A Final Report of

SAfety VEhicles using adaptive Interface Technology (Phase II: Task 11):

Data Fusion for

Distraction Mitigation and

Safety Warning Countermeasures

Prepared by:

Matthew Smith

Deborah L. Bakowski

Gerald J. Witt

Harry Zhang

Delphi Electronics & Safety

Phone: (765)-451-9816

Email: <matt.smith@delphi.com>

January 2008

**Table of Contents**

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

[11.1. PROGRAM OVERVIEW 8](#program-overview)

[11.2. INTRODUCTION 9](#introduction)

[11.3. DISTRACTION MITIGATION PREPARATION
13](#distraction-mitigation-preparation)

[11.3.1. Method 13](#method)

[11.3.2. Results 14](#results)

[11.3.3. Discussion 26](#discussion)

[11.4. ITERATIVE ON-ROAD TESTING AND DEVELOPMENT
29](#iterative-on-road-testing-and-development)

[11.4.1. Method 29](#method-1)

[11.4.2. Results 31](#results-1)

[11.5. ON-ROAD ADAPTIVE WARNING VALIDATION
38](#on-road-adaptive-warning-validation)

[11.5.1. FCW Validation Method 38](#fcw-validation-method)

[11.5.2. FCW Validation Results 43](#fcw-validation-results)

[11.5.3. LDW Validation Method 54](#ldw-validation-method)

[11.5.4. LDW Validation Results 57](#ldw-validation-results)

[11.5.5. Discussion 65](#discussion-1)

[11.6. CONCLUSIONS AND SAVE-IT IMPLEMENTATION SUMMARY
68](#conclusions-and-save-it-implementation-summary)

[References 73](#references)

#  

# 11.0. EXECUTIVE SUMMARY

The data fusion task was a collection of studies that were necessary to
bridge the gaps between the component tasks (1-10) and the final system
integration (Task 13), in preparation for the Task 14 evaluations. Data
fusion consisted of three major efforts:

> **Distraction Mitigation Preparation**---The results of Task 2 were
> not yet ready for integration into the SAVE-IT system and so
> additional work was required in order to develop a functioning
> algorithm for detecting driving task demand.
>
> **Iterative On-Road Testing and Development**---Due to the complexity
> of the SAVE-IT systems, the optimal method for developing and refining
> these systems was a highly iterative process of development and
> on-road testing that examined the system behavior and subjective
> driver feedback.
>
> **On-Road Adaptive Warning Validation**---Task 9 primarily focused on
> the safety effectiveness evaluation of FCW and LDW. As a precursor to
> the evaluations (Task 14), additional on-road work was conducted to
> evaluate the acceptance of the adaptive FCW and LDW systems.

***Distraction Mitigation Preparation***

Vehicle data were generated by driving an instrumented vehicle on
various roads and traffic environments. From this raw data, one hundred
8-s video segments were selected that represented different levels of
traffic, road types, number of lanes and various driving maneuvers.
Based on the subjects' judgments, the video segments were assigned to
one of three demand categories (low, medium, and high).

The driving task demand increased with either a reduction of time
headway or a reduction in the normalized range between the host vehicle
and targets. As a result, the equation that produced the best prediction
of driving demand evaluated the number of targets surrounding the
vehicle, their directional movement (towards, away, stationary), the
minimum value between normalized range and time headway, and the
longitudinal distance between the host and targets. A weight was
computed for each of the video segments using this equation but adjusted
for yaw rate, GPS info, lane width, speed and brake pedal depression.
The resulting weights (predicted) were classified into three demand
groups: Low, Medium and High Demand. The predicted weights were
correlated with the median demand ratings yielding a correlation
coefficient of 0.79. Moreover, 75% of the videos matched the
algorithm-generated demand group and the median demand ratings group.
Examination of the mismatches showed that most occurred with borderline
cases and that the rating cut-off values could be adjusted to generate
better matches (increased to 93%).

Subjects also provided judgment on visual distraction feedback and on
demand-based advisories for IVIS functions. These judgments were
correlated with the median demand ratings. The results indicated that as
ratings of driving demand increase, so do judgments of visual
distraction and advisories for IVIS functions. Subject judgments also
indicated that when the driving task demand is high, drivers should be
advised to refrain from using the IVIS functions. Note that these
results replicate the results from Task 2 (Driving Task Demand).

***Iterative On-Road Testing and Development***

As an extension of previous, simulator-based experiments, on-road drives
were completed to observe the behavior of the adaptive interfaces on
real roadways and to collect a wide range of subjective measures. The
drives were conducted as part of an iterative process that allowed for
immediate cycles of implementation, testing and refinement based on both
subjective and objective measures. This iterative refinement process
afforded a data-driven methodology to support this complex data-fusion
task. As the performance of the component systems and their resultant
interactions with the overall system became apparent, system
functionality could be modified to provide more effective and acceptable
SAVE-IT adaptations.

Twelve drivers participated in this study across a two-month period.
Each researcher-accompanied drive encompassed a variety of driving
environments including, highway, interstate, city, suburban retail
district, and wooded/hilly, and took place during either morning or
afternoon. These naïve-subject drives were also supplemented with
numerous engineering drives and commutes that assessed specific effects
of the adaptive systems. As part of the iterative nature of this
evaluation, the type and depth of training administered by the
researcher varied across participants. Initially, drivers were
introduced to each of the four warning systems (Forward Collision
Warning (FCW), Lane Departure Warning (LDW), Distraction Alerts, and
Proximity Alerts) and were given specific details about how the systems
worked (e.g. time headway, thresholds, minimum speed activation), as
well as, the intent and behavior of adaptation (e.g. warning
suppression, implications of driver head pose). Drivers were encouraged
to share their reactions, comments and questions, with drives taking on
an informal think aloud approach.

In an effort to realize more naturalistic driving, later drives withheld
adaptation details from the training and simplified the warning system
explanations by eliminating discussion of the more technical aspects of
the system. Likewise, during subsequent drives, the researcher did not
provide specific answers to the participants' questions until the drive
was complete. It was thought that the streamlined training, coupled with
increased warning exposure during the demonstration, would provide
drivers with a more meaningful and practical representation of the
warning systems. The importance of driving naturally and, specifically
not inducing alerts were emphasized to each driver.

Reports from participants and researcher observations led to the
optimization of the adaptive interfaces.

The distraction alert system presented unique challenges. Drivers
consistently experienced a high frequency of alerts, but more
importantly, were often unable to associate these alerts with their
cause. Users reported receiving alerts when they did not perceive
themselves as being distracted or when performing regular driving
maneuvers, like checking a blind spot. As a result, high levels of
frustration and annoyance became associated with this system. After much
testing, the distraction alert was abandoned, due to the unresolved
nuisance alert rate and the fear that real-time feedback could add
distraction.

***On-Road Adaptive Warning Validation***

After establishing the final adaptive warning interfaces and algorithms
through the process of iterative testing and development, the data
fusion task conducted an on-road evaluation of the optimized SAVE-IT
configuration for Adaptive FCW and LDW. These on-road drives afforded 28
naïve participants a chance to contrast the non-adaptive mode with the
SAVE-IT adaptations, and provided a number of dependent measures, across
two studies. Objective results revealed reductions in overall
alert-frequency for the adaptive versions of both FCW and LDW. Drivers
received significantly fewer alerts, and fewer alerts per mile, in the
adaptive mode of both the FCW and LDW studies. The FCW and LDW warning
systems demonstrated high adaptive suppression rates, approximately 70%
and 95% respectively. Significantly more drivers expressed a preference
for adaptive mode in both studies, and when they did so, the most
popular reason provided was the lower alert-rate. It appears that the
difference in alert frequency between modes was the most salient aspect
of both systems, and it directly influenced several subjective ratings.
The trend across both warning systems was for drivers to report that the
adaptive warning system was more acceptable, had fewer nuisance alerts,
and was less distracting.

***Conclusions and Final SAVE-IT Implementations***

The various SAVE-IT tasks have generated algorithms and strategies for
distraction mitigation. Data fusion has yielded five adaptive interfaces
(adaptive FCW, adaptive LDW, trip report, adaptive infotainment
availability and advisory, and adaptive phone management) to be
implemented in the Task 14 evaluations (see Figure 11.1).

![](media/image1.png){width="6.496527777777778in"
height="3.104861111111111in"}

> ***Figure 11.1.** Summary of the SAVE-IT countermeasures as a function
> of sensing systems.*

1.  *[Adaptive Forward Collision Warning]{.underline}.* As a result of
    both Task 9 driving simulator research and the on-road acceptance
    testing of the data fusion task, the differential alert timing
    strategy appeared to be the most promising. In this particular
    implementation, the timing adjustment for the forward attention
    state was quite extreme (assumed reaction time of 0.5 s) in order to
    suppress almost all alerts when the driver's head pose was forward.
    When the driver's head pose is not forward, the driver will
    experience very early alerts that allow for a long driver brake
    reaction time.

2.  *[Adaptive Forward Collision Warning]{.underline}*. The final form
    of adaptation that was selected for LDW was the suppression
    strategy. Due to the fact that cognitive distraction and brief or
    small-angle visual distractions do not appear to significantly
    degrade lane-keeping performance (see Tasks 5 and 9), LDW is
    unlikely to provide a safety benefit for visually-attentive drivers.
    The prevalence of rapid head pose changes resulting from mirror
    checks just prior to a lane crossing required a distinction between
    head pose not forward for an extended period of time versus the
    instantaneous head pose state.

3.  *[Trip report]{.underline}.* In order to mitigate additional
    distraction by providing the driver with feedback in real time, a
    delayed feedback mechanism will also be implemented in the form of a
    trip report. This report will be presented to the driver at the end
    of the drive and includes information on the driver's head pose
    behavior during the drive and a summary of the FCW and LDW
    incidents. Although this countermeasure would be voluntary in a
    production implementation, it is expected that the trip report could
    be implemented in a manner that might trigger the driver's
    curiosity. Task 4 (Distraction Mitigation) demonstrated that
    post-drive feedback could improve the driver's behavior on
    subsequent drives.

4.  *[Adaptive Infotainment Availability and Advisory]{.underline}*. As
    driving task demand increases, drivers are advised to shed more IVIS
    function in order to concentrate on the driving task. An amber color
    was used on the buttons to signal that the feature was not advisable
    given the current driving task demand level. In addition to the
    color change of the button, the "Use Cautiously" text appeared just
    under the page heading to help communicate to the driver the
    intention of the adaptation. As the driving task demand gets to
    greater levels, some of the most demanding IVIS features are
    locked-out. Initially it was expected that the lock-out of features
    had the potential of being too annoying or too confusing, however
    the Task 4 results seemed to indicate that lock-out might be a
    potentially useful distraction-mitigation strategy if it could be
    implemented in a way that clearly communicates the intention to the
    driver. By using the gray-out effect that is now common in computer
    GUIs (graphical user interfaces), and tying it to the text just
    below the page description, it appears that driver-confusion may be
    avoided.

5.  *[Phone screening based on driver preference and driving task
    demand]{.underline}*. Task 4 demonstrated that driver-initiated
    mitigation methods are more acceptable to drivers than
    system-initiated methods. Task 11a showed that the perceived risk
    associated with phone functions increases with driving task demand.
    In order to optimize driver acceptance, drivers will be given three
    choices for phone screening: no screening, do-not-disturb, and
    automatic screening. If the driver selects automatic screening, the
    SAVE-IT system will determine whether a call should be routed to the
    driver or to voice mail based on the concurrent driving task demand.

# 11.1. 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.

# 11.2. INTRODUCTION

The purpose of Task 11 was to fuse the data from component tasks and
implement the SAVE-IT adaptive interfaces in instrumented vehicles in
order to further develop and refine the most promising set of
countermeasures for the Task 14 evaluations. This report describes the
data fusion efforts for both the distraction mitigation and the adaptive
warning countermeasures. For distraction mitigation, mitigation methods
and algorithms were extracted from the research findings in Tasks 2
(driving task demand), 3 (performance), 4 (distraction mitigation), 5
(cognitive distraction), 6 (telematics demand), 7 (visual distraction),
8 (intent or maneuver prediction), and 10 (technology development). For
the adaptive warnings, adaptation methods and algorithms were extracted
from the research findings in Tasks 1 (crash scenario analysis), 3
(performance), 5 (cognitive distraction), 7 (visual distraction), 8
(intent or maneuver prediction), 9 (safety warning countermeasures), and
10 (technology development).

As an input to this data fusion task, there was a huge body of work
conducted in the previous phases of SAVE-IT that would help guide the
design of the final algorithms and interfaces. On the sensing side,
tasks had focused on cognitive and visual distraction, maneuver
prediction, and driving task demand. The performance task (Task 3) had
demonstrated that it is difficult to assess the driver's state merely by
using performance measures (e.g., derivations of steering wheel,
throttle position, lane position). The distribution of these kinds of
measures varied widely across the types of situations that were
encountered and provided more information about the driving task demand
than the driver's state. The maneuver prediction task had also begun to
identify diagnostic measures that indicated the driver was about to
engage in a maneuver, however, this work could not be completed in time
to provide a useful input into the data fusion task. Furthermore, due to
the requirement of a Task 9C (Safety Warning Countermeasures) effort
that was not originally planned, the resources of Task 8 (Maneuver
Prediction) were migrated toward the more urgent needs of Task 9C. Like
the maneuver prediction task, the driving task demand task (Task 2) was
not on schedule, and although the effort provided groundwork for an
algorithm that was capable of detecting the real-time demand of the
driving task, this work was not far enough along to be incorporated in
the final SAVE-IT vehicle configuration. Thus a portion of the data
fusion task needed to devote resources to the task of developing an
algorithm to detect the driving task demand as a function of the
available sensor data (e.g., radar, yaw-rate, windshield wipers).

Task 5 (Cognitive distraction) developed some sophisticated algorithms
to support the detection of the driver's degree of cognitive distraction
and although the performance of the detection algorithm appeared was
impressive, the countermeasure tasks (Task 4: Distraction Mitigation and
Task 9: Safety Warning Countermeasures) did not reveal any reasonable
countermeasures to make use of the driver distraction data. The Safety
Warning Countermeasures task revealed that the strategies that used
cognitive distraction as a basis of adaptation performed less favorably
than non-adaptive systems. At this stage, there was no clear method for
responding to cognitive distraction in a manner that drivers would be
likely to accept. It is questionable whether drivers would be open to a
countermeasure that asks them to "think less" about non-driving related
material. Some Task 5 work appears to suggest that there may be some
important interactions between visual and cognitive distraction,
however, the requirements for supporting a system that can detect
cognitive distraction were beyond the current state-of-the-art for
automotive grade hardware, requiring relatively fine discriminations of
the driver's eye positions. Although the detection of cognitive
distraction is apparently possible with expensive research-grade
apparatus, it appears unlikely that a system that can reliably detect
cognitive distraction will become available in the automotive market in
the next decade. Therefore, although Task 5 (Cognitive distraction)
clearly provides an impressive body of work and a valuable contribution
to the science of cognitive distraction, the results of this task were
not incorporated into the final SAVE-IT vehicle configuration.

The component countermeasure tasks (Task 4: Distraction Mitigation and
Task 9: Safety Warning Countermeasures) had already laid much groundwork
for the development of adaptive interfaces. However, because this work
had been conducted in the relatively simplistic environments of the
driving simulators, prior to data fusion, little was known about how
these systems would interact with real roadways or how drivers would
accept them when presented as a more concrete reality. The Task 4 and 9
work had already rejected some unfavorable adaptive interface candidates
and had begun to refine the countermeasures as a function of safety
effectiveness. The major need for the data fusion task was to expose the
system to real roadways and further develop the countermeasures as a
function of on-road performance and the resulting driver feedback.

In the early stages of this data fusion task, as naïve drivers began to
experience the systems on real roadways, it became increasingly apparent
that a critical requirement was that the countermeasures needed to be
understandable. Countermeasures that were more elaborate or included
more hidden assumptions were not well tolerated by drivers. As the early
countermeasures began to be subjected to on-road testing with naïve
subjects, it became clear that the countermeasures needed to be simple
and coherent. As the need for simplicity and clarity increasingly became
the overriding principle for the SAVE-IT countermeasures during the
iterative development and testing cycles, the more complex
countermeasures tended to be abandoned in favor of the simpler or more
transparent countermeasures. Through iterative development and testing
cycles, it became increasingly clear that the most promising decision
making techniques were simple rules that a typical driver might be
capable of digesting. For example, a driver might not understand an
algorithm that takes into account both the driver's level of distraction
and the demand level of the driving task in determining whether a
collision warning should be issued. A more simple rule such as, if the
driver's head pose is directed away from the forward scene a collision
warning is issued earlier, is more likely to be digestible. Furthermore,
it became clear that for the adaptive warnings, only the most recent or
instantaneous head pose information was useful for determining whether a
warning should be issued. Prior to lane changes and other situations
that frequently lead to FCW and LDW nuisance alerts, drivers would often
check mirrors, thus increasing the proportion of time that the driver's
head pose is away from the forward scene. This consideration led to the
simple rule that if the driver's head pose is forward, the prior history
of head pose is ignored for the purposes of adaptive warnings.

Ultimately, the SAVE-IT system amounted to something relatively simple.
Figure 11.2 displays the inputs and outputs of the SAVE-IT
countermeasures, and how the countermeasures group into the two branches
of distraction mitigation and adaptive warnings. Whereas adaptive
warnings include adaptive FCW, adaptive LDW, the distraction mitigation
includes the trip report, the adaptive infotainment availability /
advisory, and the adaptive phone management. The SAVE-IT configuration
also included a proximity alert, however, this was not an adaptive
interface because, in order to preserve consistency, it did not adapt
with either head pose of driving task demand, and simply alerted the
driver with an amber exogenous display when time headway was beneath 0.5
s.

![](media/image1.png){width="6.496527777777778in"
height="3.104861111111111in"}

***Figure 11.2.** Summary of the SAVE-IT countermeasures as a function
of sensing systems.*

As Figure 11.2 reveals, the two major inputs into the countermeasures
are head pose and driving task demand, and these inputs operate
relatively independently, such that a system is either adapted by head
pose or by the demand of the environment, but not both. This
implementation ultimately resulted from the need to maintain a simple
system that the driver would be able to comprehend. Much iteration of
on-road testing and development cycles were used to select and refine
the relatively simple rules that govern these countermeasures.

This report is divided into the following sections:

***11.3 Distraction Mitigation Preparation*:** This section describes
the experiment conducted during the data fusion task that was used to
derive an algorithm that predicts driver ratings of driving task demand
based on vehicle sensor data. The experiment not only asked participants
to rate the driving task demand but also to suggest when certain
features should be available, providing useful data for guiding both the
adaptive infotainment availability / advisory and the adaptive phone
management countermeasures.

***11.4 Iterative On-Road Testing and Development*:** This section
describes the iterative on-road testing that was used to guide the
development of the countermeasures and to finally settle on the
configuration that was tested in the evaluation phase of SAVE-IT.

