A Final Report of

SAfety VEhicles using adaptive Interface Technology (Phase II: Task 7C):

Visual Distraction

Prepared by

Harry Zhang

Matthew Smith

Robert Dufour

Delphi Electronics and Safety

Phone: (765) 451-9816

Email: [matt.smith@delphi](mailto:john-d-lee@uiowa.edu) com

February 2008

Table of Contents

[7.0 EXECUTIVE SUMMARY 3](#executive-summary)

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

[7.2. Introduction 7](#introduction)

[7.2. Method 10](#method)

[7.2.1. Subjects 10](#subjects)

[7.2.2. Design 10](#design)

[7.2.3. Procedure 13](#procedure)

[7.3. Results 15](#results)

[7.3.1. Variables 15](#variables)

[7.3.2. ANOVA and Correlation Results
17](#anova-and-correlation-results)

[7.3.3. Subject-by-Subject Analyses 28](#subject-by-subject-analyses)

[7.3.4. Across-Conditions Analyses 32](#across-conditions-analyses)

[7.4. Discussion 37](#discussion)

[7.5. References 41](#references)

#   {#section .unnumbered}

# 7.0 EXECUTIVE SUMMARY

The objective of Task 7 was to investigate how visual distraction
impacts driving performance. Drivers have the ability to allocate their
attention to various activities besides driving. Their attention to the
driving task fluctuates as a function of the demand of the road and the
demands of competing tasks, which distract from the main task. Given
that drivers alternate between attentive driving and distracted driving,
safety systems dedicated to Distraction Mitigation will require
monitoring of the driver state. One of the goals of this research is to
determine how drivers allocate their visual attention from one point of
focus to another and to isolate a few measures which could be used to
inform various countermeasure systems that the driver's attention is
away from the main driving task.

In Phase I, the research focused on determining the effects of visual
distraction on drivers' reaction times and driving performance. Two
simulator experiments revealed several real-time eye-glance measures
that correlate highly with accelerator release reaction time (ART) and
standard deviation of lane position (SDLP). Two such measures, Eye Gaze
Variability and Eye Gaze Vector (calculated over a time window), require
precise eye gaze measurements that are not readily available in
automotive-grade eye tracking systems. Given this limitation, these
measures will not be pursued further. A third measure, Proportion of
Eyes-Off-Road Glance Time, is highly correlated with driving performance
(ART and SDLP) and can be measured by automotive-grade eye tracking
systems. The eyes-off-road proportion is calculated as the ratio of
total eyes-off-road glance time over a time window (e.g., 5 seconds).
Phase I research yielded strong correlations between eyes-off-road
proportion and driving performance measures. However, these correlations
were computed using across-subjects data summation due to the rarity of
the target events (i.e., braking to avoid collision with a lead
vehicle). For real-time implementation of such a system, the research
has to show that the strong correlations between these measures hold for
most individual drivers.

Phase II focused on demonstrating that eyes-off-road proportion is a
reliable within-subject measure that can be correlated with individual
driving performance. The new experiment consisted of two 3-hour sessions
and generated as many as sixteen target events for each of the subjects
tested. Three other issues were also investigated: Finding threshold
values for eyes-off-road proportion, exploring other diagnostic measures
and further investigating display eccentricity effect (i.e., how
distance away from the optical focus of expansion leads to poorer
driving performance).

***Method***

Twelve middle-aged subjects were tested in twelve different distraction
conditions. The primary task consisted of following a lead vehicle while
the secondary task (distraction task) consisted of reading a varied
number of words aloud. The words were presented in either the
forward[^1] location (five conditions) simulating information processing
from the environment (e.g., street sign or license plate reading) or the
words were presented in common areas that may require visual attention:
high-mounted cluster, console, rearview mirror, and left and right
mirrors. Figure 7.1 illustrates the display locations. The words were
changed every five seconds, an auditory beep alerted subjects to the
change in display. The console display had three distinct conditions: 1,
3, or 5 words. A baseline condition with no distraction task completed
the experimental design.

![](media/image1.wmf){width="2.25in" height="1.6875in"}

***Analysis & Results***

As indicated by the NASA Task Load Index measure of task difficulty
(NASA-TLX), driving while performing a secondary task was more difficult
than baseline (condition 1) (Figure 7.2). The distraction conditions
with forward and high-mount cluster displays were rated as equally
difficult. The remaining displays were rated as equally difficult to one
another, but were rated as more difficult than the high-mount cluster
display showing that subjects experienced display eccentricity effect
when reporting on task difficulty. Finally, the console display
(5-words) (condition 12) was rated as the most difficult. To sum up, it
was more difficult to complete the primary task of driving if the
secondary task required that the visual attention be shifted further
away from the focus of expansion and if it required more or longer
look-away (1-word\< 3-words \< 5-words).

