 2D0 | 2   | 3   | 3   | 0   | 9   | 7/ |
|         |                 |     | .77 | .21 | .67 | .56 | .89 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 7  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
| 1-s     | V               | 1N2 | 0   | 1   | 1   | 1   | 1   | 3/ |
|         | isual-plus-tone |     | .93 | .53 | .93 | .59 | .62 | 10 |
| Not-Dis |                 |     |     |     |     |     |     |    |
| tracted |                 |     | (0. | (0. | (0. | (0. | (1. | 3  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | Visual- only    | 1N1 | 0   | 1   | 1   | 2   | 0   | 1/ |
|         |                 |     | .90 | .39 | .90 | .12 | .48 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 1  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | No Warning      | 1N0 | 0   | 1   | 2   | 1   | 0   | 1  |
|         |                 |     | .95 | .45 | .25 | .61 | .09 | /9 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 1  |
|         |                 |     | 16) | 15) | 17) | 46) | 25) | 1% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
| 2-s     | V               | 2N2 | 1   | 2   | 2   | 2   | 0   | 1/ |
|         | isual-plus-tone |     | .19 | .02 | .80 | .53 | .93 | 10 |
| Not-Dis |                 |     |     |     |     |     |     |    |
| tracted |                 |     | (0. | (0. | (0. | (0. | (1. | 1  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | Visual- only    | 2N1 | 1   | 2   | 2   | 2   | 0   | 1/ |
|         |                 |     | .67 | .33 | .91 | .19 | .78 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 2  |
|         |                 |     | 15) | 15) | 16) | 46) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | No Warning      | 2N0 | 1   | 2   | 3   | 2   | 2   | 2/ |
|         |                 |     | .44 | .32 | .12 | .18 | .27 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         |                 |     | (0. | (0. | (0. | (0. | (1. | 1  |
|         |                 |     | 15) | 15) | 16) | 48) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
| 2-s     | Early           | 2DE | 1   | 2   | 3   | 3   | 0   | 0  |
| Dis     |                 |     | .51 | .26 | .24 | .10 | .00 | /9 |
| tracted | *(lead braking  |     |     |     |     |     |     |    |
|         | 3.72 m/s^2^)*   |     | (0. | (0. | (0. | (0. | (0. | 0% |
|         |                 |     | 22) | 22) | 29) | 75) | 00) |    |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | Nominal         | 2DN | 1   | 3   | 4   | 1   | 0   | 2/ |
|         |                 |     | .98 | .11 | .06 | .93 | .97 | 10 |
|         | *(lead braking  |     |     |     |     |     |     |    |
|         | 3.72 m/s^2^)*   |     | (0. | (0. | (0. | (0. | (0. | 2  |
|         |                 |     | 15) | 17) | 19) | 39) | 67) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+
|         | Console icon    | 2DC | 1   | 2   | 2   | 2   | 1   | 2/ |
|         | with            |     | .51 | .23 | .93 | .45 | .31 | 10 |
|         |                 |     |     |     |     |     |     |    |
|         | V               |     | (0. | (0. | (0. | (0. | (1. | 2  |
|         | isual-plus-tone |     | 15) | 15) | 17) | 49) | 18) | 0% |
+---------+-----------------+-----+-----+-----+-----+-----+-----+----+

Although the reaction times are not highly indicative of an effect in
the 1-s distracted-driver (1D) conditions, the differences between
Visual-plus-tone and non-warned began to emerge in the later measures.
For example, the difference between warned and unwarned HBTs approached
statistical significance (1D2 \< 1D0, p = 0.06; 1D1 \< 1D0, p = 0.06)
and the CV warning effect reached statistical significance (1D1 \< 1D0,
p \< 0.05; 1D2 \< 1D0, p \< 0.01). The difference between the number of
collisions further implies that warning distracted drivers at short
headways can produce a benefit, with drivers warned by the
Visual-plus-tone stimulus (1D2) crashing 13 % of the time compared with
unwarned drivers (1D0) crashing 80 % of the time, χ^2^(1) = 13.05, p \<
0.01.