***11.5 On-Road Adaptive Warning Validation*:** This section describes
the on-road testing of the Adaptive FCW and LDW systems once they had
reached maturity. Several drivers were exposed to the systems on-road in
order to collect some subjective feedback and alert rate data. The
purpose of this testing was to provide some level of confidence that
these systems were ready for the evaluation testing that followed.

***11.6 Conclusions and SAVE-IT Implementation Summary*:** The
conclusion section will summarize the final SAVE-IT configuration and
highlight some of the conclusions reached during the data fusion task.

# 11.3. DISTRACTION MITIGATION PREPARATION

Task 2 (driving task demand) has provided a wealth of information
regarding the determination of driving task demand from road conditions
(e.g., road curvature), traffic, and weather (Cullinane & Green, 2004;
Eby & Kostyniuk, 2004b, 2004c). A considerable amount of information is
based on driving simulator research (Cullinane & Green, 2004). In Task
14, evaluations of SAVE-IT systems will be performed both in driving
simulators and on-road. In order to fulfill the need for Task 14
(especially the on-road portion of the evaluation), a substantial
portion of the data fusion task consisted of a new experiment that was
performed to assess driving task demand using vehicle sensor data. An
algorithm was developed to predict driving task demand in real time.

## 11.3.1. Method

### *Participants*

Ten subjects (five males and five females) were recruited from the
salaried employee pool at Delphi Electronics and Safety in Kokomo,
Indiana. They were required to be in the range of 35-55 years old and
possess a valid driver\'s license. Subjects had a minimum vision of
20/40 (vision correction with eyeglasses permitted) as tested with the
Snellen Eye Chart. The actual age for the ten subjects ranged between
35-53, averaged 43, and had a standard deviation of 5. They were paid a
\$50 Wal-Mart gift card for their participation in the 2-h experiment.

### *Apparatus* 

A 2000 Buick Le SABRE^®^ was equipped with a forward looking radar, a
forward looking camera, a yaw sensor, a steering wheel angle sensor, a
photo cell, and other sensors for determining the brake pedal
depression, turn signal activation, wiper status, and ambient
temperature. The forward-looking radar and a micro-controller produced
the range and range rate for up to 20 objects (targets) in the forward
scene.

An experimenter drove the instrumented vehicle in the Indianapolis and
Kokomo areas for approximately 7 hours. The roads driven included urban,
suburban, and rural surface streets and highways. The drives covered
both rush hours and off-peak times. Vehicle data including the vehicle
speed, target range and range rate were recorded in the text format for
subsequent processing. The forward-looking camera was routed to a video
recording device to record the forward scenes (in black-and-white).

After reviewing the 7-hour videos, the experimenter selected 103 8-s
videos that spanned a wide range of driving conditions, including
different levels of traffic (no traffic, low, medium, and high traffic
density), road types (e.g., urban, suburban, rural), number of lanes
(e.g., 2, 4, 6), and various driving maneuvers (e.g., lane change,
merge, high entry or exiting). Eight seconds were selected because it
was long enough for a reliable measurement of vehicle sensor data and
subjective ratings and short enough to capture changes in the driving
task demand.

### *Procedure*

After completing the consent form, subjects were asked to watch three
practice videos one at a time and rate the driving task demand. Subjects
were then given one hundred videos one at a time and asked to rate the
driving task demand on a 7-point scale. The driving task demand scale
was as follows,

**\"Driving Demand**: On a scale of 1-7 (1-low, 4-medium, 7-high), rate
how

attentive you need to be in order to detect unexpected events and
hazards,

control vehicle speed and lane position, and avoid crashes and road
departures.\"

For each video, subjects were asked to determine whether visual
distraction should be curtailed when driving under the condition that
was portrayed by the video by answering the following question.

**\"Visual Distraction**: In your judgment, should you look away from
the forward

road less often than nominal? (yes/no)\"

Subjects were also asked to answer the following questions to determine
if certain IVIS features should be advised against or disallowed.

\"**IVIS:** In your judgment, which of the following features should be
1 (allowed),

2 (advised not to be used), or 3 (disallowed) when driving under the
condition

that is portrayed by the video clip?

Tuning radio (with seek or preset buttons)

Play satellite radio (Select by genre or favorites)

Play CD (track selection, random selection)

MP3 (Search by artist, album, genre and select from playlist)

Manually dialing a 7-digit number

Answering a phone call

Talking on cell phone

Enter nearest points of interest on navigation system

Read maps on navigation system

Use turn-by-turn directions on navigation system

Read text messages (SMS or email).\"

## 11.3.2. Results

### *Demand ratings*

Because there were large individual differences among the demand ratings
provided by the ten subjects, the median rating was chosen to represent
the rated demand for a particular video. This ensured that the result
reflected the result of the majority and prevented a potentially
divergent response from a single subject overly influencing the result.
As shown in Figure 11.3, the median demand ratings ranged from 1.5 to 6.
They were classified into the following three groups:

A. 13 videos with a low demand level (with the median demand rating \<=
3),

> B. 63 videos with a medium demand level (with the median demand rating
> is \>= 3.5 and \<= 5),

C. 24 videos with a high demand level (with the median demand rating \>=
5.5).

![](media/image2.wmf){width="5.284722222222222in"
height="2.9569444444444444in"}

***Figure 11.3.** Distribution of median demand ratings.*

### *Predicting rated demand from vehicle sensor data* 

The vehicle sensor data were analyzed for the middle 5-s portion of the
8-s video to predict the median demand ratings. The rated demand was
highly correlated with several vehicle sensor variables, including the
mean range value that was calculated with the 5-s time window (r=-0.60).
It had a moderate correlation with variables including mean time headway
(r=-0.34), standard deviation of time headway (r=0.33), and speed
variability for the host vehicle (r=0.31) that were calculated with the
5-s time window. It had a low correlation with variables including speed
variability among targets (r=-0.11) and speed variability for lead
vehicles (r=0.15) that were calculated with the 5-s time window.

The vehicle sensor variables that were correlated with the rated demand
were fused to derive an equation that can predict driving task demand.
The equation that produced the best prediction was as follows,

![](media/image3.wmf){width="5.991666666666666in"
height="0.5722222222222222in"}

Where k was a constant, which was 1 for moving targets that moved in the
same direction as the host vehicle, 5 for moving targets that moved in
the opposite direction (e.g., in oncoming lanes), and 0 for stationary
targets. The normalized range was defined as follows,

$$\text{Normalizedrange} = \frac{\text{Range}}{\text{25}}$$

Time headway was defined as

$$\text{TimeHeadway} = \frac{\text{Range}}{\text{HostSpeed}}$$

The normalized angle was defined as

$$\text{Normalizedangle} = 1 + \frac{\text{abs}(\text{Angle} - \text{AngleCIPV})}{4}$$

Where angle is the angle for targets and angleCIPV is the angle for the
closest in-path moving vehicle.

The minimum value between *normalized range* and time headway
represented the range variable that was most demanding to subjects. In
general, driving task demand increased with a reduction in either
*normalized range* or time headway. The *normalized angle* variable
indicated that the driving task demand was lower for targets that were
laterally farther away from the longitudinal path of the host vehicle.
The summation across tracks/targets indicated that the driving task
demand increased with an increasing number of targets.

The weight equation above was adjusted based on the following factors.

-   Yaw rate which indicates turning and driving on curves.

-   GPS info (e.g., merge, highway entry, exit, interchange)

-   Lane width

-   Speed

-   Brake pedal depression

The adjusted (predicted) weights were classified into three demand
groups as follow,

A. Low demand (if the adjusted weight \< 0.5),

B. Medium demand (if the adjusted weight \>=0.5 and \<2.18),

C. High demand (if the adjusted weight \>= 2.18).

Figure 11.4 presents the median demand ratings against the adjusted
(predicted) weights. The dark diagonal line represented the best-fitting
regression line (Y=3.03+0.82X). It was clear from Figure 11.4 that most
of the data points were near the regression line. The Pearson
correlation coefficient between the adjusted (predicted) weights and
median demand ratings was 0.79. This high correlation indicated that the
demand algorithm was able to predict the rated demand very well.

![](media/image4.wmf){width="6.222916666666666in"
height="3.1055555555555556in"}

***Figure 11.4.** A scatter plot of median demand ratings against the
adjusted (predicted) demand weight.*

The demand groups for the predicted demand (from the predicted weight)
and median demand ratings were tabulated in Table 11.1. When there was a
match between the predicted and rated demand groups (e.g., both were
high demand), the cell was colored green. When the predicted and rated
demand groups mismatched by one group (e.g., high and medium demands),
the cell was colored yellow. When the mismatch was two groups (e.g., low
vs. high demand), the cell was colored red. It was clear from Table 11.1
that all red cells were 0, indicating no gross mismatches. There were
some yellow non-zero cells, indicating some small mismatches. The
largest cell values were green, indicating a reasonable match between
the predicted and rated demands. The green cells represented 75% of the
videos.

***Table 11.1.*** Tabulation of Predicted and Rated Demand Groups.

![](media/image5.wmf){width="6.002083333333333in"
height="1.8958333333333333in"}

> *Note.* The values in the parentheses represent the borderline videos
> that could have been re-classified.

A close examination of Figure 11.4 and Table 11.1 indicated that most of
the mismatches were borderline cases. As shown in Figure 11.4, out of
the 15 videos that were predicted to have a high demand level but rated
as a medium demand level, 11 videos had a rated demand of 5, which was
very close to a high demand rating (if rating \>=5.5). Out of the 7
videos that were predicted to have a low demand level but rated as a
medium demand level, 6 videos had a rated demand of 3.5, which was very
close to a low demand rating (if rating \<=3). Furthermore, the
experimenter reviewed these videos and indicated that they could have
been re-classified to match the predicted demand. If these videos were
re-classified (as shown in Table 11.1), the percentage of matches
between the predicted and rated demands was increased to 93%.

### *Visual distraction feedback*

Figure 11.5 presents the number of \"yes\" responses to curtail off-road
glances or the amount of visual distraction based on the driving task
demand. The correlation coefficient between the number of \"yes\"
responses and driving task demand rating was 0.76. As shown in Figure
11.5, drivers believed that as the driving task demand increased, more
visual distraction should be curtailed. The regression line was:

Y= -2.9+1.48X, where Y was the number of \"yes\" responses to curtail
the amount of visual distraction and X was the rated demand. In other
words, the allowable visual distraction should be reduced as the driving
task demand increased. These results supported the SAVE-IT approach and
distraction mitigation concepts.

![](media/image6.wmf){width="6.393055555555556in"
height="3.501388888888889in"}

***Figure 11.5.** The number of responses to curtail off-road glances
(visual distraction) as a function of driving task demand.*

### *Demand-based advisories against using certain IVIS tasks*

Figure 11.6 presents the median ratings for allowable radio tuning as a
function of driving task demand. As the driving task demand increased,
the median rating for advisories against the use of radio tuning also
increased. The correlation coefficient was 0.65. The regression line was
Y = 0.04+0.29X, where Y was the median rating for advisories against the
use of radio tuning and X was the rated demand. Because the median
rating was below 2 even for the highest level of demand, the distraction
mitigation strategy was to allow radio tuning at all demand levels.

![](media/image7.wmf){width="6.4375in" height="3.377083333333333in"}

***Figure 11.6.** Median ratings of allowable radio tuning as a function
of driving task demand.*

Figure 11.7 presents the median ratings for allowable use of satellite
radio as a function of driving task demand. As the rated demand
increased, the median rating for advisories against the use of satellite
radio also increased. The Pearson correlation coefficient was 0.72. The
regression line was Y=0.07+0.35X, where Y was the median rating for
advisories against the use of satellite radio and X was the rated
demand. As shown in Figure 11.7, the median rating for advisories
against the use of satellite radio approached 2 (advised not to be used)
at the highest demand level. The distraction mitigation strategy was
therefore to advise against the use of satellite radio when the demand
was high.

Figure 11.8 presents the median rating for advisories against the use of
CD functions as a function of driving task demand. The correlation
coefficient was 0.65 and the regression line was Y=0.04+0.3X, where Y
was the median rating against the use of CD functions and X was the
rated demand. As the rated demand increased, the median rating for
advisories against the use of CD functions also increased. It approached
a median rating of 2 (advised not to be used) when the rated demand was
high. The distraction mitigation strategy was therefore to provide
advisories against using CD functions when the driving task demand was
high and no advisories when the driving task demand was low or medium.

![](media/image8.wmf){width="6.445138888888889in"
height="3.4694444444444446in"}

***Figure 11.7.** Median ratings of allowable satellite radio use as a
function of driving task demand.*

![](media/image9.wmf){width="6.513194444444444in"
height="3.459722222222222in"}

***Figure 11.8.** Median ratings of allowable CD use as a function of
driving task demand.*

Figure 11.9 presents the median rating for advisories against the use of
MP3 functions as a function of driving task demand. The correlation
coefficient was 0.75 and the regression line was Y=0.15+0.39X, where Y
was the median rating for advisories against the use of MP3 functions
and X was the driving task demand. As shown in Figure 11.9, the median
rating for advisories against the use of MP3 functions increased sharply
with the driving task demand. It approached a median rating of 2
(advised not to be used) when the driving task demand was medium and
exceeded a median rating of 2 when the driving demand was high.
Therefore, the distraction mitigation strategy was to provide advisories
against the use of MP3 when the driving task demand was medium or high,
and no advisories when the driving task demand was low.

![](media/image10.wmf){width="6.463888888888889in" height="3.41875in"}

***Figure 11.9.** Median ratings of allowable MP3 use as a function of
driving task demand.*

Figure 11.10 presents the median rating for advisories against manual
phone dialing as a function of driving task demand. The correlation
coefficient between the median rating for phone dialing advisories and
the rated demand was 0.72. The regression line was Y=0.85+0.34X, where Y
was the median rating for advisories against manual phone dialing and X
was the driving task demand. As the driving task demand increased, the
median rating for phone dialing advisories also increased. The median
rating for phone dialing advisories was approximately 2 when the driving
task demand was medium and approached 3 when the driving task demand was
high. Therefore, the distraction mitigation strategy was to provide
advisories against manually dialing 7- or 10-digit phone numbers when
the driving task demand was medium, advisories against any type of phone
dialing (including speed dial) when the driving task demand was high,
and no advisories when the driving task demand was low.

Figure 11.11 presents the median rating for advisories against answering
a phone call as a function of driving task demand. As the driving task
demand increased, the median rating for call answering advisories also
increased. The Pearson correlation coefficient was 0.79, and the
regression line was Y=-0.1+0.39X, where Y was the median rating for
advisories against answering phone calls and X was the driving task
demand. When the driving task demand was high, the median rating for
call answering advisories exceeded 2 (advised not to be used). The
distraction mitigation strategy was then to provide advisories against
answering phone calls when the driving task demand was high and no
advisories when the driving task demand was low or medium.

![](media/image11.wmf){width="5.995138888888889in"
height="3.2958333333333334in"}

***Figure 11.10.** Median ratings of allowable phone dialing as a
function of driving task demand.*

![](media/image12.wmf){width="6.8590277777777775in"
height="3.5861111111111112in"}

***Figure 11.11.** Median ratings of allowable phone call answering as a
function of driving task demand.*

Figure 11.12 presents the median rating for advisories against telephone
conversation as a function of driving task demand. As the driving task
demand increased, the median rating for phone conversation advisories
also increased. The correlation coefficient between them was 0.72, and
the regression line was Y=0.16+0.27X, where Y was the median rating for
advisories against telephone conversation and X was the driving task
demand. When the driving task demand was high, the median rating for
telephone conversation advisories approached 2 (advised not be used).
The distraction mitigation strategy was therefore to provide advisories
against having a phone conversation when the driving task demand was
high and no advisories when the driving task demand was low or medium.

![](media/image13.wmf){width="6.4125in" height="3.345138888888889in"}

***Figure 11.12.** Median ratings of allowable phone conversation as a
function of driving task demand.*

Figure 11.13 presents the median rating for advisories against the use
of the point-of-interest function on a navigation system as a function
of driving task demand. The Pearson correlation coefficient between them
was 0.70, and the regression line was Y=0.94+0.34X, where Y was the
median rating for advisories against the use of navigation
point-of-interest function and X was the driving task demand. The median
rating for point-of-interest advisories exceeded 2 when the driving task
demand was medium and approached 3 when the driving task demand was
high. The distraction mitigation strategy was therefore to provide
advisories against the use of navigation point-of-interest functions
when the driving task demand was high or medium and no advisories when
the driving task demand was low.

Figure 11.14 presents the median rating for advisories against map
reading on a navigation system as a function of driving task demand. The
Pearson correlation coefficient between them was 0.79 and the regression
line was Y=0.38+0.41X, where Y was the median rating for map reading
advisories and X was the driving task demand. The median rating for map
reading advisories was approximately 2 when the driving task demand was
medium and 3 when the driving task demand was high. The distraction
mitigation strategy was therefore to provide advisories against reading
maps on a navigation system when the driving task demand was high or
medium and no advisories when the driving task demand was low.

![](media/image14.wmf){width="6.440972222222222in"
height="3.435416666666667in"}

***Figure 11.13.** Median ratings of allowable use of navigation
point-of-interest feature as a function of driving task demand.*

![](media/image15.wmf){width="6.692361111111111in"
height="3.435416666666667in"}

***Figure 11.14.** Median ratings of allowable map reading on the
navigation system as a function of driving task demand.*

Figure 11.15 presents the median rating for advisories against using the
turn-by-turn function on a navigation system as a function of driving
task demand. The correlation coefficient between them was 0.61, and the
regression line was Y=0.74+0.28X, where Y was the median rating for
advisories against using the turn-by-turn function and X was the driving
task demand. The median rating for advisories against using the
turn-by-turn function was approximately 2 when the driving task demand
was high or medium. Therefore the distraction mitigation strategy was to
provide advisories against using the turn-by-turn function when the
driving task demand was high or medium and no advisories when the
driving task demand was low.

![](media/image16.wmf){width="6.270833333333333in"
height="3.3666666666666667in"}

***Figure 11.15.** Median ratings of allowable use of navigation
turn-by-turn feature as a function of driving task demand.*

Figure 11.16 presents the median rating for advisories against reading
text messages as a function of driving task demand. The Pearson
correlation coefficient between them was 0.71, and the regression line
was Y=1.78+0.23X, where Y was the median rating for text message
advisories and X was the driving task demand. As shown in Figure 11.16,
the median rating for text message advisories exceeded 2 when the
driving task demand was high, medium, or low. Therefore, the distraction
mitigation strategy was to provide advisories against reading text
messages when the driving task demand was high, medium, or low.

![](media/image17.wmf){width="6.625in" height="3.4590277777777776in"}

***Figure 11.16.** Median ratings of allowable use of text messaging as
a function of driving task demand.*

## 11.3.3. Discussion

The demand experiments provided information not only about how to detect
driving task demand as a function of the sensor data but also about how
the countermeasures that use demand should be implemented. At the time
of this research, the set of distraction mitigation countermeasures
included the following:

1.  **Adaptive Infotainment Advisory**: This countermeasure displayed
    the driving task demand level on the center console screen at all
    times (see Figure 11.17) and changed the color of the buttons to
    yellow in order to indicate to the driver that using the feature was
    not advisable given the current driving task demand level.