Given that the eyes-off-road proportion measure can be calculated using
any time window, a large number of time windows ranging from one to
thirty seconds were computed. All generated similar results and a
computationally efficient time window of 4.3 seconds was implemented for
all analyses. As Figure 7.2 shows there was a clear effect of display
location on the eyes-off-road proportion. The non-forward displays (8, 9
& 11) generated greater eyes-off-road proportion than forward displays
(2-7). This difference between forward and non-forward displays
illustrates the effect of display eccentricity. Increasing the number of
words displayed on the console (10-12) also yielded greater
eyes-off-road proportion. Distraction presented in the mirror locations
and console generated eyes-off-road proportion of 0.5, whereas the
console presentation of 5 words (12) generated a proportion of 0.62 and
was rated as most difficult. These findings suggest that a threshold of
0.5 be used for determining that visual distraction is occurring.
Furthermore, these results show that this measure does relate to visual
distraction in a manner that can be quantified.

As Figure 7.3 illustrates, the pattern of results found for standard
deviation of lane position (SDLP) was similar to the one described for
eyes-off-road proportion. The non-forward displays (8, 9 & 11) generated
greater SDLP than forward displays (2-7). This shared pattern by the two
measures suggests that they are associated to one another. The high
correlation coefficient ([r]{.underline} = .843) supports this
conclusion. As the eyes-off-road proportion increased so did SDLP.

![](media/image2.wmf){width="6.395833333333333in"
height="3.134027777777778in"}

![](media/image3.wmf){width="6.467361111111111in" height="3.125in"}

***Figure 7.3.** Eyes-off-road Proportion and SDLP as a function of
Distraction*

The results for Accelerator Release Reaction Time (ART) were not as
clear. There was a strong correlation between eyes-off-road proportion
and ART ([r]{.underline} = .869) if the data for forward displays (2-6)
are removed from the analysis. The reason for this finding may have to
do with the manner by which the distraction task was presented in the
forward displays. Otherwise, as the eyes-off-road proportion increased
so did ART.

To summarize, the results indicate that the measure of Visual
Distraction (eyes-off-road proportion) does correspond to driving
performance associated with distracted driving (e.g., lane departure). A
key goal of this research was to show that this finding applies to most
subjects. The within-subject analysis yielded correlation between
eyes-off-road proportion and SDLP ([r]{.underline} = .857) and 11 of 12
subjects had relatively high correlation coefficients ([r]{.underline}
\> .5). This finding indicates that eyes-off-road proportion is a
reasonable diagnostic of visual distraction for most individuals.

Another key goal of this research was to investigate alternative
diagnostic measures. Two such measures did emerge: Eye gaze gamma is
based on the instantaneous eyes-off-road glance duration at the moment
of lead vehicle braking and proportion of head-off-road glance time
which is a corollary measure related to eyes-off-road proportion and is
the ratio of total head-off-road glance time over a time window. Both
these measures yielded strong correlation with SDLP
([r]{.underline}~egg~ =.922 and [r]{.underline}~horp~ = .841) and
reliable within-subjects correlations (11 of 12 and 7 of 12 subjects had
[r]{.underline} \> .5). Head-based measures are advantageous as they are
easier to measure with automotive-grade eye tracking equipment.

***Conclusions***

Reliable measures of visual distraction can be linked to
distracted-driving and can be used to inform safety countermeasure
sub-systems. A short time window (4.3 seconds) was used and it yielded
timely and reliable information as to the state of the driver. However,
other time windows could be implemented. Although the eye-based measures
have slightly higher correlations with the performance measures than the
head-based measures, the sensors that are required to sense head pose
are more robust and more affordable than the sensors required to measure
eye gaze. Furthermore, the more severe distractions, that last for a
longer duration or are directed further away from forward, are more
likely to involve a head pose component. Given some of the limitations
associated with eye-based measures (e.g., eyes sometimes occluded by
eyewear), optimizing a head-based measure should become a goal.