![](media/image14.png)

***Figure 9.12.** The three-way interaction (Headway x Distraction x
Warning) that approached statistical significance for Accelerator
Release Times (s).*

Figure 9.13 displays the Warning x Distraction interaction across the
ART, BRT, TTC, and CV measures. This interaction reached statistical
significance for ART, F(2,115) = 6.43, p \< 0.01, and CV, F(2,115) =
8.93, p \< 0.01, and approached statistical significance for BRT,
F(2,115) = 2.87, p = 0.06, and TTC, F(2,115) = 2.61, p = 0.08. The
pattern of the warnings having little effect on non-distracted drivers
and a large effect on distracted drivers appears to be consistent across
the dependent measures. This trend supports the general philosophy of
negative adaptation (suppressing or softening warnings when a driver is
attentive). At least in the scenario that was simulated here, when the
drivers were attentive, warnings failed to show any signs of providing a
benefit. Thus, this experiment suggests that neither the Differential
Stimuli (suppressing auditory) or Alert Suppression adaptations are
likily to compromise the safety of an FCW system. This figure also
suggests that the visual-plus-tone warning was able to negate the effect
of distraction.

The main effect of distraction was evident across all dependent
measures, with distracted drivers responding more slowly and
consequently being exposed to greater risk. Whereas 47 % of distracted
drivers crashed, only 15 % of the drivers who were not distracted
crashed. The main effect of Headway was also evident across most of the
measures, with drivers at shorter headways reacting more quickly. The
faster reaction times at shorter headways compensated for the more
threatening initial situation, ultimately leading to little difference
in CV or collision percentage (35 % of drivers crashed in the 1-s
condition and 27 % of drivers crashed in the 2-s condition). The 1-s
Headway did, however, translate to higher TTCs than the 2-s Headway.

The main effect of Warning was consistent across dependent measures,
however, ART was the only measure to reveal a significant difference
between Visual-plus-tone in comparison to Visual-only (p \< 0.05). Both
warnings were significantly different from the no-warning condition
across all dependent measures (p \< 0.05), except for TTC where the
difference between Visual-only and no warning approached statistical
significance (p = 0.08). The Visual-plus-tone condition was
significantly different from no warning (p \< 0.01).

The add-on conditions with the --3.72 m/s^2^ braking event (2DE and 2DN)
were created to examine the benefit of providing distracted drivers with
earlier warnings. The means and standard errors of these conditions were
displayed in Table 9.9, and one-tailed t-tests revealed that the earlier
warning led to shorter reaction times for ART, t(17) = 1.82, p = 0.04,
BRT, t(17) = 3.15, p \< 0.01, and HBT, t(17) = 2.43, p = 0.01. The 1-s
earlier warning translated into a 850-msec earlier BRT. The TTC, t(17) =
1.43, p = 0.09, and CV, t(17) = 1.37, p = 0.09, measures approached
statistical significance. The differences observed in the reaction time
measures provide support for the use of earlier warnings for distracted
drivers in the Differential Timing Strategy. However, the Differential
Location adaptation did not appear to be promising. The differences
between the condition with the console addition (2DC) in comparison to
the Visual-plus-tone (2D2) were relatively small, and although no
results reached statistical significance, in many cases the differences
went in the direction of a degradation of effectiveness.

![](media/image15.png){width="6.298611111111111in"
height="7.847222222222222in"}

***Figure 9.13.*** The two-way interaction (Distraction x Warning)
across dependent measures (Accelerator Release Time, Brake Reaction
Time, Time-to-collision, and Collision Velocity).