![](media/image18.wmf){width="2.5076388888888888in"
height="2.0409722222222224in"}

***Figure 11.17.** The preliminary method for indicating adaptive
infotainment advisories.*

2.  **Adaptive Phone Management**: The driver selects between the three
    different modes of no screening (allows all calls through),
    do-not-disturb (screens all calls to voicemail), and automatic
    screening (allows calls through when driving task demand is low and
    otherwise screens the calls to voicemail). Only the automatic
    screening utilized the driving task demand information to determine
    whether calls were allowed or screened.

3.  **Real-time Distraction Feedback**: Prior to the iterative on-road
    testing, the SAVE-IT program had planned to provide real-time
    distraction feedback in the form of an amber bezel around the
    infotainment screen (see Figure 11.18). During the iterative on-road
    testing (described in the next section), this interface was replaced
    with an amber exogenous display for a distraction alert, due to the
    potential for feedback on the infotainment screen to maintain the
    driver's attention on the center console area. Task 4 (Distraction
    Mitigation) had demonstrated that driver's reaction times could
    actually increase as a result of this countermeasure. Ultimately the
    iterative on-road testing led to the total abandoning of real-time
    distraction feedback, including the amber exogenous display, due to
    the high rate of nuisance alerts and fears that this countermeasure
    would be overly annoying to the drivers.

![](media/image19.wmf){width="5.6506944444444445in"
height="1.9958333333333333in"}

![](media/image20.wmf){width="3.6458333333333335in"
height="2.040277777777778in"}

***Figure 11.18.**.A flow chart for real-time distraction feedback and
the amber bezel used to advise drivers to reduce off-road glances*

4.  **Trip Report**: Rather than displaying distraction feedback in
    real-time and risking adding additional distraction during the
    driving task, the strategy of providing feedback at the end of the
    drive (Trip Report) was used. Post-drive feedback was investigated
    in Task 4 and appeared to be a promising alternative to providing
    real-time distraction feedback. Due to its late emergence, this
    strategy was not evaluated in the iterative on-road testing.
    Although the trip report could have presented driving task demand
    information to the driver, the goal was to keep the presentation
    relatively simple and present only the information that would most
    likely change the driver's future driving behavior.

The next section describes the process of how the iterative on-road
testing was used to refine the final set of SAVE-IT countermeasures that
were implemented in the evaluation phase.

#  

# 11.4. ITERATIVE ON-ROAD TESTING AND DEVELOPMENT

Before the final on-road evaluation could commence, an iterative
development process was used to evaluate drivers' perceptions and
acceptance of the adaptation strategies in the context of on-road
driving, and refine the adaptations accordingly. The primary purpose of
this initial portion data fusion task was to use insights gained through
on-road evaluations to facilitate the refinement and final selection of
the adaptation strategies and HMI. As an extension of previous,
simulator-based experiments, on-road drives were completed so that a
range of subjective measures, in addition to metrics representing the
effectiveness of adaptation, could be examined. These drives were
conducted as an iterative process that allowed for immediate cycles of
implementation, testing and refinement based on both subjective and
objective measures that were indicative of system performance. This
iterative refinement process afforded a data-driven methodology to
support this complex, multi-faceted data-fusion task. As the performance
of the component systems and their resultant interactions with the
overall system became apparent, system functionality could be modified
to provide more effective and acceptable SAVE-IT adaptations. Whereas
the component tasks (Tasks 4 and Task 9) had focused more on the
safety-benefit of the respective systems, these on-road evaluations were
used to assess the feasibility of the alternatives in terms of how
easily understood the countermeasures were and driver acceptance.

## 11.4.1. Method

### *Participants*

Twelve employees, 10 men and 2 women, at Delphi Electronics and Safety
Systems in Kokomo, Indiana, participated in the on-road testing.
Employees were randomly informed about the study by email and were
selected for participation based on the information provided in a
screening questionnaire. In order to qualify, participants were required
to have a valid driver's license, be able to drive without eyeglasses,
not take driving-impairing drugs prior to departure, agree not to wear
sunglasses during the testing, not work directly with the products being
tested, and women could not be pregnant.

All participants were between 30 and 48 years of age, with a mean age of
38, had between 14 and 33 years of driving experience, and drove between
6,000 and 25,000 miles per year. All but one of the participants were
engineers, with the exception being a background in a computer-related
technical field. Per information provided on the screening
questionnaire, five participants reported a familiarity with FCW
systems, and four with LDW systems. None of the participants
acknowledged having had exposure to DSM (Driver State Monitor) prior the
drive. Accident and driving history were not considered as part of the
selection process. Participants were paid for their participation with a
\$75 Best Buy gift certificate.

### *Apparatus*

The test vehicle used during these drives was a 2000 Buick Le SABRE^®^,
equipped with safety warning, driver monitoring and data collection
systems. The vehicle included FCW, LDW, proximity alert, distraction
alert, and driving task demand systems, in addition to a DSM unit that
was secured near the instrument cluster. Data and video feeds were
collected using several methods, including camcorders and a custom
data-acquisition software tool on laptops, which were always displayed
out-of-view of the driver.

### *Design*

All of these researcher-accompanied drives occurred on public roads.
Drives took place on weekdays, departed Delphi Electronics and Safety
Systems between 8:00am and 1:00pm, and typically lasted about 3 hours.
All but two drives were completed in dry conditions. Drivers were not
asked to engage in a secondary task, but were asked to glance at the
IVIS screen when they were comfortable doing so to observe changes in
the driving task demand, which was being displayed in a manner similar
to that shown in Figures 11.17 and 11.18. The iterative nature and the
immediate implementation of feedback during this portion of testing
demanded that a number of variables not be held constant and, instead,
allow for variation and refinement across participants. Variations in
the warning systems, driver training, and route had the greatest impact
on drivers' experiences and afforded the most effective refinement
process. Because of the iterative nature of the testing no formal set of
dependent variables were selected a priori, and unstructured
observations were the basis of the decision making process.

**Warning Systems**

> The testing began with five drives that included simultaneous
> activation of FCW, LDW, proximity alert, and driving task demand
> systems. The final seven drives included the addition of the
> distraction alert, active only in adaptive mode. Later drives included
> improvements and updates to the DSM system and to the presentation of
> driving task demand indicators. While the majority of drives focused
> on a comparison between non-adaptive and adaptive modes, two drives
> limited exposure to only the adaptive mode. By using a three-part
> format, the final three drives gave participants a chance to explore
> the warning systems before the researcher presented training
> information. Feedback from participants about the frequency of
> warnings, their interpretation of the alerts, and their perception of
> nuisance alerts, guided the iterative refinement process. However, it
> was particularly problematic for drivers to evaluate multiple warning
> systems in areas with heavier traffic, due to the increased alert-rate
> and the limited interval between alerts for discussion or
> clarification with the researcher. Drivers also reported only a basic
> understanding of the warning systems when training was not provided
> prior to the drive.

**Training**

> As part of the iterative nature of this evaluation, the type and depth
> of training administered by the researcher varied across participants.
> Initially, drivers were introduced to each of the four warning
> systems, FCW, LDW, proximity alert and driving task demand, provided a
> demonstration, and given specific details about how each system worked
> (e.g., time headways, distraction thresholds, imminent collision
> criteria). The intent and behavior of the adaptation strategies were
> also described to participants (e.g., auditory, timing, and
> suppression strategies, as well as, the implications of driver head
> pose). Drivers were encouraged to share their reactions, comments and
> questions, with drives taking on an informal "think aloud" approach.
> During testing, detailed answers were provided when a driver made an
> inquiry about the camera, radar, or one of the warning systems.
>
> In an effort to realize more naturalistic driving, later drives
> simplified the training by eliminating discussion of the details of
> the adaptation (e.g., specific adaptation strategies), in addition to
> the more technical aspects of the warning systems (e.g., time
> headways, distraction thresholds, and imminent collision criteria).
> Most drivers found the inclusion of so much detail overwhelming and
> much too challenging to grasp during a brief exposure. It was thought
> that the streamlined training, coupled with increased warning exposure
> during the demonstration, would provide drivers with a more meaningful
> and practical representation of the warning systems. In addition, the
> redesigned training also afforded drivers a more natural process of
> discovery and they were encouraged to continue to share their
> impressions. The importance of driving as they normally would and
> specifically, not inducing alerts, was strongly emphasized to each
> driver during training.

**Route**

> Two different routes encompassed a variety of driving environments.
> The first route included, highway, city, suburban retail district, and
> wooded/hilly, and a second route provided exposure to interstate
> driving. The length of drives varied between 100 mi and 120 mi. While
> most of the drivers were generally familiar with a majority of each
> route, some participants did not frequently travel on these roads and
> some drivers were new to the area. These diverse routes revealed that
> road and traffic conditions can impact warning system behavior. The
> wooded/hilly portion proved especially important for understanding the
> high-rate of out-of-path, stationary alerts. Drivers' curiosity while
> traveling through an unfamiliar area resulted in a tendency to look
> away from the road and also impacted system behavior and alert-rate.

## 11.4.2. Results

The twelve naïve-subject drives described here were also supplemented
with numerous engineering drives and commutes that assessed specific
effects of the adaptive systems. A compilation of data from those
engineering drives, reports from participants, and researcher
observations drove the iterative optimization of the FCW, LDW,
distraction alert, and proximity alert systems, their respective
adaptive enhancements. It became apparent that warning system behavior
can be quite variable for different types of driving styles and
individual differences became an important consideration as
modifications were considered. A summary of key modifications is shown
in Table 11.2.

***Table 11.2.** Summary of Key Observations and their Resultant System
Modifications.*

  ------------- ----------------------------- --------------------------------
  **System**    **On-Road Evaluation**        **Modification**

  Exogenous     Amber exogenous display       Diameter and brightness of the
  Display       appeared too large and too    amber exogenous display were
                bright.                       reduced.

  DSM           Head pose 'forward' angle too 'Forward' angle was increased to
                narrow, causing frequent      20 deg. This is consistent with
                nuisance alerts when the      some of the 100-Car study
                driver was attending to the   observations that glances
                road but had a slightly       outside of the 20 deg region are
                angled head posture.          particularly problematic.

  FCW           Auditory car horn alert was   Auditory car horn alert changed
                too startling.                to tonal alert.

  FCW           Volume of auditory alerts too Volume of alerts reduced.
                startling.                    

  FCW           Adaptive FCW auditory         Auditory FCW suppression removed
                suppression strategy          in favor of differential alert
                perceived as inconsistent.    timing approach.

  FCW           Alerts for out-of-path,       Alerts for stationary objects
                stationary objects too        (CIPS) were suppressed.
                frequent.                     

  LDW           Warnings perceived as         Minimum speed criterion for
                nuisance alerts at lower      alerts increased. Permissible
                speeds.                       lane width increased.

  Proximity     Behavior of alerts caused     Complex dimming and flashing
  Alert         confusion.                    algorithm replaced by solid
                                              exogenous display at a fixed
                                              0.5-s threshold.

  Distraction   High frequency of nuisance    Experimented with Different
  Alert         alerts.                       thresholds but ultimately
                                              determined that the problems
                                              could not be resolved and
                                              abandoned this countermeasure.
  ------------- ----------------------------- --------------------------------

Several of the first on-road evaluations provided strong feedback about
the auditory portion of the FCW system. Drivers reported the auditory
car horn warning to be startling and, depending on the context of the
driving situation, not always indicative of a forward event (e.g., when
making a lane change). The auditory warning was replaced with a series
of four, quick, 400-Hz tones. Although the car horn had shown promise in
the driving simulator for speeding up driver responses, there was
insufficient time to examine the risk of driver confusion that might be
caused by using this sound. However, when the first warning exposures
continued to produce a startle effect, volume of the warning was
adjusted to provide a more ideal balance between capturing the attention
of and startling the driver. It quickly became clear that the number of
FCW events triggered by out-of-path, or off-road, stationary objects was
far too frequent. Early questionnaire results also reflected similar
concerns. Due to the time constraints on developing the FCW algorithm,
it was decided that the alerts for stationary targets would be
suppressed, reflecting the functionality of many production
implementations. The likelihood of recommending or buying the FCW system
was most negatively impacted by the frequency, volume, and pitch of
warnings.

The initial LDW system also presented concerns for several of the
participants. Drivers demonstrated a tendency to categorize warnings as
nuisance alerts when they occurred at lower speeds and expressed that
the warnings occurred too early, well before the threat of a lane
departure. A high alert-rate was cited as the reason for not
recommending or purchasing the LDW system. In order to reduce the number
of perceived nuisance alerts, the minimum activation speed was raised
and lane width was increased, effectively delaying the activation of
warnings. In addition to these enhancements, an LDW auditory warning was
also implemented, as it is considered to be more representative of
production systems. The auditory warning, similar to a rumble strip
sound, was issued in synchrony with the exogenous display and haptic
seat vibration.

Several changes to the proximity alerts were implemented during the
on-road evaluation. The original system included dimming and flashing of
the exogenous display for various stages of adaptation and time headway,
but drivers expressed uncertainty and annoyance about what each display
represented. Several improvements to the presentation of the alerts were
incorporated in an effort to communicate a clearer, less-distracting
message to drivers. The flashing and dimming were removed to produce a
solid amber exogenous display. Questionnaire responses revealed that
drivers thought the timing of the alert was too early and that a closer
following distance, such as is maintained in heavy traffic, should be
allowed. The characteristics of the exogenous display became especially
salient during proximity alerts, when the display was active for an
extended period. Several drivers commented that the exogenous display
was too large and too bright. While this effect was reduced as nuisance
alerts across warnings were reduced, the exogenous display was also
modified to address drivers' concerns. The brightness and diameter of
the amber proximity alert display were both slightly reduced.

The Distraction Alert system presented unique challenges. Drivers
consistently experienced a high alert-rate, but more importantly, were
often unable to associate these alerts with their cause. Users reported
receiving alerts when they did not perceive themselves to be distracted
or when performing regular driving maneuvers, like checking a blind
spot. As a result, high levels of frustration and annoyance became
associated with this system. The first modification made was to widen
the head pose angle that DSM defines as 'forward' to 20 deg. The 20 deg
window was based on the 100-Car Study observation that the risk
increased significantly for glances outside of 20 deg (Klauer, Dingus,
Neale, Sudweeks, & Ramsey 2006). Secondly, the temporal distraction
criterion was lengthened to reduce the number of alerts. The alert rate
was drastically reduced when the glance away threshold for the
distraction rate was increased. Variable thresholds were implemented,
ranging from 2 to 4 s, depending on the time headway to the lead
vehicle. Although alerts were relatively rare, the proportion of alerts
that were false was still too high. Because the time thresholds were so
high, the most common cause for alerts were from the failure of DSM to
detect momentary glances back to the forward scene. The DSM would detect
glances away, however, after missing the rapid glances back that often
only involved the eyes returning and not the head, the glance duration
was summed over multiple glances away, reaching the threshold over
several glances away. Often the distraction alerts were issued at the
same moment that the driver returned their attention to the forward
roadway. Furthermore, the impact on safety of the distraction alerts was
not clear. Task 4 (Distraction Mitigation) had demonstrated that
distraction alerts may actually increase driver reaction times, perhaps
because the distraction alerts themselves actually added a source of
distraction. Due to the unresolved nuisance alert issues and the unclear
safety implications the real-time distraction alerts were abandoned in
favor of the post-drive distraction feedback. This countermeasure (trip
report) appeared to late to be tested in the data fusion task, however,
it had been investigated in the late stages of Task 4 (Distraction
Mitigation) and so Delphi and the University of Iowa were able to work
quickly to install the trip report countermeasure for the Task 14 NADS
evaluation.

**Conclusions**

After numerous iterations of development and testing, the performance of
the SAVE-IT systems began to converge on a final implementation that
appeared to best balance the constraints of acceptance and safety. As
the rate of iterative improvements began to decline, the system began to
solidify and was frozen in place. Table 11.3 displays the final system
configuration for Adaptive FCW that was tested in Task 14 (Evaluation).
After much research in Task 9 and some on-road acceptance testing in
Task 11, the Differential Alert Timing strategy appeared to be the most
promising. In this particular implementation, the timing adjustment for
the forward attention state was quite extreme (assumed reaction time of
0.5 s) in order to suppress almost all alerts when the driver's head
pose was forward. This adaptation method was selected because it at
least provides some coverage for driver's who are visually oriented to
the forward scene but might benefit from an alert for some other reason
(e.g., drowsiness or cognitive distraction). When the driver's head pose
is not forward, the driver will experience very early alerts that allow
for a long driver brake reaction time.

![](media/image21.wmf){width="6.0in"
height="2.703472222222222in"}***Table 11.3.** Adaptive and Non-Adaptive
mode Forward Collision Warning as a Function of Driver Head Pose.*

A distinction was made between head pose not forward and an extended
period during which head pose was not forward. This was an important
distinction that allowed drivers to glance away from the forward scene
for brief moments (such as mirror checks) yet still experience the
potential nuisance-alert suppression of the SAVE-IT system. The
distinction between glances away for greater than 2 s compared with
shorter driving-related glances away from the forward roadway (such as
mirror checks) is supported by the results of the 100-Car Study (Klauer
et al., 2006).

The final form of adaptation that was selected for LDW was the
suppression strategy (see Table 11.4). This strategy was selected
because several drivers experienced confusion with the differential
alert stimuli strategy (suppression of the auditory warning stimulus;
see Task 9) because the interface lacked consistency. Due to the fact
that cognitive distraction and brief or small-angle visual distractions
do not appear to significantly degrade lane-keeping performance (see
Tasks 5 and 9), LDW provides little safety benefit for
visually-attentive drivers[^1]. The distinction between head pose not
forward for an extended period of time versus the instantaneous state
was especially necessary for LDW, where lane changes were often preceded
by mirror checks just before the lane-crossing. The CAMP workload
program revealed that the number of and duration of glances to the
secondary task were the best predictors of lane crossings (Angell,
Auflick, Austria, Kochhar, Tijerina, Biever, Diptiman, Hogsett, & Kiger,
2006). It is expected that the SAVE-IT implementation of adaptive LDW
will not compromise the safety benefit of LDW because only with
prolonged distraction episodes is it likely that drivers will lose
lateral control of the vehicle without being able to make the
appropriate corrective response. For an alert to be generated in
adaptive mode, the driver's head pose needed to be away for a minimum of
2 s, and at least 1 s of the head-pose away needed to occur prior to the
lane crossing. This was because if drivers looked away just as they were
about to cross the lane, it could be assumed that they were aware of the
lane crossing.