# 7.1. PROGRAM OVERVIEW

Driver distraction is a major contributing factor to automobile crashes.
National Highway Traffic Safety Administration (NHTSA) has estimated
that approximately 25% of crashes are attributed to driver distraction
and inattention (Wang, Knipling, & Goodman, 1996). The issue of driver
distraction may become worse in the next few years because more
electronic devices (e.g., cell phones, navigation systems, wireless
Internet and email devices) are brought into vehicles that can
potentially create more distraction. 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
Delphi Electronics & Safety to develop, demonstrate, and evaluate the
potential safety benefits of adaptive interface technologies that manage
the information from various in-vehicle systems based on real-time
monitoring of the roadway conditions and the driver\'s capabilities. 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 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, not only include sophisticated
telematics, mobile office, entertainment and safety warning systems, but
also incorporate the state of the driver. This revolutionary closed-loop
vehicle environment will be achieved by measuring the driver's state,
assessing the situational threat, prioritizing information presentation,
providing adaptive countermeasures to minimize distraction, and
optimizing advanced collision warning.

# 7.2. Introduction

In Phase I research of the SAVE-IT program, two simulator experiments
have been performed to determine the effects of visual distraction on
reaction times and driving performance (Zhang & Smith, 2004a, 2004b;
Zhang, Smith, & Witt, 2006). Several real-time eye glance measures have
been discovered to correlate highly with accelerator release reaction
time and standard deviation of lane position. Two potential measures
include eye gaze variability and eye gaze vector that are calculated
over a time window (e.g., 5 seconds). Eye gaze variability is the
product of the standard deviations of gaze yaw angle and pitch angle
multiplied by 4. It is used in Recarte and Nunes (2000). Eye gaze vector
reflects the distance of eye gaze from the focus of expansion and is the
square root of the sum of squared gaze yaw angle and pitch angle. These
two measures have been shown to correlate strongly with accelerator
release reaction time and SDLP using a large window (e.g., 30 or 60
seconds) or a small window (e.g., 1 second). These measures require
precise measurements of eye gaze. Because precise measurements of eye
gaze are not readily available in automotive-grade eye tracking systems
such as Delphi\'s Driver State Monitor (Edenborough, Hammoud, Harbach,
Ingold, Kisačanin, Malawey, Newman, Scharenbroch, Skiver, Smith,
Wilhelm, Witt, Yoder, & Zhang, 2004), eye gaze variability and eye gaze
vector will not be implemented and evaluated in Task 14 of the SAVE-IT
program.

Another diagnostic measure of visual distraction is the proportion of
eyes-off-road glance time that is calculated over a time window (e.g., 5
seconds). High correlations have been reliably acquired between
eyes-off-road proportion and accelerator release reaction time or SDLP
using either a small time window (e.g., 1 second) or a large time window
(e.g., 30 or 60 seconds). The eyes-off-road proportion requires a binary
classification of eye gaze into forward or non-forward area (e.g.,
beyond a 24x24-deg square around the focus of expansion), which is
achievable using automatic eye tracking systems such as Seeing
Machines\' Facelab eye tracking system
([www.seeingmachines.com](http://www.seeingmachines.com/); Heinzmann &
Zelinsky, 1998; Victor, Blomberg & Zelinsky, 2001) and Delphi\'s Driver
State Monitor (Edenborough, Hammoud et al., 2004). Because it does not
require precise measurements of eye gaze and has a strong correlation
with driving performance, eyes-off-road proportion has been recommended
as a real-time diagnostic of visual distraction that will be implemented
and evaluated in Task 14.

In Phase I (Zhang & Smith, 2004b), the strong correlations for
eyes-off-road proportion were obtained after averaging across subjects.
Across-subjects averaging is necessary to achieve data reliability
because each subject experienced only two or three non-imminent lead
vehicle braking events per condition. Reaction times can vary randomly
within a particular distribution and averaging across a number of
reaction time values is a typical method employed in human factors to
acquire reliable results. In real-time implementations, though,
eyes-off-road proportion will be computed using eye gaze information
from one single driver rather than from across-subjects averages. To
bridge the gap, a new simulator experiment is performed to produce a
larger number of lead vehicle braking events per subject. The new
experiment consists of two 3-hr sessions and generates as many as
sixteen lead vehicle braking events per subject per condition. Reaction
times can be averaged across these sixteen braking events to obtain
reliable results.