## 9.5.3 Experiment 5 Discussion

This experiment provided support for three out of four of the adaptation
strategies. Whereas the data suggest that neither of the negative
adaptations (Differential Stimuli or Alert Suppression) are likely to
compromise safety and that the positive side of the Differential Timing
strategy (earlier warnings) could provide a safety benefit, these data
did not support the Differential Alert Location strategy. The
experimental conditions provided the most likely scenario for the
console stimuli to provide a benefit, because the drivers were highly
distracted by material in the precise location of the additional warning
stimulus, and yet it still did not provide a benefit. This lack of
benefit might be anticipated from Figure 9.6 which predicts that icons
add an additional stage before the driver responds to the warning. Based
on this finding, the Differential Location adaptation was removed from
further consideration and was not considered in the on-road testing of
the Data Fusion (Task 11) task. The three other candidates were
investigated further. It should also be noted that whereas the
Differential Timing adaptation is a positive adaptation (designed to
provide a safety benefit during driver distraction), the Differential
Stimuli and Alert suppression adaptations that were tested in this
experiment are negative adaptations (designed to improve driver
acceptance during attentive driving). For this reason, they are not
mutually exclusive and could potentially be combined. Furthermore, the
negative side of Differential Timing (later rather than nominal alerts)
that was not tested in this experiment, could also be used and becomes
increasingly like an Alert Suppression adaptation as the timing is
pushed further back, making an alert increasingly unlikely. An
adaptation strategy could potentially include multiple manipulations
from these candidates, even within the positive or negative side. For
example, an alert could be both later and without an auditory stimulus
when the driver is attentive.

This experiment demonstrated that suppressing the entire warning
compromised safety no more than just suppressing the auditory warning.
To evaluate the overall effectiveness of these two candidates, the
affects on acceptance need to be considered. The philosophy behind this
research was to separate the evaluation of the impact on safety from the
evaluation of acceptance. Whereas safety was ideally investigated in
this tightly-controlled between-subject driving simulator paradigm,
acceptance required on-road testing. This on-road testing counterpart,
necessary for the final selection of adaptation for the Evaluation task
(Task 14), was transferred to the Data Fusion (Task 11) task.

# 9.6. CONCLUSIONS

The effectiveness of the different adaptation strategies were evaluated
in a series of between-subject driving simulator experiments and the
subjective evaluation was transferred to Task 11b (Data Fusion of Safety
Warning Countermeasures), where acceptance could be studied on real
roadways. In order to examine the benefit of providing visual or
auditory FCW stimuli while a driver is not distracted, a
between-subjects design was used that factorially crossed the three
display alternatives (visual-plus-tone, visual-only and no warning) with
the presence of visual distraction. This design was used to provide some
data about the potential safety cost of the suppression and
auditory-suppression strategies that attempt to increase driver
acceptance. The result suggested that neither strategy of suppressing
FCW completely (Alert Suppression) or suppressing the auditory component
of the FCW stimulus (Differential Stimuli) were likely to compromise
safety. The selection of one over the other requires further
consideration from a driver-acceptance standpoint. The goal of these two
negative adaptations is to improve acceptance without compromising
safety, and the research conducted under this task only addresses the
latter issue. The selection of the preferred negative adaptation method
might also consider other types of driver degradation, such as driver
impairment or drowsiness.

The CAMP workload metrics project (Angell, Anflick, Austria, Kochhar,
Tijerina, Biever, Diptiman, Hogsett, & Kiger, 2006) data suggested that
auditory-vocal tasks can actually lead to a reduction in the standard
deviation of lane position (SDLP), supporting the Task 9b (Phase I
Safety Warning Countermeasures) conclusion that cognitive distraction
does not degrade lane keeping. The case for LDW may therefore be quite
clear cut: in the absence of either visual distraction or driver
drowsiness, if the vehicle is departing the lane, the driver is likely
to be already aware of it. When a driver is already aware of the lane
departure, even if it is unintentional, they still unlikely to benefit
from the warning.