![](media/image22.wmf){width="4.291666666666667in"
height="1.95in"}***Table 11.4.** Adaptive and Non-Adaptive mode Lane
Departure Warning as a Function of Driver Head Pose.*

For similar reasons, the suppression strategy was also tested for the
FCW system, however, it was subsequently abandoned in favor of a timing
strategy. The timing strategy that was finally implemented is similar to
a suppression strategy in that it is likely to suppress virtually all
alerts while the driver's head pose is 'forward' because the system will
wait until the last possible moment before issuing the alert. When the
driver's head pose is 'not forward', the driver will experience very
early alerts that allow for a long driver brake reaction time.

These final implementations of adaptive FCW and LDW were evaluated
separately in on-road studies that are described in the next section.
Due to the relatively low baseline rate of FCW alerts generated by
typical algorithms, the FCW algorithm was sensitized in both the
baseline and adaptive conditions. This was necessary in order to provide
drivers with some exposure in the relatively short durations of both the
data fusion and Task 14 UMTRI on-road evaluations. Without
sensitization, it would be likely that many drivers would not experience
a single alert in either adaptive or non-adaptive mode. In the
non-adaptive mode, the algorithm assumed a brake reaction time of 2.5 s
to the alert (see Figure 11.19). In the adaptive mode, the algorithm
would assume a brake reaction time of 0.5 s when the driver's head pose
is forward, and 3.5 s when the driver's head pose is away for an
extended period of time (e.g., 2 s). When the driver's head pose was
first directed away from the roadway, the brake reaction time would
begin at 1.5 s and then linearly increase towards 3.5 s, which would be
reached at a 2-s extended away pose. The iterative testing and
development suggested that, although the non-adaptive and adaptive alert
rates were elevated, they did not appear to be excessive. Alerts still
seemed to have similar causes to those of a more typically tuned
algorithm.

![](media/image23.wmf){width="5.875in" height="3.55in"}

# 

# 

# 

# 

# 

# 

# 

***Figure 11.19.** FCW assumed brake reaction times (BRT) for adaptive
and non-adaptive systems.*

# 11.5. ON-ROAD ADAPTIVE WARNING VALIDATION

Following the conclusion of the iterative testing and development, Task
11 provided an opportunity to examine drivers' perceptions and
acceptance of the newly optimized adaptive warnings. Particular emphasis
was placed on obtaining a comparison between the non-adaptive and
adaptive modes, in a naturalistic driving context by measuring the
frequency of warnings in addition to the drivers' subjective
experiences. During the iterative testing and development, it was
observed that the simultaneous exposure to four warning systems was
usually overwhelming for most participants and precluded a clear
understanding of the behavior of the warning systems, adaptation
strategy, and HMI. As a result, the on-road testing was narrowed to two
separate studies, allowing the FCW and LDW systems to be tested
independently of one another. It was anticipated that this refined
approach would help to increase the transparency of warning system
behavior and the contrasts between the non-adaptive and adaptive modes.

[]{.mark}

## 11.5.1. FCW Validation Method

### *Participants*

Fourteen employees (eight men and six women) at Delphi Electronics and
Safety Systems in Kokomo, Indiana, participated in the on-road FCW
study. Employees were invited to participate via a randomly distributed
email and were selected when they successfully met the participation
requirements. In order to be eligible for the study, participants could
not require eyeglasses to drive, had to have a valid driver's license,
had to agree not to take driving-impairing drugs for at least 4 hours
before their scheduled drive, had to agree not to wear sunglasses for
the duration of the drive, could not work directly with the products
being tested, and women could not be pregnant.

All participants were between 36 and 59 years of age, with a mean age of
48, had between 20 and 43 years of driving experience, and drove between
10,000 and 40,000 miles per year. Five participants wore contact lenses
and nine did not require any vision correction. Six of the participants,
all male, were engineers, and the remaining eight participants had no
background in engineering or safety warning systems. Prior to testing,
seven of the 14 participants indicated that they were familiar with FCW
systems to some degree, typically through exposure at work,
participation in previous testing, or mainstream media. However, none of
the 14 participants acknowledged that they were familiar with the
concept of adaptation. One driver had brief exposure to the DSM unit,
but was not familiar with its interaction with the warning systems.
Accident and driving history were not considered as part of the
selection process. Participants were paid for their participation with a
\$75 Best Buy gift certificate.

### *Apparatus*

Equipment used in this experiment included the test vehicle driven by
participants, a 2000 Buick Le SABRE^®^, modified to include safety
warning, driver monitoring, and data collection systems. The FCW system
installed on the test vehicle consisted of a forward-looking radar
mounted above the front bumper and a forward-looking camera. Per the
results of the iterative testing and development, alerts were only
generated for moving targets or targets previously seen to move. The FCW
system HMI included auditory and visual warnings. A tonal sound served
as the auditory FCW alert and consisted of a series of four pulses of
400 Hz for a total duration of 0.5 s. The exogenous FCW alert consisted
of a red LED, reflected onto the windshield. When activated, the
exogenous display flashed three times at 5 Hz for a 0.5-s duration. The
auditory and visual warnings activated simultaneously when an FCW alert
occurred.

The test vehicle incorporated a Driver State Monitor (DSM) system that
included a combination camera and IR light source positioned in front of
the instrument cluster, partially occluding the gear selector and the
bottom of the speedometer. The test vehicle was also fitted with an
infotainment system that included a touch screen interface in the center
console area. Data were collected on a laptop using custom
data-acquisition software and included the processed DSM video, in
addition to video feeds from four camera locations: interior
roof-mounted, exterior left side, exterior right side, and forward-view.
CANalyzer software captured and displayed information about host vehicle
speed and acceleration, lead vehicle speed and acceleration, range,
brake switch activation, and steering angle. The software also recorded
information about FCW events, including the time of the event, alert
suppression status, the brake reaction time dictated by adaptation for
each event, and the activity of auditory and exogenous displays. The
laptop computer responsible for recording and displaying video and data
was located in the backseat, out-of-view of the driver.

### *Design*

All participants drove using the same FCW system, and experienced the
same non-adaptive and adaptive modes of the warning system, the order of
which was counterbalanced. The route was prescribed by the researcher
but consisted entirely of public roads. Although the majority of driving
took place on dry roads, two drives encountered snow flurries and
partially wet roadways and one drive experienced rain during part of the
route. No tests were conducted during heavy precipitation, on
snow-covered roads, or in icy conditions. In as much as it was possible
to achieve a naturalistic driving environment, drivers were instructed
not to induce FCW events or to test the warning system. Likewise, the
accompanying researcher had no control over the FCW system, the manner
in which adaptation was applied to the warning system, or the actions of
other drivers. While all participants generally drove the same route,
there was some variation. The length of FCW drives varied between 108 mi
and 136 mi, with an average length of 122 mi. The second half of the
drive followed the reverse of the first half. All drives took place on
weekdays, departed Delphi Electronics and Safety Systems between 10:00am
and 1:00pm, and typically lasted about 4 hours. Early-morning and
evening drives were not scheduled as a means of maintaining comparable
traffic density across drives.

The primary goal of the on-road FCW testing was to achieve a comparison
of the non-adaptive versus adaptive modes by examining alert frequency,
the effectiveness of adaptation, and subjective experiences of drivers.
Dependent measures included objective measures (i.e., alert-rate) and
subjective ratings (i.e., mode preference, annoyance, acceptability, and
HMI preferences).

### *Procedure*

After arriving at the test vehicle at Delphi Electronics and Safety
Systems, participants were instructed to adjust and position the
driver's seat, side and rear-view mirrors, fan, temperature, and
steering wheel height as they normally would. Participants were given a
\$75 Best Buy gift certificate and asked to provide the last four digits
of their Social Security Number for Delphi Vehicle Administration. The
researcher provided participants with the following information about
the study:

> *The purpose of this drive is to evaluate a Forward Collision Warning
> (FCW) system. The intent of Forward Collision Warning systems is to
> warn drivers of the threat of a collision with a vehicle, or object,
> in front of them. In other words, the goal of a Forward Collision
> Warning System is to reduce rear-end collisions by warning drivers
> when there is a threat of colliding with the vehicle in front of them.
> The focus of this test is to evaluate two different versions of that
> FCW system, non-adaptive and adaptive.*

Figure 11.20 was used as a visual aid to explain the concept of
adaptation. This simplified explanation of the adaptation strategies was
incorporated into training as a means of introducing concepts to drivers
by providing them with a tangible example of how they could expect the
system to behave. This explanation was not intended to capture the
specifics of alert timing (earlier, nominal, later) or the distinctions
between head pose (forward, not forward, extended not forward). As was
observed during the iterative testing and development, presenting naïve
drivers with too many specific details tended to impede their
understanding of the adaptation. It is preferable to achieve a balance
between identifying the system attributes being tested and providing a
complete list of system specifications. In-depth training was also
withheld so as to create a more naturalistic environment, so that
drivers felt less compelled to alter their normal driving behavior. The
researcher explained to participants:

> *In the non-adaptive mode, the FCW system provides all possible
> warnings to a driver, regardless of the driver's head-pose. In
> adaptive mode, however, the FCW system monitors head-pose and
> suppresses FCW alerts when the driver's head-pose is forward.*
>
> *The camera, mounted to the steering column, is able to monitor the
> driver's head-pose. By head-pose, we mean where the driver's head is
> pointed, either 'forward' or 'not forward' (demonstrated by the
> researcher). We are not, however, referring to eye gaze or eye
> closure, as fatigue testing is not the focus of this study.*
>
> *So, looking at the examples (see Figure 11.18), in the non-adaptive
> mode, the driver receives an FCW alert each and every time the threat
> of a forward collision is detected. In adaptive mode, when the
> driver's head-pose is very far to the left or very far to the right
> and the vehicle detects the threat of a forward collision, the driver
> receives an FCW alert, however, when the driver's head-pose is
> forward, or straight-ahead, an assumption is made that their attention
> is forward, they are likely aware of the situation, and can be
> expected to respond appropriately. In those cases the warning is
> turned off, or suppressed. Finally, the FCW system has a minimum
> activation speed of 20 mph, so below that speed, the system is
> completely inactive.*

  ---------------- ---------------- ----------------------------------------------------- ----------------------- -------------------
  **If your car    **...and your                                                          **...then the           **...then the
  detected the     Head Pose was:**                                                       [Non-Adaptive]{.mark}   [Adaptive]{.mark}
  threat of a                                                                             system will:**          system will:**
  Forward                                                                                                         
  Collision...**                                                                                                  

  Forward          Looking          ![](media/image24.png){width="0.8333333333333334in"   Warn You                Not Warn You
  Collision Threat Straight-ahead   height="0.5729166666666666in"}                                                
  Detected                                                                                                        

  Forward          Checking the     ![](media/image25.png){width="0.8333333333333334in"   Warn You                Not Warn You
  Collision Threat Driver-side      height="0.5729166666666666in"}                                                
  Detected         Mirror                                                                                         

  Forward          Checking the     ![](media/image26.png){width="0.8333333333333334in"   Warn You                Not Warn You
  Collision Threat Passenger-side   height="0.59375in"}                                                           
  Detected         Mirror                                                                                         

  Forward          Staring out the  ![](media/image27.png){width="0.8229166666666666in"   Warn You                Warn You
  Collision Threat Passenger-side   height="0.5833333333333334in"}                                                
  Detected         Window                                                                                         

  Forward          Checking the     ![](media/image28.png){width="0.8229166666666666in"   Warn You                Not Warn You
  Collision Threat Speedometer      height="0.5833333333333334in"}                                                
  Detected                                                                                                        

  Forward          Staring out the  ![](media/image29.png){width="0.8333333333333334in"   Warn You                Warn You
  Collision Threat Driver-side      height="0.59375in"}                                                           
  Detected         Window                                                                                         

  Forward          Checking the     ![](media/image30.png){width="0.8229166666666666in"   Warn You                Not Warn You
  Collision Threat Rearview Mirror  height="0.5833333333333334in"}                                                
  Detected                                                                                                        

  Forward          Staring at the   ![](media/image31.png){width="0.8020833333333334in"   Warn You                Warn You
  Collision Threat Navigation       height="0.5729166666666666in"}                                                
  Detected         System                                                                                         
  ---------------- ---------------- ----------------------------------------------------- ----------------------- -------------------

***Figure 11.20.** A comparison of adaptation as applied to the FCW
system.*

After receiving this explanation, participants were given an opportunity
to ask any questions they had about the instructions they had just
heard. Common questions included a clarification about head-pose versus
eye gaze and a restatement of the behavior of the non-adaptive versus
adaptive modes. Following some discussion, participants generally agreed
that they understood the concepts. Drives were divided into two halves,
non-adaptive and adaptive modes, the order of which was counterbalanced.
The researcher then asked the driver to look forward, as they would
while driving, and demonstrated the FCW alert three times for the
participant, pointing out both the auditory and visual components of the
warning. Participants were also informed that cruise control was not
active on the test vehicle and were cautioned against resting their hand
at the top of the steering wheel, so as not to block the DSM camera.

The researcher strongly emphasized to all drivers the importance of
driving as they normally would, not testing the system by inducing FCW
alerts, and not altering their driving to receive more or fewer alerts.
In accordance with the overall purpose of on-road testing, the goal of
the drives was not to achieve as many or as few FCW alerts as possible,
nor was it to evaluate participants' driving. Rather, it was to realize
a baseline frequency of FCW alerts during normal driving conditions and
to assess the effectiveness and acceptability of adaptation.
Participants were encouraged to report any impressions or opinions they
had about the FCW system and were told they would be given a
questionnaire at the end of the drive that focused heavily on the
comparison between the non-adaptive and adaptive modes. Drivers were
told to carefully focus on the comparison between the two halves of
their drives, as they might notice a difference in alert-rate or in the
timing of the alerts. Participants were encouraged to stop the drive at
any time if they did not feel comfortable with road conditions and were
told to let the researcher know if and when they needed a break.

Next, the researcher confirmed, via the video displayed on the laptop,
that DSM had successfully captured their image. In some cases, the DSM
camera had to be adjusted slightly or the participant was asked to raise
the height of the driver's seat, while maintaining a comfortable
position. In an effort to add clarity to the testing and reinforce the
concepts introduced during training, drivers were reminded which mode,
non-adaptive or adaptive, they were currently experiencing and how that
mode was intended to behave, as the drive began.

During the drive, the researcher provided turn-by-turn directions to the
drivers, as needed. The FCW route primarily covered three areas: Kokomo,
IN, Indiana State Route 26, and Lafayette/West Lafayette, IN (see Table
11.5 for a description of the three areas). At no time were drivers
asked to engage in a secondary task. Halfway through the drive, at
approximately 60 mi for most drives, the researcher changed the FCW
system to the second mode, again reminded drivers of the concepts
(non-adaptive and adaptive) introduced during training, and emphasized
the distinction between the two halves. The second half of all drives
retraced the route traveled in the first half. All participants were
familiar with the main roads traveled in the study and about one third
of drivers were familiar with the downtown and university campus
portions of the route. When a driver did receive an FCW alert, the
researcher made note of the nearest intersection or landmark and asked
the participant what their interpretation of the event was. One drive
was cut short and not included in the results due to heavy rain. At the
completion of the second half of the drive, participants were reminded
which mode each half of the drive corresponded to, how many alerts they
had received during each half, and were then administered the
questionnaire.

***Table 11.5.*** On-Road FCW Testing: Route Description.

+--------------+--------------+---------------+----+----+----+-----+---+
|              |              | Encountered   |    |    |    |     |   |
|              |              | as part of    |    |    |    |     |   |
|              |              | FCW Route     |    |    |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
| Location^1^  | Description  | Roadway       | M  | Po |    | T   |   |
|              |              | Type(s)       | il | st |    | raf |   |
|              |              |               | es | ed |    | fic |   |
|              |              |               |    | S  |    | Lig |   |
|              |              |               |    | pe |    | hts |   |
|              |              |               |    | ed |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
|              |              |               |    | Mi | Ma |     |   |
|              |              |               |    | n. | x. |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
| Kokomo, IN   | pop.         | Downtown City | 11 | 25 | 45 | 47  |   |
|              | 46,100^2^    |               |    | m  | m  |     |   |
|              |              | 4-lane        |    | ph | ph |     |   |
|              | 61 mi North  | highway       |    |    |    |     |   |
|              | of           |               |    |    |    |     |   |
|              |              |               |    |    |    |     |   |
|              | I            |               |    |    |    |     |   |
|              | ndianapolis, |               |    |    |    |     |   |
|              | IN           |               |    |    |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
| Indiana      | Russiaville, | 2-lane rural  | 36 | 30 | 55 | 3   |   |
| State Route  | IN           | highway       |    | m  | m  |     |   |
| 26           |              |               |    | ph | ph |     |   |
|              | Sedalia, IN  |               |    |    |    |     |   |
|              |              |               |    |    |    |     |   |
|              | Rossville,   |               |    |    |    |     |   |
|              | IN           |               |    |    |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
| Lafayette,   | pop.         | Downtown City | 14 | 25 | 55 | 40  |   |
| IN           | 91,000^2,3^  |               |    | m  | m  |     |   |
|              |              | 4-lane        |    | ph | ph |     |   |
| West         | 65 mi        | highway       |    |    |    |     |   |
| Lafayette,   | Northwest of |               |    |    |    |     |   |
| IN           | I            | Purdue        |    |    |    |     |   |
|              | ndianapolis, | University    |    |    |    |     |   |
|              | IN           |               |    |    |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
| ^1^          |              |               |    |    |    |     |   |
| Description  |              |               |    |    |    |     |   |
| represents   |              |               |    |    |    |     |   |
| one half of  |              |               |    |    |    |     |   |
| complete FCW |              |               |    |    |    |     |   |
| Route        |              |               |    |    |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
| ^2^          |              |               |    |    |    |     |   |
| Population,  |              |               |    |    |    |     |   |
| 2003 Census  |              |               |    |    |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+
| ^3^ Combined |              |               |    |    |    |     |   |
| Population   |              |               |    |    |    |     |   |
+--------------+--------------+---------------+----+----+----+-----+---+

## 11.5.2. FCW Validation Results

During the course of the study, a total of 83 alerts were issued. One of
those alerts did not include the auditory component because of a system
malfunction. In that case, the exogenous component of that FCW alert was
observed by the driver and brought to the researcher's attention. Upon
examination of the recorded data at the completion of the study, two
more FCW alerts were discovered. It is unlikely that the auditory
warning was activated in these cases, but it is unknown whether their
respective drivers detected the exogenous display, and since neither
acknowledged to the researcher that they were aware of those warnings,
these two FCW alerts were not included in the results. One driver
engaged in a cell phone conversation while driving, but did not
encounter any FCW incidents during that time. Several drivers did not
participate in a conversation, but interacted with cell phones to make
adjustments such as turning off the ringer.