More research is also needed to determine threshold values for
eyes-off-road proportion. When visual distraction thresholds are
exceeded, distraction feedback can be provided to drivers to re-direct
their attention back to the forward road (Donmez,

Boyle, Lee, & McGehee, 2004a, 2004b). Because this experiment produces a
large number of lead vehicle braking events, the relationship between
eyes-off-road proportion and reaction time will be investigated more
fully to determine the thresholds.

The use of a large number of reaction time events also provides
opportunities to explore other diagnostic measures. Donmez, Boyle, Lee,
and Scott (2005) have shown that eye gaze gamma, which is defined as
(weight)X(instantaneous eye gaze duration)+(1-weight)X(total
eyes-off-road glance duration) within a time window (e.g., 5-s), is a
promising measure of visual distraction. In this experiment, the effect
of eye gaze gamma on accelerator release reaction time and SDLP will be
investigated.

Although Phase I (Zhang & Smith, 2004b) showed a display eccentricity
effect, some common vehicle locations (e.g., left mirror or rearview
mirror) were not investigated. In this experiment, several new values of
display eccentricity are added. Subjects are asked to read aloud words
that are presented at locations such as the left mirror, rearview
mirror, high-mounted head-down display, and center console monitor.
Words are also presented in the forward road area to simulate
distractions such as reading license plates of forward vehicles and
street signs. Five forward locations include: the focus of expansion,
the left side of the forward screen, the right side of the forward
screen, the top of the forward screen, and the bottom of the forward
screen (near the typical location of a head-up display). All these
locations are within a 24x24-deg square and eye gaze to these locations
is therefore classified as forward. Using these display locations,
driving performance and reaction time will be assessed for both small
and large display eccentricities.

# 7.2. Method

### 7.2.1. Subjects

Twelve subjects (seven males and five females) were recruited from the
salaried employee pool at Delphi Electronics and Safety at Kokomo,
Indiana. They were required to be in the range of 35-55 years old and
possess a valid driver\'s license. One subject wore thin eyeglasses, two
subjects wore contact lenses, and nine subjects did not wear eyeglasses
or contact lenses. 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 twelve subjects ranged between 35-53,
averaged 45, and had a standard deviation of 7. They were paid a \$100
Wal-Mart gift card for their participation in the 6-h experiment.

### 7.2.2. Design

7.2.2.1. Delphi Driving Simulator and Display Monitors

The experiment was performed in the Delphi Driving Simulator at Kokomo,
Indiana. It was a fixed-base, one forward channel DriveSafety system
(www.drivesafety.com). The simulator projected a 1024x768-pixel
50x40-deg forward field-of-view image located at the front bumper of the
vehicle cab. The vehicle handling system was configured to represent a
mid-size front wheel drive sedan, such as a Ford Taurus. Steering
feedback was presented with a force-feedback torque motor, to reproduce
the feel of the road at the steering wheel, as well as the forces on the
front tires during evasive maneuvers. The vehicle cab consisted of the
front half of a 1995 Pontiac Bonneville exterior (with doors and roof
removed), with a 1996 Buick Park Avenue instrument cluster and
dashboard.

Distraction words were presented on the displays to create various
distraction conditions. As shown in Figure 7.1, a 21-in. CRT monitor was
placed in the center console at 27 deg to the right of the focus of
expansion and 26 deg below the horizon (with a diagonal eccentricity of
37 deg). Two 7-in. LCD displays were used as the left-side mirror (36
deg to the left of the focus of expansion and 13 deg below the horizon
with a diagonal eccentricity of 38 deg) and the rearview mirror (35 deg
to the right of the focus of expansion and 6 deg above the horizon with
a diagonal eccentricity of 36 deg), respectively. Another 7-in. LCD
display was placed sideways on the top of the instrument cluster at 10
deg below the focus of expansion.

7.2.2.2. Experimental Design

The amount of distraction was the major independent variable. The
following twelve distraction conditions were created,

-   1\. Baseline: no distraction was introduced.

-   2\. Center: Subjects were asked to read a row of 3 words presented
    at the center of the forward screen (the words were centered at 0
    deg horizontally and 0 deg vertically).

-   3\. Left: Subjects were asked to read a row of 3 words presented at
    the left side of the forward screen (the words were centered at -12
    deg horizontally, 0 deg vertically).

-   4\. Right: Subjects were asked to read a row of 3 words presented at
    the right side of the forward screen (the words were centered at 12
    deg horizontally, 0 deg vertically).