The extent to which FCW alerts may assist drivers who are cognitively
distracted may be more uncertain. Many studies have demonstrated that
cognitive distraction can have a relatively small yet detectable effect
on driver reaction times to a lead-vehicle conflict (Horrey & Wickens,
2006). Reyes and Lee (2004) examined whether an auditory-vocal task
increased driver reaction times to a lead-vehicle conflict. Although the
auditory-vocal task had a small effect on conflicts that could not be
anticipated, the effect emerged when the conflict could be anticipated,
increasing accelerator release times by as much as 0.6 s. Reyes and Lee
proposed that although auditory-vocal tasks had relatively little effect
on the control level of driving, these tasks could degrade performance
at the tactical level (Task 5: Cognitive Distraction). Whereas
cognitively-attentive drivers were receptive to the clues that the
conflict was about to occur, responding earlier when the clues were
present, cognitively-distracted drivers did not appear to use the clues.
Therefore, although cognitive distraction clearly has a smaller effect
on a driver's ability to respond to rear-end conflicts, it is still
uncertain whether FCW alerts are completely redundant for situations
involving cognitive distraction alone (e.g., talking on a cell phone).
In the 100-Car Study data, two out of the fifteen rear-end crashes
occurred just after the driver appeared to be "lost in thought" or
"daydreaming". It is difficult to predict whether an FCW system would
have been successful in preventing these collisions, but this
uncertainty may suggest that even if cognitive distraction cannot easily
be measured, perhaps it should still be accounted for. For example,
rather than suppressing an FCW alert completely in the absence of visual
distraction, perhaps the alert should be softened or delayed. For this
reason, a negative adaptation such as Differential Timing or
Differential Alert Modalities might be a safer choice than Alert
Suppression for FCW, because they may allow for the possibility of
providing some benefit for drivers who are severely cognitively
distracted or drowsy if such drivers are able to benefit from the
warnings. Whereas the total suppression of an alert when the driver is
visually-oriented to the forward scene would not provide assistance for
driver states other than visual distraction, the other negative
adaptations may allow for other driver-states (even if they cannot be
detected) that may be interfering with a successful avoidance response.

The positive adaptations of Differential Location and Differential
Timing were also evaluated for FCW in Phase II to determine whether
these adaptations could improve the safety benefit of conventional
warning systems. The Differential Location adaptation attempted to speed
up drivers' response to the warning by displaying the FCW visual icon
(redundantly with the exogenous display) in the location of the driver's
distraction (in this case the center console). These data did not
support this adaptation method, and even showed a slight increase in
accelerator release time that was not statistically significant when the
redundant console (1.51 s) was compared to just an exogenous display
(1.36 s). The Differential Location adaptation was therefore rejected as
an adaptation alternative. Another evaluation compared the responses of
distracted drivers for an early FCW alert (0.5 s after the lead vehicle
braking event) versus a late FCW alert (1.5 s after the braking event).
The brake reaction time results revealed that the earlier warning
translated into a significantly earlier response, with drivers who
experienced the earlier alert braking 2.3 s after the event compared
with the late-alert drivers who began braking 3.1 s after the event. The
final selection of adaptation strategies and the human machine interface
was reserved for the on-road Task 11b work (Adaptive Safety Warning
Countermeasures Data Fusion).

As sensing technologies provide increasingly accurate and sensitive
information about the state of the driver, the methods for adaptation
will likely evolve to make use of the new information. For example,
whereas head-pose provides a coarse indication of visual distraction
only, future systems will provide information about where the driver's
eyes are focused. This more fine-grained information will likely support
the detection of both cognitive distraction (Reyes & Lee; in
preparation) and driver intention (Smith & Zhang, 2004a). Although the
price of automotive-grade technology with sufficient resolving power to
support the detection of cognitive distraction may currently be
excessive, such technology is likely to become increasingly affordable,
soon providing adaptive systems with information regarding the driver's
cognitive state (see Reyes & Lee, In preparation). Increased resolving
power may also eventually support the detection of driver impairment due
to alcohol or other drugs. The adaptation techniques that were developed
in this task did not appear to be suitable for cognitive distraction
(Smith & Zhang, 2004a), however, more subtle and sophisticated
techniques may be developed to make use of this information. One of the
challenges of adapting collision warning systems to the driver's
cognitive state may be that whereas the criteria for a driver's visual
orientation might be easily observed and understood, the criteria for
more complex phenomena such as the degree to which the driver is
mentally engaged in the driving task may be more subjective and less
easily understood by the driver. However, even if the driver's cognitive
state is not the subject of the adaptation, the potential for cognitive
distraction might still be taken into account by an adaptation that does
not completely suppress the warning (e.g., Differential Stimuli
adaptation).