***FCW Alert Rates***

Across both the non-adaptive and adaptive modes, drivers received a
total of 83 FCW alerts, 64 and 19, respectively. Drivers experienced
significantly more FCW alerts while in the non-adaptive mode (*M* =
4.57, *SD* = 1.65) than in comparison to the adaptive mode (*M* = 1.36,
*SD* = 1.39), *t*(26) = 5.56, *p* \< .01. See Table 11.6 for a summary
of total miles traveled and a total count of FCW alerts. The rate of
suppression reported in Table 11.6 is an approximate estimate based on
the difference in the number of alerts experienced in non-adaptive and
adaptive modes for that portion of the testing. As cited during the
iterative testing and development, driving styles ranged from
conservative to slightly aggressive at times and influenced the number
of alerts incurred by drivers. Total FCW alerts for an individual driver
ranged from between two to ten alerts.

***Table 11.6.*** On-Road FCW Testing: Alert Rate, Total Miles, and
Suppression Rate.

<table>
<colgroup>
<col style="width: 14%" />
<col style="width: 2%" />
<col style="width: 12%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 7%" />
<col style="width: 10%" />
<col style="width: 4%" />
<col style="width: 14%" />
<col style="width: 17%" />
</colgroup>
<tbody>
<tr class="odd">
<td><strong>Non-Adaptive</strong></td>
<td colspan="2"><strong>Adaptive</strong></td>
<td colspan="2"><strong>Overall</strong></td>
<td colspan="2"><strong>Rate of Suppression</strong></td>
<td colspan="3"></td>
</tr>
<tr class="even">
<td colspan="2" rowspan="4">Kokomo, IN</td>
<td colspan="2">Miles</td>
<td colspan="2">150</td>
<td colspan="2">150</td>
<td>300</td>
<td rowspan="4">80%</td>
</tr>
<tr class="odd">
<td colspan="2">Alerts</td>
<td colspan="2">25</td>
<td colspan="2">5</td>
<td>30</td>
</tr>
<tr class="even">
<td colspan="2">Mean Alerts</td>
<td colspan="2">1.79</td>
<td colspan="2">0.36</td>
<td>1.07</td>
</tr>
<tr class="odd">
<td colspan="2">Alerts per Mile</td>
<td colspan="2">0.17</td>
<td colspan="2">0.03</td>
<td>0.10</td>
</tr>
<tr class="even">
<td colspan="2" rowspan="4"><p>Indiana State</p>
<p>Route 26</p></td>
<td colspan="2">Miles</td>
<td colspan="2">504</td>
<td colspan="2">504</td>
<td>1008</td>
<td rowspan="4">80%</td>
</tr>
<tr class="odd">
<td colspan="2">Alerts</td>
<td colspan="2">10</td>
<td colspan="2">2</td>
<td>12</td>
</tr>
<tr class="even">
<td colspan="2">Mean Alerts</td>
<td colspan="2">0.71</td>
<td colspan="2">0.14</td>
<td>0.43</td>
</tr>
<tr class="odd">
<td colspan="2">Alerts per Mile</td>
<td colspan="2">0.01</td>
<td colspan="2">0.004</td>
<td>0.01</td>
</tr>
<tr class="even">
<td colspan="2" rowspan="4"><p>Lafayette</p>
<p>&amp;</p>
<p>West</p>
<p>Lafayette, IN</p></td>
<td colspan="2">Miles</td>
<td colspan="2">195</td>
<td colspan="2">195</td>
<td>390</td>
<td rowspan="4">59%</td>
</tr>
<tr class="odd">
<td colspan="2">Alerts</td>
<td colspan="2">29</td>
<td colspan="2">12</td>
<td>41</td>
</tr>
<tr class="even">
<td colspan="2">Mean Alerts</td>
<td colspan="2">2.07</td>
<td colspan="2">0.86</td>
<td>1.46</td>
</tr>
<tr class="odd">
<td colspan="2">Alerts per Mile</td>
<td colspan="2">0.15</td>
<td colspan="2">0.06</td>
<td>0.11</td>
</tr>
<tr class="even">
<td colspan="2" rowspan="4"><strong>Overall</strong></td>
<td colspan="2"><strong>Miles</strong></td>
<td colspan="2"><strong>849</strong></td>
<td colspan="2"><strong>849</strong></td>
<td><strong>1698</strong></td>
<td rowspan="4"><strong>70%</strong></td>
</tr>
<tr class="odd">
<td colspan="2"><strong>Alerts</strong></td>
<td colspan="2"><strong>64</strong></td>
<td colspan="2"><strong>19</strong></td>
<td><strong>83</strong></td>
</tr>
<tr class="even">
<td colspan="2"><strong>Mean Alerts</strong></td>
<td colspan="2"><strong>4.57</strong></td>
<td colspan="2"><strong>1.36</strong></td>
<td><strong>5.93</strong></td>
</tr>
<tr class="odd">
<td colspan="2"><strong>Alerts per Mile</strong></td>
<td colspan="2"><strong>0.07</strong></td>
<td colspan="2"><strong>0.02</strong></td>
<td><strong>0.05</strong></td>
</tr>
</tbody>
</table>

Drivers experienced a higher alert-rate per mile in the non-adaptive
mode (*M* = .07, *SD* = .03) than in the adaptive mode (*M* = .02, *SD*
= .02), *t*(26) = 5.71, *p* \< .01. An inspection of road types showed
that the rate of alerts per mile for city driving (Kokomo and
Lafayette/West Lafayette combined) (*M* = .10, *SD* = .05) was higher
than that of rural highway (Route 26) (*M* = .01, *SD* = .01), *t*(26) =
6.99, *p* \< .01 (see Table 11.6).

***FCW Cause***

When an FCW alert occurred, drivers were asked what their interpretation
of the event was (see Table 11.7). Out of a total of 83 FCW events, the
most common cause reported by drivers was the lead vehicle turning out
of path (n = 37), usually onto a cross street or into a parking lot. The
second most frequent cause was the lead vehicle braking or slowing for
either a traffic signal or slower/congested traffic (n = 21). The host
vehicle changing lanes, usually as part of a passing maneuver, was the
next most popular cause (n = 17). Together, the top three categories
accounted for more than 90 percent of FCW alerts, as reported by the
participant. In all but one of the 83 instances, the driver's
interpretation of the cause agreed with the accompanying researcher's
opinion.

***Table 11.7.*** On-Road FCW Testing: Alert Cause.

+--------------------------------------+-------+-------+-----+-------+
| **FCW Cause**                        | *     | **    | **T | **Pe  |
|                                      | *Non- | Adapt | ota | rcent |
| **(as reported by driver)**          | Adapt | ive** | l** | of    |
|                                      | ive** |       |     | To    |
|                                      |       |       |     | tal** |
+--------------------------------------+-------+-------+-----+-------+
| Lead Vehicle Turning Out of Path     | 30    | 7     | **3 | 44.6% |
|                                      |       |       | 7** |       |
+--------------------------------------+-------+-------+-----+-------+
| Lead Vehicle Braking/Slowing         | 14    | 7     | **2 | 25.3% |
|                                      |       |       | 1** |       |
+--------------------------------------+-------+-------+-----+-------+
| Host Vehicle Changing Lanes and      | 14    | 3     | **1 | 20.5% |
| Passing                              |       |       | 7** |       |
+--------------------------------------+-------+-------+-----+-------+
| Lead Vehicle Changing Lanes          | 4     | 0     | **  | 4.8%  |
| (Departing Host Lane)                |       |       | 4** |       |
+--------------------------------------+-------+-------+-----+-------+
| Lead Vehicle Perpendicularly Crossed | 1     | 1     | **  | 2.4%  |
| Path of Host                         |       |       | 2** |       |
+--------------------------------------+-------+-------+-----+-------+
| Lead Vehicle Braking/Slowing in      | 1     | 0     | **  | 1.2%  |
| Adjacent Lane                        |       |       | 1** |       |
+--------------------------------------+-------+-------+-----+-------+
| Opposing Lead Vehicle in Opposing    | 0     | 1     | **  | 1.2%  |
| Left Turn Lane^1^                    |       |       | 1** |       |
+--------------------------------------+-------+-------+-----+-------+
| **Total**                            | *     | *     | **8 |       |
|                                      | *64** | *19** | 3** |       |
+--------------------------------------+-------+-------+-----+-------+
| ^1^ Driver possibly misinterpreted   |       |       |     |       |
| event; likely cause was Lead Vehicle |       |       |     |       |
| Braking/Slowing.                     |       |       |     |       |
+--------------------------------------+-------+-------+-----+-------+

Unless otherwise noted, all 14 drivers responded to each of the
following questions.

***FCW System and Adaptation Understandability***

Following completion of the drive, participants were asked to rate their
agreement with the following statement, "The distinction between
non-adaptive and adaptive modes was clear after a) training and b)
driving", on a 4-point Likert Scale (Strongly Disagree = 1, Somewhat
Disagree = 2, Somewhat Agree = 3, Strongly Agree = 4). Respondents
typically agreed that the distinction was clear after both training (*M*
= 3.64, *SD* = .84) and driving (*M* = 3.79, *SD* = .43). Across both
questions, only one of the 14 participants reported that they 'Strongly
Disagreed' with the statement regarding training. It is plausible that
this answer was an error, as the participant expressed no concerns to
the researcher during training and provided a correct explanation of
adaptation on the post-drive questionnaire.

In an effort to confirm participants' reported level of understanding,
drivers were asked, "How would you explain the FCW system, the
non-adaptive mode, and the adaptive mode in detail to someone who was
unfamiliar them?" A number of participants gave incomplete answers, and
several drivers provided inaccurate information. Four out of the 14
drivers provided explanations that could be declared at least partially
incorrect, as rated by the researcher.

One driver reported that the FCW system "cautions \[a\] driver that a
vehicle either in front or behind him \[is\] closing in the 'safety
gap'". A second driver said the FCW system "lets you know when the
vehicle ahead is stopping or changing lanes". Another participant said
the non-adaptive mode alerts drivers "every time a car stops abruptly or
changes lanes" and the adaptive mode alerts when "you stop abruptly".
The adaptive mode was described by one driver as a "front/back object
detection warning notifying the driver to check driving road and/or
mirrors for warning signal cause".

It is apparent that these participants failed to capture the intention
of the FCW system, which is to warn drivers when they incur the threat
of an imminent forward collision, rather than any time the lead vehicle
simply slows or changes lanes. This error may reflect their experience
with the FCW system during the drive, particularly if they perceived
those FCW alerts that occurred during lead vehicle lane changes to be
nuisance warnings. Likewise, the pre-drive training administered by the
researcher may have failed to clearly communicate the severity of an
imminent forward collision threat. Two drivers questioned whether the
FCW system detected collision threats from the rear of their vehicle and
warned them if they were about to be struck. This confusion might have
been avoided by noting the location of the radar during training.

These answers also generally fail to capture an accurate representation
of adaptation. It is possible that the four drivers who failed to
provide accurate descriptions may have been hesitant or less motivated
to ask for clarification when they had questions. There is little doubt
that one participant was simply not engaged in the task. Some
participants, particularly those with an engineering background,
demonstrated a curiosity about the behavior of the warning system and an
awareness of the concepts, prompting questions and observations from
them throughout the drive. Drivers without an engineering background
might be less likely to consider the specific concepts involved, making
errors on this type of question more likely. Notably, all four drivers
whose answers could not be classified as correct were non-engineers.
This observation suggests important considerations about an introduction
method of the adaptation strategy to a broader audience. It also brings
to light concerns about accepting participants' self-report measures for
assessing their level of understanding about a particular concept. In
all four of these cases, participants' willingness to 'Strongly Agree'
that the distinction between modes did not reflect their ability to
accurately explain those differences in their own words.

***FCW Preferred Mode***

When asked which mode of the FCW system they would prefer to use, more
drivers chose adaptive (n = 10), as compared with non-adaptive (n= 3),
and 'not sure' (n = 1). Only one driver received an equal number of
alerts in each half and, accordingly, selected 'not sure' in response to
preference. The remaining 13 drivers all received more alerts in
non-adaptive than in adaptive. The preference for adaptive mode
approached statistical significance, χ^2^(1) = 3.77, *p* = .052.

When drivers preferred the adaptive mode, they reported that their
preference was based predominantly on the frequency of the FCW alerts
they had experienced, that is, fewer alerts in adaptive mode than in
non-adaptive. Almost all of the ten drivers who selected the adaptive
mode stated that the lower alert-rate was the most important factor in
their decision. It is fair to assume the pre-drive explanations provided
by the researcher also contributed to their understanding and acceptance
of why fewer alerts were generated in adaptive. One participant
elaborated by saying that she preferred the lower alert rate because
most of the FCW "situations were not that threatening". Another driver
suggested that he preferred the lower alert rate because he thought
drivers would "become immune" to the warning if it sounded too often and
it would eventually fail to capture their attention.

Drivers who expressed a preference for the non-adaptive mode offered
several explanations. One participant said that adaptive "felt like it
wasn't doing anything". He added that he would rather be "safe than
sorry" and since the warning is "no more annoying than a turn signal",
he preferred the non-adaptive mode. A second driver expressed a similar
sentiment and said that "it felt like it \[the adaptive mode\] wasn't
on". She added that the non-adaptive mode brought more to her attention
by making her more aware of the surroundings. Another driver stated that
she did not find the warnings to be annoying and preferred that the
warning system provide an alert even when she was looking forward, but
"daydreaming", or cognitively distracted.[^2] The same respondent did
express concern that if the alert was presented too often, she might
"develop a tolerance to it". The perception that the FCW alert was not
annoying and that more than one driver anticipated that frequent
exposure might lead to a failure to capture the driver's attention is
notable. A long-term study might offer insight into whether these
perceptions persist, or are the effect of relatively short exposure to
the system.

Although the overall preference for adaptive mode was expected, warning
system behavior and the adaptive strategy were less transparent to
drivers than originally anticipated. While they did attribute the lower
alert-rate to adaptation, drivers generally did not identify the
specifics of adaptation, like the effect of head pose on timing, in
their explanations or during the drive. Several issues might have
contributed to this result.

-   It seemed to have been challenging for some drivers to appreciate
    that they, and specifically their head pose, could play a role in an
    alert's activation or suppression. In several instances, drivers in
    adaptive mode commented that they would have expected an FCW alert
    after a particular lead vehicle event, and wondered why they had not
    received one. It seemed that some drivers had to be reminded that
    they were in adaptive mode, where alerts are delayed and often
    suppressed when the driver's head pose is forward, and were
    encouraged to consider what their head pose was at the time of the
    event and how that might have impacted the behavior of the potential
    FCW alert.

-   In many cases, drivers commented to the researcher that they did not
    understand why they had received a particular FCW alert. While they
    were almost always able to identify the lead vehicle that had been
    the target of the alert, drivers often said that they felt like they
    had maintained a sufficient distance and did not know why their car
    was warning them. It proved especially difficult in these cases to
    overcome their concerns about the validity of the alert and have
    them instead focus on how their head pose might have influenced the
    alerts or to consider the timing of the alert. In response to some
    of their observations, the researcher attempted to refocus the
    driver on what their *preference* for receiving an FCW alert in that
    situation would have been, and encouraged them to consider their
    level of attentiveness (head pose), rather than external factors
    like the radar's detection range or the intent of the lead vehicle's
    driver.

Developing a complete working knowledge of the details of the FCW system
and the nuances of the adaptation strategy may not be reasonable in a
relatively short exposure like this one. While drivers had a clear
preference for the adaptive mode, it is apparent that alert frequency
was one of the largest, and in some cases one of the only, factors that
influenced their decision.

***FCW Alert Timing***

Drivers were asked to rate the timing of the FCW alerts that they
received in each of the two modes on a 5-point Likert Scale (1 = Very
Early, 2 = Somewhat Early, 3 = Just Right, 4 = Somewhat Late, 5 = Very
Late). On average, drivers rated the adaptive mode (n = 13, *M* = 2.85,
*SD* = .38) closer to 'Just Right' than the non-adaptive mode, (*M* =
2.71, *SD* = .91). Responses for the non-adaptive mode varied from 'Very
Early' to 'Somewhat Late', whereas only two drivers rated the adaptive
mode anything other than 'Just Right' (see Figure 11.21).

![](media/image32.wmf){width="4.510416666666667in"
height="2.717361111111111in"}

***Figure 11.21.** FCW Alert Timing as Rated by Drivers.*

***FCW Alert Behavior***

Participants' expectations of alert behavior were measured on a 4-point
Likert Scale. Drivers were slightly more likely to agree that the
adaptive mode (*M* = 3.29, *SD* = .61) behaved as they had expected it
to as compared with the non-adaptive mode (*M* = 3.14, *SD* = .36) (see
Figure 11.22). More participants 'Strongly Agreed' that the adaptive
mode behaved as they had expected it to than the non-adaptive. One
driver, who reported he 'Somewhat Disagreed' with the adaptive statement
thought he had been looking forward when an FCW alert occurred. Another
driver commented that he was "surprised at the adjacent lane warning".

***FCW Acceptability of Alert Frequency***

Participants rated their agreement with the statement, "the frequency of
FCW alerts would be acceptable to me, in my own car, during everyday
driving", on a 4-point Likert Scale. Respondents rated their agreement
in the adaptive mode (*M* = 3.43, *SD* = .76) higher than in the
non-adaptive mode (*M* = 2.57, *SD* = .85) (see Figure 11.22). Of the
two drivers who chose 'Somewhat Disagree' for adaptive mode, one
reported a preference for the non-adaptive mode, preferring the higher
alert-rate.

Inevitably, the acceptance of alerts was partially influenced by
individual differences. Some drivers responded to alerts by commenting
that, "I must have been too close to that \[lead\] vehicle", and
seemingly accepted the alert as a correction. Other drivers, however,
announced that they "had plenty of room", and did not understand the
reason for the warning. In a few cases, they seemed to view the alert as
an unreasonable critique of their driving.

***FCW Perception of Nuisance Alerts***

Drivers were asked "what percentage of FCW alerts would you classify as
nuisance warnings?" and chose between, 0% - 24%, 25% - 49%, 50% - 74%,
or 75% - 100%. As shown in Figure 11.22, 13 out of 14 selections fell
below 50% in adaptive, while only six of 14 fell below 50% in their
non-adaptive ratings. Interestingly, participants differed in their
interpretation of a 'nuisance alert'. One driver commented that provided
that the auditory and visual components did not annoy her, she did not
consider it a nuisance alert. A second driver expressed that he did not
classify them as nuisance alerts provided that, "I understood why I was
getting them".

***FCW Acceptability of Nuisance Alert Frequency***

As a follow-up to question, drivers were asked to rate their agreement
with, "the frequency of nuisance FCW alerts would be acceptable to me,
in my own car, during everyday driving". The mean response, on a 4-point
Likert Scale was higher for adaptive (*M* = 3.43, *SD* = .65), than for
non-adaptive, (*M* = 2.57, *SD* = .94) (see Figure 11.22).

***FCW On-Road Safety***

Drivers were asked to rate their agreement with "the FCW system enhances
on-road safety". The higher alert-rate in non-adaptive appeared to have
only a small impact on drivers' perceptions of safety. Adaptive mode
(*M* = 3.36, *SD* = .50) was rated only slightly higher than
non-adaptive (*M* = 3.21, *SD* = .70) (see Figure 11.22).