-   5\. Top: Subjects were asked to read a row of 3 words presented near
    the top of the forward screen (the words were centered at 0 deg
    horizontally, 9 deg vertically).

-   6\. Down: Subjects were asked to read a row of 3 words presented
    near the bottom of the forward screen (the words were centered at 0
    deg horizontally,

> -6.5 deg vertically). This location corresponded to typical projection
> locations for head-up displays (HUD).

-   7\. Cluster: Subjects were asked to read a row of 3 words presented
    at the high-mounted cluster display (the words were centered at 0
    deg horizontally,

> -9.5 deg vertically).

-   8\. Left Mirror: Subjects were asked to read a row of 3 words
    presented at the left mirror (the words were centered at -36.5 deg
    horizontally, -13 deg vertically).

-   9\. Rearview Mirror: Subjects were asked to read a row of 3 words
    presented at the rearview mirror (the words were centered at 35 deg
    horizontally, 5.5 deg vertically).

-   10\. One Word at Console: Subjects were asked to read 1 word
    presented at the center console monitor (the word was centered at 27
    deg horizontally,

> -25.5 deg vertically).

-   11\. Three Words at Console: Subjects were asked to read a row of 3
    words presented at the center console monitor (the words were
    centered at 27 deg horizontally, -25.5 deg vertically).

-   12\. Five Words at Console: Subjects were asked to read a row of 5
    words presented at the center console monitor (the words were
    centered at 27 deg horizontally, -25.5 deg vertically).

For distraction conditions 2-12, words were updated once every 5
seconds. The words were in a sans serif font extending a visual angle of
approximately 25 minute of arc on all displays.

The dependent variables included performance variables such as positions
of the accelerator pedal and the brake pedal, steering wheel angles,
lane positions, and vehicle speeds. These variables were used to
generate performance variables that were commonly investigated in the
literature. For example, lane positions were used to produce the
standard deviation of lane positions (SDLP) and number and duration of
lane departures.

Eye glance variables included head orientations, eye gaze coordinates,
and attention coordinates that were based on weighted head orientations
and gaze coordinates. Eye closures, blinks and saccades were also
recorded. These variables were used to produce eye glance measures that
are commonly examined in the literature. For example, attention
coordinates were used to determine the eyes-off-road glance duration and
glance frequency.

7.2.2.3. FaceLab Eye Tracking System from Seeing Machines, Inc.

Eye glance variables were measured and recorded with the FaceLab eye
tracking system (Version 2.0.1) developed by Seeing Machines, Inc.
(Heinzmann & Zelinsky, 1998; Victor, Blomberg, & Zelinsky, 2001). The
FaceLab system consisted of a stereo head with two Sony cameras for
image capturing, and a Dell computer for image processing and gaze
coordinate determination. The stereo head was installed above the
dashboard and centered horizontally with respect to the subject. The
simulator room was dimly illuminated to minimize glares and reflections.
A 9x4 infrared LED array with a peak emission of 880 nm was placed
between the two cameras to provide a high level of illumination to the
subject\'s face area.

The method of image processing with template matching feature tracking
was used by the FaceLab system to track both the head and eye movements.
An initial calibration was required to mark the salient facial features
including the eye corners and mouth corners. Once calibrated, the system
operated automatically without subjects\' interventions. It generated
output measures such as head position and orientation, eye gaze
coordinate (e.g., pitch and yaw), attention pitch and yaw angle
(combining head orientation and eye gaze coordinate), eye blink, eye
closure, and associated confidence levels with a sampling rate of 60 Hz.
The reported accuracy for eye gaze coordinates was +/-3 deg (Victor,
Blomberg, & Zelinsky, 2001). Graphic depictions of head orientation and
eye gaze were provided by the FaceLab system for easy operations by the
experimenter.

7.2.2.4. NASA-TLX

The NASA Task Load Index (TLX) was used to measure the workload and
perceived task difficulty associated with various experimental
conditions (Hart & Staveland, 1988). It was a multi-dimensional rating
procedure that provided an overall workload score based on a weighted
average of rat­ings on six sub-scales: Mental demands, physical demands,
temporal demands, performance, effort, and frustration. The score on
each sub-scale ranged from 0 to 100. The overall workload score varied
from 0 to 100, with a higher score indicating a higher level of
subjective workload and perceived task difficulty.