The extent to which drivers will accept adaptive collision warnings may
also be influenced by how these systems are marketed to the public.
Drivers who have an inadequate understanding of the system might
perceive the warning behavior as inconsistent when an alert is provided
in one instance (when the driver's head pose is not forward) but not in
another (when the driver's head pose is forward). Such drivers might
view this system as failing to provide a safety benefit. If unnecessary
alerts do not degrade driver's trust (Lees & Lee, in preparation),
drivers may prefer to witness the system issuing an alert, even when the
system may be able to predict that the alert is unnecessary. Drivers who
understand the concept of adaptation may prefer a system that provides a
subtle alert (e.g., haptic or visual only) when they are attentive
rather than one that suppresses the alert completely, because it may
continue to reinforce that the system is providing the protection and is
accurately detecting the lane change or the lead vehicle braking. The
driver's mental model is thus a crucial factor in determining the
acceptance of different adaptation techniques. How an adaptive system is
perceived or which type of adaptation is preferred may therefore be
dependent on how these systems are marketed or how they are sold on the
show-room floor. As adaptive and non-adaptive systems increasingly
penetrate the market, they will further reveal the answers to these
kinds of questions.

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[^1]: In a comparison of steering reaction times for LDW events, the
    type of human machine interface appeared to make little difference.
    The exogenous display (1.10 s) and the HUD icon (1.12 s) appeared to
    be equally effective in decreasing the steering reaction times in
    response to a lane departure event, compared with no warning (1.67
    s).

[^2]: Driver impairment (such as drowsiness) is currently outside the
    scope of the SAVE-IT program and so was not considered.

[^3]: E.g., a lead vehicle braking or the host vehicle drifting out of
    the lane

[^4]: This finding may be limited to the particular type of adaptation
    strategies and cognitive distractions used. Task 5 suggests that
    cognitive distraction affects drivers in a qualitatively different
    manner than visual distraction, (degrading the situational awareness
    rather than the reactive response to a sudden and obvious threat),
    and may require a more subtle and sophisticated method of
    adaptation.

[^5]: The differences did not necessarily reach statistical
    significance.

[^6]: The point of origin of the optic flow field corresponding to the
    direction of where the self-motion is headed (Gibson, 1979).

[^7]: When there was no auditory stimulus, the exogenous display yielded
    faster Accelerator Release Times than no visual stimulus (p =
    0.014).

[^8]: When the tonal stimulus was presented, the exogenous display
    yielded faster Glance Reaction Times than the HUD-icon (p = 0.050)
    and no visual stimulus (p = 0.004) and the HUD-icon yielded faster
    Glance Reaction Times than no visual stimulus (p = 0.044).

[^9]: No pairwise comparisons between visual stimuli within the car-horn
    condition reached statistical significance at an alpha level of
    0.05.

[^10]: This driver was also the first author of this report.

[^11]: E.g., alerts caused by overhead out-of-path objects such as signs
    or peripheral objects such as vehicles in other lanes or cones on
    the roadside.

[^12]: Any sooner than 0.5 s after the braking event is likely to be
    unrealistic given the current sensing technology and the time
    required to differentiate velocity to provide reliable acceleration
    data.

[^13]: This is true if the algorithm assumes that the driver of the host
    vehicle will brake at the same rate as the lead vehicle, which in
    this case was -5 m/s^2^.

[^14]: The greater effectiveness of warnings in the 2-s conditions was
    revealed in a statistically significant interaction between Headway
    and Warning for Accelerator Release Time, F(2,115) = 4.67, p =
    0.01).