***FCW Alert Distraction***

Participants rated their agreement with, "the FCW system was
distracting". As shown in Figure 11.22, the non-adaptive mode received a
higher rating (*M* = 2.00, *SD* = .96) than adaptive (*M* = 1.50, *SD* =
.76). Drivers presumably found the adaptive mode less distracting
because of the lower alert-rate and the explanation of the adaptation
concept during training.

![](media/image33.wmf){width="2.9993055555555554in"
height="1.8777777777777778in"}![](media/image34.wmf){width="3.0in"
height="1.8666666666666667in"}

![](media/image35.wmf){width="2.9993055555555554in"
height="1.8694444444444445in"}

![](media/image36.wmf){width="2.9993055555555554in"
height="1.8722222222222222in"}

![](media/image37.wmf){width="3.0in" height="1.8666666666666667in"}

![](media/image38.wmf){width="2.9993055555555554in"
height="1.8694444444444445in"}

![](media/image39.wmf){width="2.9993055555555554in"
height="1.8722222222222222in"}

![](media/image40.wmf){width="2.998611111111111in" height="1.875in"}

***FCW Likelihood to Recommend***

In order to ascertain drivers' overall attitudes toward each mode,
participants were asked to rate their agreement with the following
statement, "I would recommend the FCW system to other drivers", on a
4-point Likert Scale. The mean response for the adaptive mode (*M* =
3.21, *SD* = .58) was higher in comparison to the non-adaptive mode (*M*
= 2.79, *SD* = .89) (see Figure 11.22). Only one of 14 disagreed in
response to a recommendation of the adaptive mode, while five drivers
disagreed with the non-adaptive mode. One of the three drivers who
preferred the non-adaptive mode was the only driver to 'Somewhat
Disagree' with an adaptive mode recommendation. He reported that,
"adaptive seemed to filter virtually everything out". One driver
commented that she chose not to recommend non-adaptive because of the
higher alert-rate, and added that this mode felt "too sensitive". A
second driver reiterated that comment by saying the non-adaptive mode
felt "too sensitive and too proactive" and called it "annoying". A third
driver also reported that the non-adaptive mode could be "a little less
sensitive". He later added that he felt he had received alerts for
turning lead vehicles that were "almost through the turn", implying that
those warnings were unnecessary. Another driver reported that she was
more likely to recommend the adaptive mode because it was a "little more
accurate". One participant disagreed with the non-adaptive
recommendation because there were "too many nuisance alerts".

***FCW Alerts***

As FCW Alerts occurred during the drive, the researcher noted a location
or landmark to identify it and drivers were asked to provide specific
ratings for each of those alerts on the post-drive questionnaire. On
5-point Likert Scales (1 = Not at All and 5 = Very Much), drivers rated
how threatening the situation was, whether they understood the alert,
how annoying the alert was, how useful the alert was, how distracted
they were at the moment of the alert, how much they needed the alert to
avoid colliding with the lead vehicle, and how acceptable the alert was.
They also responded to, "I would have preferred that this FCW was
suppressed", on a 4-point agreement scale. Ratings were obtained for
each of the 83 FCW alerts. The mean rating and standard deviation for
each question as a function of adaptation are displayed in Table 11.8,
and as a function of the top three FCW causes in Table 11.9.

***Table 11.8.*** On-Road FCW Testing: Alert Ratings by Adaptation.

+--------------------------------------------------+----------+-------+
|                                                  | Non-     | Ada   |
|                                                  | Adaptive | ptive |
|                                                  |          |       |
|                                                  | (n = 64) | (n =  |
|                                                  |          | 19)   |
+--------------------------------------------------+----------+-------+
| This situation posed a threat.^2^                | 1.7      | 1.5   |
|                                                  | (0.8)^1^ | (0.8) |
+--------------------------------------------------+----------+-------+
| I understood the cause of this FCW.^2^           | 4.0      | 3.4   |
|                                                  | (1.3)    | (1.7) |
+--------------------------------------------------+----------+-------+
| The FCW was annoying.^2^                         | 2.5      | 1.8   |
|                                                  | (1.1)\*  | (0    |
|                                                  |          | .9)\* |
+--------------------------------------------------+----------+-------+
| The FCW was useful.^2^                           | 2.6      | 2.1   |
|                                                  | (1.1)    | (1.0) |
+--------------------------------------------------+----------+-------+
| I was distracted at the time of this FCW.^2^     | 1.5      | 1.4   |
|                                                  | (0.8)    | (0.1) |
+--------------------------------------------------+----------+-------+
| I needed this FCW in order to avoid colliding    | 1.3      | 1.2   |
| with the vehicle in front of me.^2^              | (0.5)    | (0.4) |
+--------------------------------------------------+----------+-------+
| This FCW was acceptable.^2^                      | 3.2      | 2.8   |
|                                                  | (1.0)    | (1.3) |
+--------------------------------------------------+----------+-------+
| I would have preferred that this FCW was         | 2.5      | 2.8   |
| suppressed.^3^                                   | (1.0)    | (1.0) |
+--------------------------------------------------+----------+-------+
| ^1^ Mean (SD)                                    |          |       |
|                                                  |          |       |
| ^2^ 5-point Scale (1 = Not at All 5 = Very Much) |          |       |
|                                                  |          |       |
| ^3^ 4-point Scale (1 = Strongly Disagree 4 =     |          |       |
| Strongly Agree).                                 |          |       |
|                                                  |          |       |
| \* p \< .05                                      |          |       |
+--------------------------------------------------+----------+-------+

There appear to be far fewer differences between ratings of the
non-adaptive and adaptive modes than expected. The only significant
different between non-adaptive and adaptive modes occurred for
annoyance. Drivers rated the non-adaptive alerts (*M* = 2.5, *SD* = 1.1)
as more annoying than the adaptive alerts (*M* = 1.8, *SD* = 0.9) (χ^2^
(4) = 10.159, *p* = .038). Trends in results suggest that drivers may
have had a slightly greater understanding of alert cause in non-adaptive
(*M* = 4.0, *SD* = 1.3), than in adaptive (*M* = 3.4, *SD* = 1.7). While
non-adaptive alerts were significantly more annoying, they were also
slightly more useful (*M* = 2.6, *SD* = 1.1) and slightly more
acceptable (*M* = 3.2, *SD* = 1.0) than their adaptive counterparts,
(*M* = 2.1, *SD* = 1.0) and (*M* = 2.8, *SD* = 1.3). It is apparent that
drivers did not consider themselves 'distracted' in either the
non-adaptive (*M* = 1.7, *SD* = 0.8) or adaptive modes (*M* = 1.5, *SD*
= 0.8), which raises an important consideration about the definitions
and language applied to DSM and adaptive mode. Drivers might have been
more likely to agree if the question referred to 'non-forward' head pose
(e.g., checking a blind spot) rather than 'distraction'. Providing a
definition of the term 'distraction' in the context of this study to
include all non-forward head poses (e.g., looking at a cell phone, doing
a blind spot check) might have added more clarity to this question and
reduced some of the negative connotation associated with it. The term,
'distraction', was likely misapplied in this question, particularly
since it was not used during training. The large difference between the
number of alerts in each category (non-adaptive = 64, adaptive = 19),
likely contributed to the large number of results that were not
significantly different.

***Table 11.9.*** On-Road FCW Testing: Alert Ratings by Top Three FCW
Causes.

+--------------------------------+-----+------+-----+-----+----+----+
|                                | L   |      | L   |     | Ho |    |
|                                | ead |      | ead |     | st |    |
|                                | V   |      | V   |     | V  |    |
|                                | ehi |      | ehi |     | eh |    |
|                                | cle |      | cle |     | ic |    |
|                                | T   |      | Br  |     | le |    |
|                                | urn |      | aki |     | Ch |    |
|                                | ing |      | ng/ |     | an |    |
|                                | Out |      | S   |     | gi |    |
|                                | of  |      | low |     | ng |    |
|                                | P   |      | ing |     | L  |    |
|                                | ath |      |     |     | an |    |
|                                |     |      | (n  |     | es |    |
|                                | (n  |      | =   |     | a  |    |
|                                | =   |      | 21) |     | nd |    |
|                                | 37) |      |     |     | P  |    |
|                                |     |      |     |     | as |    |
|                                |     |      |     |     | si |    |
|                                |     |      |     |     | ng |    |
|                                |     |      |     |     |    |    |
|                                |     |      |     |     | (n |    |
|                                |     |      |     |     | =  |    |
|                                |     |      |     |     | 1  |    |
|                                |     |      |     |     | 7) |    |
+--------------------------------+-----+------+-----+-----+----+----+
| This situation posed a         | 1.5 | (    | 1.8 | (0  | 1  | (  |
| threat.^2^                     |     | 0.7) |     | .9) | .6 | 0. |
|                                |     |      |     |     |    | 8) |
+--------------------------------+-----+------+-----+-----+----+----+
| I understood the cause of this | 4.1 | (    | 3.4 | (1  | 3  | (  |
| FCW.^2^                        |     | 1.4) |     | .6) | .9 | 1. |
|                                |     |      |     |     |    | 3) |
+--------------------------------+-----+------+-----+-----+----+----+
| The FCW was annoying.^2^       | 2.5 | (    | 2.2 | (0  | 2  | (  |
|                                |     | 1.1) |     | .9) | .3 | 1. |
|                                |     |      |     |     |    | 1) |
+--------------------------------+-----+------+-----+-----+----+----+
| The FCW was useful.^2^         | 2.3 | (    | 2.7 | (1  | 2  | (  |
|                                |     | 1.1) |     | .1) | .5 | 1. |
|                                |     |      |     |     |    | 3) |
+--------------------------------+-----+------+-----+-----+----+----+
| I was distracted at the time   | 1.4 | (0.  | 1.7 | (   | 1  | (  |
| of this FCW.^2^                |     | 8)\* |     | 1.1 | .5 | 0. |
|                                |     |      |     | )\* |    | 7) |
|                                |     |      |     |     |    | \* |
+--------------------------------+-----+------+-----+-----+----+----+
| I needed this FCW in order to  | 1.2 | (    | 1.4 | (0  | 1  | (  |
| avoid colliding with the       |     | 0.4) |     | .7) | .2 | 0. |
| vehicle in front of me.^2^     |     |      |     |     |    | 4) |
+--------------------------------+-----+------+-----+-----+----+----+
| This FCW was acceptable.^2^    | 2.8 | (    | 3.3 | (1  | 3  | (  |
|                                |     | 1.1) |     | .2) | .4 | 0. |
|                                |     |      |     |     |    | 8) |
+--------------------------------+-----+------+-----+-----+----+----+
| I would have preferred that    | 2.7 | (    | 2.5 | (1  | 2  | (  |
| this FCW was suppressed.^3^    |     | 1.0) |     | .0) | .5 | 0. |
|                                |     |      |     |     |    | 8) |
+--------------------------------+-----+------+-----+-----+----+----+
| ^1^ Mean (SD)                  |     |      |     |     |    |    |
|                                |     |      |     |     |    |    |
| ^2^ 5-point Scale (1 = Not at  |     |      |     |     |    |    |
| All, 5 = Very Much)            |     |      |     |     |    |    |
|                                |     |      |     |     |    |    |
| ^3^ 4-point Scale (1 =         |     |      |     |     |    |    |
| Strongly Disagree, 4 =         |     |      |     |     |    |    |
| Strongly Agree).               |     |      |     |     |    |    |
|                                |     |      |     |     |    |    |
| \* p \< .05                    |     |      |     |     |    |    |
+--------------------------------+-----+------+-----+-----+----+----+

The individual alert ratings provided an opportunity to further examine
the three most common causes of FCW alerts, as perceived by the driver,
lead vehicle turning out of path (n = 37), lead vehicle braking/slowing
(n = 21), and host vehicle changing lanes and passing (n = 17). The only
significant result was found in the ratings for distraction (χ^2^ (6) =
15.477, *p* = .017). It is notable that drivers did not feel that any of
the top three causes constituted a threatening situation, as the means
for all causes were ≤ 1.8. Trends in the data show that drivers seemed
to have had a greater understanding of lead vehicle turning out of path
alerts, but also found them the most annoying, the least useful, and the
least acceptable.

***FCW HMI***

Drivers were asked several questions about their HMI preferences, the
first of which was, "of the two types of FCW alerts that you
experienced, which one was best at capturing your attention?" All but
one of the 14 respondents chose 'audio', χ^2^(1) = 10.29, *p* \< .001
(see Figure 11.23). The next HMI question asked drivers, "which of the
two warning types was most annoying?" The majority, 10 of 14
respondents, again selected 'audio'. Three drivers added that they had
not found either type of warning to be especially annoying. One driver,
whose mode preference was non-adaptive, added that he selected 'visual'
as most annoying, in part, because he anticipated that he might focus
more on "the light than the lead vehicle's bumper". His observation is
particularly interesting because this concern could be alleviated by
using the adaptive mode, instead of non-adaptive. Another participant
added that the light might be especially annoying in the dark.

![](media/image41.wmf){width="4.802083333333333in"
height="2.7805555555555554in"}

***Figure 11.23.** FCW HMI Ratings.*

The next series of HMI questions asked drivers "if you were to receive
an FCW alert while you were a) attentive, b) distracted, and c)
fatigued, which combination of warnings would you prefer?" Responses to
the attentive state were nearly equally distributed across 'audio only',
'visual only', and 'audio and visual' (see Figure 11.24). However,
responses to the distracted and fatigued states showed a much stronger
preference for 'audio and visual'. A chi-square test indicated that the
preference for the combination of warnings while distracted was
significant, χ^2^(1) = 4.57, *p* \< 0.03. A chi-square test indicated
that the preference for warning type while fatigued was also
significant, χ^2^(1) = 7.14, *p* \< 0.01.

![](media/image42.wmf){width="4.8125in" height="2.6256944444444446in"}

***Figure 11.24.** FCW HMI Preferences.*

On a final note about drivers' HMI experiences, several drivers found
that the cover of the exogenous display was noticeably reflective on the
windshield, and was in some cases, bothersome.

## 11.5.3. LDW Validation Method

### *Participants*

Fourteen employees, eleven men and three women, at Delphi Electronics
and Safety Systems in Kokomo, Indiana, participated in the on-road LDW
study. Employees were invited to participate using the same method and
were selected using the same criteria as the previous study. All
participants were between 29 and 54 years of age, with a mean age of 42,
had between 13 and 38 years of driving experience, and drove between
10,000 and 30,000 miles per year. Two participants wore contact lenses
and 11 did not require any vision correction. Although driving without
eyeglasses was a criterion for the study, one participant was included
who wore them. Eleven of the participants (eight men, three women) were
engineers, and the remaining three participants did not have a
background in engineering. No driver participated in more than one of
the data fusion studies.

Prior to testing, three of the 14 participants indicated that they were
familiar with LDW systems to some degree, typically through exposure at
work, participation in previous testing, or mainstream media. However,
as with the FCW study, none of the 14 participants acknowledged that
they were familiar with the concept of adaptation. Accident and driving
history were not considered as part of the selection process.
Participants were paid for their participation with a \$75 Best Buy gift
certificate.

### *Apparatus*

Equipment used in this experiment included the same test vehicle, a 2000
Buick Le SABRE^®^, described in the FCW study. The LDW system consisted
of a forward-looking camera and an HMI that included audio, visual, and
haptic warnings. A rumble strip sound, four 125-msec bursts of 100 Hz
tones served as the auditory LDW alert and was issued on the
corresponding side of the lane crossing. The exogenous LDW alert
consisted of a red LED, reflected onto the windshield. When activated,
the exogenous display flashed three times at 5 Hz for 0.5 s. The LDW
system also included the addition of a haptic warning. A HoMedics BK
4500 Back Massager was used for the haptic-seat warning. 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 125-msec bursts, which corresponded to the
direction of the LDW event, and lasted a total of 1 s. The audio,
visual, and haptic warnings were activated simultaneously when an LDW
alert occurred.

### *Design*

All participants drove using the same LDW system, and experienced the
same non-adaptive and adaptive modes of the warning system, the order of
which was counterbalanced. The route was dictated by the researcher and
consisted entirely of public roads. All driving took place on dry roads,
on sunny or partly sunny days.

As in the FCW study, drivers were encouraged not to induce LDW events or
to test the warning system. Likewise, the accompanying researcher had no
control over the LDW system, the manner in which adaptation was applied
to the warning system, or the actions of other drivers. While all
participants generally drove the same route, there was some variation.
The length of LDW drives varied between 76 mi and 155 mi, with an
average of length of 149 mi. All drives took place on weekdays, departed
Delphi Electronics and Safety Systems between 8:00am and 1:00pm, and
typically lasted about 4 hours.

The goals of the LDW testing were identical to the previous study, that
was to achieve a comparison of the non-adaptive versus adaptive modes by
examining alert frequency, the effectiveness of adaptation, and drivers'
subjective experiences. Dependent measures included objective measures
(i.e., alert-rate) and subjective ratings (i.e., mode preference,
annoyance, acceptability, and HMI preferences).

### *Procedure*

With the exception of the warning system details noted below, the
procedure replicated that of the FCW drives. The researcher provided
participants with the following information about the study:

> *The purpose of this drive is to evaluate a Lane Departure Warning
> (LDW) system. The intent of Lane Departure Warning systems is to warn
> drivers when they inadvertently drift out of their lane. In other
> words, the goal of a Lane Departure Warning system is to reduce the
> threat of drifting out of your lane into an adjacent lane, an opposing
> lane, or off of the road. The focus of this test is to evaluate two
> different versions of that LDW system, non-adaptive and adaptive.*

A chart, similar to Figure 11.20, and adapted for the LDW system, was
used as a visual aid. The researcher explained to participants:

> *In the non-adaptive mode, the LDW system provides all possible
> warnings to a driver, regardless of the driver's head-pose. In
> adaptive mode, however, the LDW system monitors head-pose and
> suppresses LDW alerts when the driver's head-pose is forward.*
>
> *So, looking at the examples (see Figure 11.20), in the non-adaptive
> mode, the driver receives an LDW alert each and every time an LDW
> threat is detected. In adaptive mode, when the driver's head-pose is
> very far to the left or very far to the right and the vehicle detects
> an inadvertent lane departure, the driver receives an LDW alert,
> however, when the driver's head-pose is forward, or straight-ahead, an
> assumption is made that their attention is forward, they are likely
> aware of the situation, and can be expected to respond appropriately.
> In those cases the warning is turned off, or suppressed. Finally, the
> LDW system has a minimum activation speed of 40 mph, so below that
> speed, the system is completely inactive.*

The researcher then asked the driver to look forward, as they would
while driving, and demonstrated the LDW alert three times for the
participant, pointing out the auditory, visual, and haptic components of
the warning. During the drive, the researcher provided turn-by-turn
directions to the drivers, as needed. At no time were drivers asked to
engage in a secondary task. The LDW route primarily covered three areas:
Indiana State Route 26, Interstates 65, 70, and 465 in and around
Indianapolis, IN, and U.S. Highway 31 (See Table 11.10, for a
description of the three areas).

***Table 11.10.*** On-Road LDW Testing: Route Description.

+---------------+------------+----------------+----+----+----+------+
|               |            | Encountered as |    |    |    |      |
|               |            | part of LDW    |    |    |    |      |
|               |            | Route          |    |    |    |      |
+---------------+------------+----------------+----+----+----+------+
| Location^1^   | D          | Roadway        | M  | Po |    | Tra  |
|               | escription | Type(s)        | il | st |    | ffic |
|               |            |                | es | ed |    | Li   |
|               |            |                |    | S  |    | ghts |
|               |            |                |    | pe |    |      |
|               |            |                |    | ed |    |      |
+---------------+------------+----------------+----+----+----+------+
|               |            |                |    | Mi | Ma |      |
|               |            |                |    | n. | x. |      |
+---------------+------------+----------------+----+----+----+------+
| Indiana State | Ru         | 2-lane rural   | 36 | 30 | 55 | 6    |
| Route 26      | ssiaville, | highway        |    | m  | m  |      |
|               | IN         |                |    | ph | ph |      |
|               |            | Bends,         |    |    |    |      |
|               | Sedalia,   | Straight       |    |    |    |      |
|               | IN         |                |    |    |    |      |
|               |            |                |    |    |    |      |
|               | Rossville, |                |    |    |    |      |
|               | IN         |                |    |    |    |      |
+---------------+------------+----------------+----+----+----+------+
| Interstate    | I-65       | 6-lane         | 79 | 55 | 70 | 0    |
|               |            | interstate     |    | m  | m  |      |
|               | I-70       |                |    | ph | ph |      |
|               |            | Rural,         |    |    |    |      |
|               | I-465      | Suburban, City |    |    |    |      |
|               |            |                |    |    |    |      |
|               |            | Bends,         |    |    |    |      |
|               |            | Straight       |    |    |    |      |
+---------------+------------+----------------+----+----+----+------+
| U.S. Highway  | Carmel, IN | 4 - 6 lane     | 40 | 45 | 60 | 26   |
| 31            |            | divided        |    | m  | m  |      |
|               | Westfield, | highway        |    | ph | ph |      |
|               | IN         |                |    |    |    |      |
|               |            | Straight       |    |    |    |      |
|               | Kokomo, IN |                |    |    |    |      |
+---------------+------------+----------------+----+----+----+------+
| ^             |            |                |    |    |    |      |
| 1^Description |            |                |    |    |    |      |
| represents    |            |                |    |    |    |      |
| complete LDW  |            |                |    |    |    |      |
| Route.        |            |                |    |    |    |      |
+---------------+------------+----------------+----+----+----+------+

Halfway through the drive, at approximately 70 mi for most drives, the
researcher switched the LDW system to the second mode, again reminded
drivers of the concepts (non-adaptive and adaptive) that were introduced
during training, and emphasized the distinction between the two halves.
Participants were generally more familiar with the State Route and
Highway than Interstate portions of the drive.

When a driver did receive an LDW alert, the researcher asked them to
report the direction of the alert, which, in all cases, matched the
researcher's perception. Due to circumstances unrelated to the study,
one drive was cut short and did not include the Interstate or Highway
portions of the route.

## 11.5.4. LDW Validation Results

Although drivers were strongly encouraged not to induce alerts, it
seemed especially tempting to test the LDW system, and in two different
instances drivers intentionally triggered an LDW alert by crossing into
the adjacent lane or onto the shoulder of the Interstate. Those two LDW
events were not included in the final analysis. While several drivers
interacted with their cell phones to turn off a ringer, for example, no
driver engaged in a cell phone conversation while driving.

***LDW Alert Rates***

In the non-adaptive and adaptive modes, participants experienced a total
of 85 LDW alerts, 81 and 4, respectively. The mean number of alerts
received by drivers was higher in the non-adaptive mode (*M* = 5.79,
*SD* = 5.12) than the adaptive mode (*M* = .29, *SD* = 0.62), *t*(26) =
3.99, *p* \< 0.01. The rate of suppression reported in Table 11.11 is an
approximate estimate based on the difference in the number of alerts
experienced in non-adaptive and adaptive modes for that portion of the
testing. It is evident that the variation in driving styles also
influenced the number of LDW alerts that drivers experienced. In part, a
variation in the driving styles contributed to drivers receiving a range
of total alerts between two and 19.

LDW alerts occurred at a higher rate per mile in non-adaptive (*M* =
.07, *SD* = 0.05) than in adaptive (*M* = .004, *SD* = .01), *t*(26) =
4.99, *p* \< 0.01. A summary of miles traveled, alerts, and suppression
rates are shown in Table 11.11. An examination of road type revealed
that the mean alert-rate per mile was not significantly different for
interstate (*M* = 0.04, *SD* = 0.06) as compared with highway (Route 26
and Highway 31 combined) (*M* = 0.03, *SD* = 0.02), *t*(25) = -.374, *p*
= 0.71. The researcher did, however, observe that LDW alerts generally
occurred more frequently while traveling through bends than on straight
portions of the roadway, independent of road type.

***Table 11.11.*** On-Road LDW Testing: Alert Rate, Total Miles, and
Suppression Rate.

  ------------------ -------------- ------------ ------------- ---------- --------------- ----------- --- ---------- ------------
  **Non-Adaptive**   **Adaptive**                **Overall**              **Rate of                                  
                                                                          Suppression**                              

  Indiana State\                    Miles                      288                        252             540        95%
  Route 26                                                                                                           

                                    Alerts                     21                         1               22         

                                    Mean Alerts                2.63                       0.14            1.47       

                                    Alerts per                 0.07                       0.004           0.04       
                                    Mile                                                                             

  Interstate                        Miles                      506                        521             1027       95%

                                    Alerts                     43                         2               45         

                                    Mean Alerts                3.31                       0.15            1.73       

                                    Alerts per                 0.08                       0.004           0.04       
                                    Mile                                                                             

  U.S. Highway 31                   Miles                      244                        280             524        94%

                                    Alerts                     17                         1               18         

                                    Mean Alerts                1.21                       0.07            0.64       

                                    Alerts per                 0.07                       0.004           0.03       
                                    Mile                                                                             

  **Overall**                       **Miles**                  **1038**                   **1053**        **2091**   **95%**

                                    **Alerts**                 **81**                     **4**           **85**     

                                    **Mean                     **5.79**                   **0.29**        **6.07**   
                                    Alerts**                                                                         

                                    **Alerts per               **0.07**                   **0.004**       **0.04**   
                                    Mile**                                                                           
  ------------------ -------------- ------------ ------------- ---------- --------------- ----------- --- ---------- ------------

Unless otherwise noted, all 14 drivers responded to each of the
following questions.

***LDW System and Adaptation Understandability***

Following completion of the drive, participants were asked to rate their
agreement with the following statement, "the distinction between
non-adaptive and adaptive modes was clear after a) training and b)
driving", on a 4-point Likert Scale. Respondents typically agreed that
the distinction was clear after both training (*M* = 3.79, *SD* = 0.43)
and driving (*M* = 3.79, *SD* = 0.43). None of 14 participants disagreed
with either of the two statements.

Drivers were then asked, "how would you explain the LDW system in detail
to someone who was unfamiliar it?" Again, some participants failed to
provide complete answers, but two out of the 14 responders provided
answers that did not meaningfully capture the critical attributes of the
system and were labeled as incorrect. One driver said the, "non-adaptive
system works by detecting where you are in the lane without compensating
for little corrections; \[and the\] adaptive system compensates for
little corrections..." A second participant answered that, in addition
to the lane departure warning, "a warning will sound as the driver is
distracted from paying attention to what is in front of the vehicle as
it is moving". A lack of task-engagement or insufficient training are
plausible explanations for these incorrect responses. Unlike the
respondents with incorrect answers in the FCW study, both of these
participants were engineers.

More drivers in the LDW study correctly answered this question than in
the FCW study. This is likely attributable to drivers' perception of the
LDW system as a more tangible concept, one that does not require a
consideration of variables like time headway, range, or deceleration
rate. In fact, after being told during training that the LDW system will
provide an alert when a driver has drifted out of their lane, one
participant quickly said, "I'll have to tell my wife they've automated
her!" However, the transparency of the LDW concept did not necessarily
translate to LDW events on the drive. Participants often commented that
they did not understand an LDW alert or did not perceive their vehicle
as being close enough to a lane marker to warrant the alert.

[]{.mark}

***LDW Preferred Mode***

When asked which mode of the LDW system they would prefer to use, more
drivers chose adaptive (n = 12), than non-adaptive (n = 2), χ^2^(1) =
7.14, *p* = 0.008. All 14 drivers received more alerts during the
non-adaptive portions of their drives than the adaptive. Of the twelve
drivers who expressed a preference for adaptive mode, almost all
reported the lower alert-rate as the foremost reason for choosing
adaptive mode. Similar to the FCW training, the pre-drive LDW
explanation likely contributed to their understanding and acceptance of
the lower alert-rate.

[]{.mark}

Drivers who preferred the non-adaptive mode offered a number of reasons.
One driver, who received three non-adaptive and zero adaptive warnings,
indicated that he appreciated the warnings and that they "helped me to
focus". He went on to say that receiving the warnings in non-adaptive
mode confirmed for him that the LDW "system was working and helping me"
and he did not consider the increased alert-rate to be a negative
attribute. A second driver, who received the same proportion of alerts,
said that the adaptive system "felt unresponsive" and the LDW alerts
were "acceptable". In addition to these two drivers, nine other drivers
experienced zero alerts in adaptive mode and several expressed concern
to the researcher during the drive that the system was not on or working
properly. One driver commented, "\[I\] did not even know it was there".
It seems that an extended absence of alerts, combined with a lack of
feedback about system status, might actually create mistrust of the LDW
system for some drivers.

As in the FCW study, this result is qualified by drivers' strong
tendency to cite alert-rate as their reason for preferring the adaptive
mode. Drivers also had a tendency to respond more to the behavior of the
LDW system than to the nuances of the adaptation strategy. Particularly
noticeable to drivers were anomalies in the LDW alerts, including alert
triggers such as seam lines in the pavement or tire skid marks on the
road surface. Interestingly though, none of the LDW participants
anticipated the problem of drivers receiving too many alerts overtime
and becoming unresponsive to them, a concern expressed by several FCW
drivers. This is unlikely due to the actual number of total alerts
experienced in LDW as compared to FCW, 85 and 83 respectively, since
they were so similar. Drivers may, however, have perceived the severity
of an FCW versus LDW threat differently. If the consequences of not
responding to an FCW alert are greater than that of an LDW, then any
degradation of reaction time to an FCW alert might have a far greater
negative impact.

***LDW Alert Timing***

On a 5-point Likert Scale, drivers reported their perception of the
timing of LDW alerts. Participants rated the adaptive mode (*M* = 3.11,
*SD* = 0.33) slightly closer to 'Just Right' than non-adaptive (*n* = 9,
*M* = 2.57, *SD* = 0.76) (see Figure 11.25). Five drivers who received
zero adaptive alerts, did not provide a response. The biggest difference
between the two modes was six drivers rating non-adaptive as 'Very
Early' or 'Somewhat Early', whereas adaptive did not receive any early
ratings.

![](media/image43.wmf){width="4.541666666666667in"
height="2.5618055555555554in"}

***Figure 11.25.*** LDW Alert Timing as Rated by Drivers.

***LDW Alert Behavior***

A 4-point agreement scale measured drivers' responses to, "the LDW
system behaved as I expected it to". Adaptive mode (n = 13, *M* = 3.69,
*SD* = 0.48) received a higher agreement rating than non-adaptive (*M* =
3.29, *SD* = 0.73) (see Figure 11.26). One driver who was the recipient
of zero alerts in adaptive did not provide a response for that mode. Two
drivers 'Somewhat Disagreed' that the non-adaptive mode behaved as they
had expected it to, while there was no 'Disagreement' with adaptive
mode. One driver who disagreed with non-adaptive, said that it, "alarmed
on a curve that I was taking normally and \[I was\] still within my lane
\[and it\] alarmed on a wide lane where I was not near the center line".
A second driver who provided the same response, wrote, "I didn't
perceive to be out of my lane or moving out of my lane when it went
off". Drivers who 'Somewhat Agreed' with the non-adaptive statement
offered similar sentiments: "there were a couple of times I didn't
believe I was drifting" and "I think the non-adaptive went off several
times, even though the car was not crossing over the lanes".

A point of confusion about the LDW system was evident in the following
response, the non-adaptive mode "alerted several times when switching
between lanes, but not having any issues (i.e. looking straight ahead,
no obstructions, etc)". It seems that this participant might have
confused the purpose of the LDW system with that of a blind-spot
detection system, a problem evident in an earlier answer from this
driver.

A second misconception about the intention of the LDW system also
influenced a driver's response to this question. One driver, who
responded 'Somewhat Agree' to adaptive, commented, "I enjoy driving and
seeing things I haven't noticed or something new, I thought for sure the
system would catch me". As evidenced in a prior response, this driver
failed to realize that head pose is only relevant during lane departure
events.

One driver who 'Somewhat Agreed' in the adaptive mode commented, "in one
case I thought I was looking ahead and it went off when car drifted
left. I was OK with the alarm, my eyes were partially focused on vehicle
to my right and ahead". This comment seems to imply a possible lack of
distinction between eye gaze and head pose. More comprehensive pre-drive
training might have prevented errors like these. As is also strongly
evidenced by these comments, it can be challenging for drivers to
consider the nuances of adaptation without focusing on the behavior of
LDW system itself.

***LDW Acceptability of Alert Frequency***

Participants rated their agreement with, "the frequency of LDW alerts
would be acceptable to me, in my own car, during everyday driving"
higher in adaptive (*M* = 3.57, *SD* = .65) than in non-adaptive (*M* =
2.07, *SD* = .83) (see Figure 11.26). While only one driver in adaptive
disagreed, 11 drivers disagreed with the non-adaptive statement. This
ratio follows a similar pattern in the FCW testing. Across warning
systems, alert-frequency appears to be one of the most salient
attributes to drivers.

***LDW Perception of Nuisance Alerts***

Participants indicated what percentage of LDW alerts they classified as
nuisance alerts. While drivers responded with a full range of answers in
non-adaptive, all 14 participants selected "0 -- 24%" in adaptive mode
(see Figure 11.26). This result seems to be driven by individual
perception of what constitutes a nuisance alert, in addition to the
number of alerts in each mode. Whereas in non-adaptive, the number of
alerts ranged from two to 19, adaptive mode alerts ranged from zero to
two, with 11 drivers receiving zero adaptive alerts.

***LDW Acceptability of Nuisance Alert Frequency***

Drivers were asked to rate their agreement with the statement, "the
frequency of nuisance LDW alerts would be acceptable to me, in my own
car, during everyday driving", on a 4-point Likert Scale. In accordance
with the results from the previous question, drivers reported a higher
level of agreement in adaptive (*M* = 3.36, *SD* = .74), than in
comparison with non-adaptive (*M* = 1.86, *SD* = .77) (see Figure
11.26). Respondents were equally split between agree and disagree in
adaptive mode, while only three of the 14 drivers agreed with the
statement in response to the non-adaptive question.

***LDW On-Road Safety***

Participants rated their agreement with, "the LDW system enhances
on-road safety". Most respondents agreed, with the level of agreement
being slightly lower in non-adaptive (*M* = 3.21, *SD* = .89) than in
adaptive (*M* = 3.79, *SD* = .43) (see Figure 11.26). Two participants
did not agree that the non-adaptive mode enhanced safety. Based on
comments provided in other questions, the higher level of agreement in
adaptive is thought to correspond to a lower nuisance alert-rate.

![](media/image44.wmf){width="2.9993055555555554in"
height="1.8777777777777778in"}

![](media/image45.wmf){width="3.0in" height="1.8666666666666667in"}

![](media/image46.wmf){width="2.9993055555555554in"
height="1.8694444444444445in"}

![](media/image47.wmf){width="2.9993055555555554in"
height="1.8722222222222222in"}

![](media/image48.wmf){width="2.9993055555555554in"
height="1.8722222222222222in"}![](media/image49.wmf){width="2.9993055555555554in"
height="1.8694444444444445in"}![](media/image50.wmf){width="2.9993055555555554in"
height="1.8722222222222222in"}

![](media/image51.wmf){width="2.998611111111111in" height="1.875in"}

***LDW Alert Distraction***

As shown in Figure 11.26, a higher nuisance alert-rate in non-adaptive
is also thought to have influenced drivers' response to, "the LDW system
was distracting". Non-adaptive mode (*M* = 2.14, *SD* = .95) received a
higher agreement rating than adaptive mode (*M* = 1.21, *SD* = .43),
indicating that participants regarded the system as more distracting in
the non-adaptive mode.

***LDW Likelihood to Recommend***

In an effort to measure drivers' overall experiences with each mode,
participants were asked to rate their level of agreement with the
statement, "I would recommend the LDW system to other drivers". The
results revealed that drivers had a slightly higher level of agreement
with adaptive mode (*M* = 3.50, *SD* = .52) than they did with
non-adaptive mode (*M* = 2.71, *SD* = .91) (see Figure 11.26). Four
drivers disagreed that they would recommend the non-adaptive system,
while all 14 respondents agreed with the adaptive mode. One participant
said he could not recommend the non-adaptive mode because of the need to
"reduce/eliminate false alarms". Other drivers said, "I wouldn't have it
go off quite as early", "change \[it\] to be more like the adaptive
system", and "nuisance warnings were a bother." After agreeing to
recommend the adaptive mode, one driver wrote, "I would make \[the\]
adaptive feature on/off. If driving tired or in bad weather I may want
the earlier/more frequent warning".

***LDW HMI***

Drivers were asked several questions about their HMI preferences. The
response to the first question, "of the three types of LDW alerts that
you experienced, which one was best at capturing your attention?" was
unanimous. All 14 participants agreed that the 'haptic' alert was best
at capturing their attention (see Figure 11.27).

The next question, "which of the three LDW alerts was most annoying",
yielded less decisive results. Results were distributed across the three
warning types, χ^2^(2, *n* = 14) = 1.00, *p* \< .61, with 'audio' being
rated as most annoying and 'haptic' as the least (see Figure 11.27). One
driver, whose mode preference was non-adaptive, said that he chose
'visual' as most annoying because "it didn't get my attention as well as
the other two". A second driver said that he found himself "focusing on
the light", while in non-adaptive mode, but ultimately appreciated the
purpose of an exogenous display for distracted drivers in adaptive mode.
Another driver agreed and said that since the exogenous display is
"right in \[the driver's\] line of sight, it felt distracting".
Observations like these may have also contributed to drivers' overall
preference for the adaptive mode, across both studies.

![](media/image52.wmf){width="4.802083333333333in"
height="2.7597222222222224in"}

***Figure 11.27.** LDW HMI Ratings.*

Drivers had a unique reaction to the haptic seat as a warning device.
The researcher observed that many drivers were surprised when this
particular warning method was introduced during the demonstration and it
almost always prompted a remark from drivers. Several participants
commented about the novelty of a vibrating seat in a vehicle, since
auditory alarms and displays have become so ubiquitous. Upon receiving
the demonstration, one driver announced the haptic warning was "like a
Disney World-experience". All drivers found the seat in combination with
the directional component of the warning, to be salient throughout the
drive.

The next series of HMI questions asked, "If you were to receive an LDW
alert while you were a) attentive, b) distracted, and c) fatigued, which
combination of the two warnings would you prefer?" Responses reflected
drivers' strong impressions of the haptic seat. Participants' selections
were nearly evenly distributed between 'Visual and Haptic' and 'Audio
and Haptic'. Only one participant chose the combination that did not
include the haptic component, 'Audio and Visual'. This participant later
commented that removing the "haptic warning might make \[the warning
system\] more acceptable". For distracted drivers, participants had a
clear preference for the 'Haptic and Audio' combination, with only
driver selecting a different combination, 'Visual and Haptic'. Following
a similar pattern for fatigued operators, drivers again expressed a
strong preference for the haptic seat paired with the auditory warning,
with all 14 participants selecting this same combination (see Figure
11.28 for a summary of HMI preferences). There is a noticeable contrast
between the LDW HMI responses and those in the FCW study. Drivers in the
previous study reported a strong preference toward 'Audio and Visual'
for distracted and fatigued drivers, while none of the LDW drivers
selected this combination for either state. It appears then that when
the haptic seat is introduced as a third warning alternative, nearly all
participants viewed it as an especially effective warning method.

![](media/image53.wmf){width="4.8125in" height="2.561111111111111in"}

***Figure 11.28.** LDW HMI Preferences.*

A second explanation for why LDW drivers, in comparison to FCW drivers,
did not express concern about operators becoming too tolerant of
frequent alerts (see LDW Preferred Mode), might be the addition of the
haptic component of the warning. Drivers viewed the seat vibration as a
unique warning method, the best for attention capture, but also the
least annoying of the three different warnings. While it seems that
these attributes might have helped to resolve concerns about the warning
lacking saliency over time, long-term exposure is required to validate
these initial opinions. Finally, as was the case in the FCW study,
several drivers commented that the cover of the exogenous display was
noticeably reflective in the windshield, particularly on tree-lined
streets in sunny weather.

## 11.5.5. Discussion

After establishing the final adaptation strategies and HMI through the
process of iterative testing and development, the data fusion task
conducted an evaluation of the optimized SAVE-IT configuration in the
context of real roadways. The final adaptation strategies and HMI were
the focus of 28 on-road drives wherein drivers experienced either the
FCW or LDW systems. These on-road drives afforded each participant a
chance to contrast the non-adaptive mode with SAVE-IT adaptation, and
resulted in a number of dependent measures, across two studies.

It was anticipated that drivers would prefer the SAVE-IT adaptation to
the non-adaptive mode, across both warning systems. Drivers were
expected to receive fewer alerts in adaptive, as compared to the
non-adaptive mode, and to perceive the lower alert-rate as the more
acceptable alternative. In addition to a lower frequency of alerts,
drivers were expected to appreciate that adaptation was a function of
head pose, and that the timing of the FCW alerts and the suppression of
the LDW alerts were a direct result of adaptation. Subjective measures
were intended to capture drivers' perceptions of these attributes. It
was expected that drivers would not only rate the adaptive version of
each warning system as more acceptable and less annoying, but would
appreciate that adaptive warnings were generated when the operator had a
greater need for them. Inherent in these expectations is the assumption
that drivers would be able to evaluate the adaptive strategies
independent of the of the warning system behavior itself.

Objective results showed strong support for the SAVE-IT adaptation in
terms of a reduction in overall alert-frequency. Drivers received
significantly fewer alerts, and fewer alerts per mile, in the adaptive
mode of both the FCW and LDW studies. The FCW and LDW warning systems
both experienced high suppression rates, approximately 70% and 95%
respectively. Significantly more drivers expressed a preference for
adaptive mode in both studies, and when they did so, the most popular
reason provided was the lower alert-rate, a result common to both
studies. It appears that the difference in alert frequency between modes
was the most salient aspect of both systems, and it directly influenced
several subjective ratings. The trend across both warning systems was
for drivers to report that the adaptive warning system was more
acceptable, had fewer nuisance alerts, and was less distracting. It is
likely that the pre-drive training also contributed to drivers'
preference and acceptance of the lower, adaptive, alert-rate.

Despite the strong preference for adaptive mode, the behavior of the
adaptation strategies and the warning systems were not as transparent to
drivers as anticipated. Several drivers expressed uncertainty about the
behavior of the FCW system to the researcher during the drive. Although
participants easily identified the target of FCW alerts, it was common
for them to question why an alert had been generated when no threat of a
forward collision was perceived. Upon further discussion, it was clear
that, in at least a portion of these cases, drivers failed to consider
the criteria for generating an alert (i.e., inputs to the radar: vehicle
range, lead vehicle deceleration, and host speed), instead, basing their
decision on their knowledge of lead vehicle's intent (e.g.,
turning-out-of-path) or their own intent (e.g., quickly decelerating or
changing lanes). From their perspective, an FCW alert seemed unwarranted
if they knew the lead vehicle was turning or if they intended to depart
their lane, regardless of range or speed at that particular moment. This
proved to be especially problematic, not only in terms of understanding
the behavior of the FCW system, but also for assessing the adaptation
strategy. In these cases, drivers found it especially difficult to
overcome their concerns about the validity of the alert and to consider
attributes associated with adaptation (e.g., alert timing, head pose).
At times, their contention that the alert was a nuisance warning,
superseded a comparison of non-adaptive and adaptive modes. Similar
examples occurred in the LDW testing too. However, understanding the LDW
concept was far less problematic for drivers, likely because as a less
tangible concept, FCW requires the consideration of a multitude of
factors, like time headway, thresholds, and the behavior of other
vehicles, whereas lane position is the single variable in the LDW
system. In addition, when the study takes place in such a dynamic,
naturalistic environment and no two events are ever identical, gaining
an understanding of a warning system is especially difficult. While
drivers still questioned, and sometimes disagreed with LDW alerts, the
criteria for an alert was much more tangible. It seems then, that for an
effective assessment of the FCW and LDW adaptation strategies, it is
important for drivers to not only understand the concept of adaptation,
but also to understand how and why alerts are generated by the warning
system. It is apparent in the FCW and LDW studies that, had drivers been
able to isolate the behavior of adaptation from that of the warning
systems themselves, more consideration and concentration could have been
given to head pose, alert timing (FCW), and alert suppression (LDW). It
is clear then that the positive effects of adaptation come from the
reduced frequency of alerts overtime, rather than interpretation of
individual alerts.

Further, when asked to provide explanations on the post-drive
questionnaire, several drivers submitted incorrect answers about both
the intent of the warning systems and the purpose of adaptation. While
this is likely a result of a lack of motivation and engagement with the
task, it also suggests an opportunity to improve upon the current
training method. Training might be enhanced by providing visual examples
of what constitutes a 'lane departure' or an 'imminent forward
collision', as well as an explanation about the factors used in
assessing an FCW threat, like range, deceleration, and speed. Providing
drivers with more examples of forward and non-forward head poses might
better communicate what seem like abstract boundaries to some drivers.
Finally, giving participants the chance to interact with the warning
systems on a closed course or for a longer duration might provide an
outlet for their curiosity, and more importantly, give them the
opportunity form a realistic set of expectations about the behavior of
the warnings, so the focus of on-road driving can truly be an assessment
of adaptation.

Future studies might provide drivers with real time feedback about
alerts that are suppressed in adaptive mode or those that would have
been suppressed if adaptation had been active in order to aid in a
comparison of the two modes. An opportunity to test on a closed track
might help to eliminate some of the constraints of on-road testing,
providing more frequent and controlled exposure, including distracted
states, to FCW and LDW events. This type of testing was conducted as a
portion of the Task 14 UMTRI evaluation and the Dana Test Track.

# 11.6. CONCLUSIONS AND SAVE-IT IMPLEMENTATION SUMMARY

Due to the overriding need to create simple interfaces that the driver
would be able to comprehend, the most effective method for refining the
countermeasures was to expose them to real on-road conditions and
observe the associated behavior of the systems and the resulting driver
responses. In driving simulators, the countermeasure tasks (Task 4:
Distraction Mitigation and Task 9: Safety Warning Countermeasures) had
investigated the effectiveness of the various countermeasures for
improving safety. As a result of the observed effectiveness at improving
safety in these tasks, some countermeasure strategies had already been
abandoned prior to the data fusion task and other countermeasure
strategies had been refined to differing extents. The primary need for
data fusion was to examine the interaction between the countermeasures
and the real world. For example, it was not yet clear what kinds of FCW
alert rate would result from the differential timing strategy or what
kinds of alert rates would result from the real-time distraction alerts.
Furthermore, the driving simulator experiments were not an ideal venue
for examining the acceptance of the different adaptive interfaces. Based
on these needs, it was clear that the most important aspect of the data
fusion task was to expose the SAVE-IT systems to real roadways. Based on
the observed rates of interventions and the resulting driver acceptance
and comments, the countermeasures were iteratively refined. The
following final set of countermeasures emerged from the data fusion task
and will be evaluated in Task 14 (evaluation):

[Distraction Mitigation]{.underline}

1.  *Adaptive Infotainment Availability and Advisory*

2.  *Adaptive Phone Management*

3.  *Trip report*

[Adaptive Warnings]{.underline}

4.  *Adaptive Forward Collision Warning*

5.  *Adaptive Lane Departure Warning*

```{=html}
<!-- -->
```
1.  *[Adaptive Infotainment Availability and Advisory.]{.underline}* As
    driving task demand increases, drivers are advised to shed more IVIS
    functions in order to concentrate on the driving task itself[^3].
    Table 11.12 lists the advisories' logic for the IVIS functions. The
    advisories are signaled by coloring the text of the buttons
    responsible for that function in an amber color that was dimmer than
    the normal color used for that text (see Figures 11.29 and 11.30).
    In addition to the color change of the button, the "Use Cautiously"
    text appeared just under the page heading to help communicate to the
    driver the intention of the adaptation. As the driving task demand
    gets to greater levels, some of the most demanding IVIS features are
    locked-out. Initially it was expected that the lock-out of features
    had the potential of being too annoying or too confusing, however
    the Task 4 results seemed to indicate that lock-out might be a
    potentially useful distraction-mitigation strategy if it could be
    implemented in a way that clearly communicates the intention to the
    driver. By using the gray-out effect that is now common in computer
    GUIs (graphical user interfaces), and clearly tying it to the text
    just below the page description, driver-confusion may be avoided.
    Whereas the Task 11 on-road testing provides a preliminary
    indication that drivers understand this mitigation strategy, Task 14
    (evaluation) will specifically test whether this strategy is likely
    to have some counter-intuitive negative impacts on safety.

***Table 11.12.*** Driving task demand-based advisories and lock-outs of
IVIS functions

  -------------------------- ------------ ------------ ------------ ------------
  **IVIS Task**              **Driving                              
                             Task                                   
                             Demand**                               

                             **In park**  **Low**      **Medium**   **High**

  Radio Tuning               No           No           No           No
  ([r]{.underline}=.65)      Advisories   Advisories   Advisories   Advisories

  Satellite Radio (r=.72)    No           No           No           Advisories
                             Advisories   Advisories   Advisories   

  CD (r=.65)                 No           No           No           Advisories
                             Advisories   Advisories   Advisories   

  MP3 (r=.75)                No           No           Advisories   Advisories
                             Advisories   Advisories                

  Phone Dialing (r=.72)      No           No           Advisories   Lock-out
                             Advisories   Advisories                

  Answering Phone (r=.79)    No           Adaptive                  
                             Advisories   Phone                     
                                          Management                

  Phone Conversation (r=.72) No                                     
                             Advisories                             

  Navigation POI (r=.70)     No           No           Advisories   Lock-out
                             Advisories   Advisories                

  Navigation Map Reading     No           No           Advisories   Lock-out
  (r=.79)                    Advisories   Advisories                

  Navigation Turn-by-turn    No           No           Advisories   Advisories
  (r=.61)                    Advisories   Advisories                

  Text Messaging (r=.71)     No           Advisories   Lock-out     Lock-out
                             Advisories                             
  -------------------------- ------------ ------------ ------------ ------------

![](media/image54.png){width="6.5625in" height="3.1618055555555555in"}

***Figure 11.29.** Task demand-based advisory and lockout for IVIS
features.*

2.  *[Adaptive Phone Management]{.underline}*. Task 4 showed that
    driver-initiated mitigation methods are more acceptable to drivers
    than system-initiated methods. The distraction mitigation
    preparation portion of data fusion demonstrated that the perceived
    risk associated with phone functions increases with driving task
    demand. In order to optimize driver acceptance, drivers will be
    given three choices for phone screening: no screening,
    do-not-disturb and automatic screening (see Figure 11.31). If the
    driver selects automatic screening, the SAVE-IT system will
    determine whether a call should be routed to driver or to voice mail
    based on the concurrent driving task demand.

![](media/image55.png){width="3.192361111111111in"
height="2.3847222222222224in"}
![](media/image56.png){width="3.154166666666667in"
height="2.3847222222222224in"}

***Figure 11.30.** Task demand-based advisory (low demand) and lockout
(medium demand) for creating text messages.*

![](media/image57.png){width="1.9305555555555556in" height="1.05in"}
![](media/image58.png){width="1.9430555555555555in" height="1.05in"}
![](media/image59.png){width="1.9430555555555555in" height="1.03125in"}

***Figure 11.31.** The three driver-selected modes of phone management.
Only the auto mode (right) is responsive to the instantaneous assessment
of driving-task demand.*

3.  *[Trip report]{.underline}.* In order to mitigate additional
    distraction by providing the driver with feedback in real time, a
    delayed feedback mechanism will be implemented in the form of a trip
    report. This report will be presented to the driver at the end of
    the drive. Drivers can review these reports and possibly modify
    their driving behavior on subsequent drives. The format of the trip
    report is displayed in Figure 11.32 and includes the driver's
    percentage of forward head-pose overlaid on the trip route and an
    overall average attention level for the trip. This information is
    color coded to make it easier to interpret. The trip report also
    displays the number and location of FCW and LDW alerts. Because in
    production the trip report would ultimately be a voluntary
    countermeasure, in an attempt to trigger the driver's curiosity, the
    trip report also displays the number of miles driven and the gas
    mileage for the trip.

![](media/image60.png){width="6.678346456692913in"
height="5.010236220472441in"}![](media/image61.png){width="0.2736220472440945in"
height="0.347244094488189in"}![](media/image62.jpeg){width="0.4744094488188976in"
height="0.3362204724409449in"}![](media/image62.jpeg){width="0.5098425196850394in"
height="0.36023622047244097in"}

***Figure 11.32.** The trip report format displayed to drivers at the
conclusion of the trip, displaying the color-coded percentage of head
pose forward for the trip, and the number of FCW and LDW alerts.*

4.  *[Adaptive Forward Collision Warning]{.underline}.* Table 11.13
    displays the final system configuration for Adaptive FCW that was
    tested in Task 14 (Evaluation). After much research in Task 9 and
    some on-road acceptance testing in Task 11, the Differential Alert
    Timing strategy appeared to be the most promising. In this
    particular implementation, the timing adjustment for the forward
    attention state was quite extreme (assumed reaction time of 0.5 s)
    in order to suppress almost all alerts when the drivers head pose
    was forward. This adaptation method was selected because it at least
    provides some coverage for driver's who are visually oriented to the
    forward scene but might benefit from an alert for some other reason
    (e.g., drowsiness or cognitive distraction). When the driver's head
    pose is not forward, the driver will experience very early alerts
    that allow for a long driver brake reaction time. For a more
    complete description of the final FCW configuration, see Section
    11.4.

![](media/image21.wmf){width="6.0in"
height="2.703472222222222in"}***Table 11.13.** Adaptive and Non-Adaptive
mode Forward Collision Warning as a Function of Driver Head Pose.*

5.  The final form of adaptation that was selected for LDW was the
    suppression strategy (see Table 11.14). This strategy was selected
    because several drivers experienced confusion with the differential
    alert stimuli strategy (suppression of the auditory warning
    stimulus; see Task 9) because the interface lacked consistency. Due
    to the fact that cognitive distraction and brief or small-angle
    visual distractions do not appear to significantly degrade
    lane-keeping performance (see Tasks 5 and 9), LDW provides little
    safety benefit for visually-attentive drivers[^4]. For a more
    complete description of the final LDW configuration, see Section
    11.4.

![](media/image22.wmf){width="4.9847222222222225in"
height="2.2645833333333334in"}***Table 11.14.** Adaptive and
Non-Adaptive mode Lane Departure Warning as a Function of Driver Head
Pose.*

The final SAVE-IT configuration also included a proximity alert that
simply displayed a steady amber exogenous alert when the time headway to
the lead vehicle fell below 0.5 s. Because this feature was not an
adaptive interface, it was not a major focus of the Task 14 evaluation
task. Furthermore, although the distraction alert (also utilizing the
amber exogenous display) has been implemented in the SAVE-IT platforms,
due to unresolved driver acceptance issues and the potential for
distracting the driver, this feature will be disabled during the
majority of the Task 14 evaluation testing. As it is implemented, the
distraction alert flashes the amber exogenous display when the driver's
head pose is away from forward for a prolonged period of time that
varies as a function of the headway to the lead vehicle. The other five
countermeasures described above became the focus of the evaluations at
the National Advanced Driving Simulator (NADS), on the Dana Test Track
by the University of Michigan Transportation Research Institute (UMTRI),
and at Ford's VIRTTEX (VIRtual Test Track EXperiment) driving simulator.
The results of these evaluations will be presented in separate SAVE-IT
reports.

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[^1]: The exception to this is that various forms of driver impairment
    (e.g., drowsiness) also degrade lane-keeping performance. A
    comprehensive system should also be capable of monitoring and
    providing countermeasures for driver impairment. Driver impairment
    is beyond the scope of the SAVE-IT program and so the driver will be
    assumed to be in a non-impaired state.

[^2]: In actuality, the adaptive FCW system does provide an alert,
    although later, to drivers with a forward head pose. Given the
    simplified training method this study employed, the participant was
    not exposed to this level of detail about the timing suppression
    aspect of the adaptation strategy.

[^3]: The advised-against functions are not locked-out and remain
    functional during advisory.

[^4]: The exception to this is that various forms of driver impairment
    (e.g., drowsiness) are known to degrade lane-keeping performance. A
    comprehensive system should also be capable of monitoring and
    providing countermeasures for driver impairment. Driver impairment
    is beyond the scope of the SAVE-IT program and so the driver will be
    assumed to be in a non-impaired state.