### 7.2.3. Procedure

Each subject ran two 3-hour sessions. All twelve distraction conditions
were run within each session. In order to obtain a sufficient number of
braking events to afford a subject-by-subject analysis, each subject was
exposed to approximately eight braking events per session (or a total of
sixteen braking events over two sessions).

Upon arrival at the laboratory, subjects were given a brief description
of the study and requested to read and sign an informed consent form.
They were given the vision test (Snellen Eye Chart) and required to pass
the test before proceeding (eyeglasses were permitted). The calibration
procedure was then completed for the eye tracking system. Five snapshots
of the subject\'s face were taken, one with the subject facing forward,
one facing slightly left, one facing slightly right, one facing 90 deg
left, and one facing 90 deg right. For each snapshot, salient features
such as eye corners, mouth corners, eyebrows, and nostrils were selected
and marked. Afterward, the FaceLab system began to track a subject\'s
head and eye movements automatically.

For the simulator driving, subjects were given two five-minute practice
blocks. During the first practice block, subjects were asked to drive
without introducing any visual distraction. During the second practice,
subjects were asked to drive while reading words on the center console
monitor. After the practice block, subjects ran 12 experimental blocks.
For each experimental block, subjects were informed about the desired
speed (65 MPH) and the nature of visual distraction (e.g., display
location). When subjects shifted the gear to \"D\", the lead vehicle
began to move forward and gradually reach the desired speed. Subjects
were asked to follow the lead vehicle with a close and safe distance.
They were instructed to maintain good lane positions and avoid crashes.

In the distraction conditions, subjects were presented with a display of
new words every 5 s. When the new words appeared on the display, a beep
was sounded to alert the subjects. Subjects were asked to read aloud as
many words as possible without sacrificing safety. The manner in which
subjects shared their visual attention between the forward driving scene
and the visual display was not controlled. Approximately 15-20 seconds
into the experimental block a software program was activated that
automatically accelerated or decelerated the lead vehicle so that the
time headway between the lead subject and the subject vehicle was
gradually changed to a constant value of 1.8 s. The previous studies
have shown that time headways influenced the measurement of reaction
times, consequently a constant value was used in order to minimize any
confounding. At a randomly chosen moment, the lead vehicle braked slowly
for 5 s (at a deceleration rate of --2.7 m/s^2^). When the lead vehicle
began to brake, the time headway control program was deactivated so that
the subject vehicle gradually closed in with the lead vehicle. This
procedure is similar to one that was deployed in previous research
(e.g., Lee, Caven, Haake, and Brown, 2001; Lee, McGehee, Brown, and
Reyes, 2002; Zhang and Smith, 2004b). A few seconds later, subjects
responded to the situation by releasing the accelerator pedal and
depressing the brake pedal. The times at which the lead vehicle began to
brake and the subjects responded to the braking lead vehicle were
recorded in order to calculate the reaction times. After the braking
event, the lead vehicle gradually accelerated to the desired speed and
subjects again followed the lead vehicle. Approximately 15-20 seconds
later, the time headway control program was reactivated so that the time
headway was gradually changed to 1.8 s. The lead vehicle braked at -2.7
m/s^2^ for 5 s at another randomly chosen moment. Within each 10-minute
block, subjects experienced approximately eight braking events. The
braking events were separated by a minimum of 50 s so that subjects
engaged in distraction tasks (except for the baseline conditions) for at
least 30 s before each braking event in order to afford data analysis
with a time window as large as 30 s.

After each experimental block, subjects were given the NASA-TLX scale
and asked to rate the task difficulty and workload for the condition
that was just completed.

# 7.3. Results

### 7.3.1. Variables

Consistent with Zhang and Smith (2004b), a 24X24-deg rectangular area
around the focus of expansion was defined as the forward road area. Like
Zhang and Smith (2004b), the attention pitch and yaw angles from Facelab
were used to determine whether or not eye gaze was on a forward road
area or away from the forward road. The head direction pitch and yaw
angles were used to determine whether or not the driver\'s head pose was
on the forward area or away from the forward area.

Figure 7.4 illustrated the sequence of events. The moment that the lead
vehicle braked was designated as the zero time point (t = 0). The time
interval between the moment that the lead vehicle braked (t = 0) and the
moment that the subject completely released the accelerator pedal (when
the simulator variable for the accelerator pedal position was zero) was
defined as the accelerator release reaction time (ART). The time
interval between the moment that the lead vehicle braked (t = 0) and the
moment that the subject began to depress the brake pedal (when the
simulator variable for the brake pedal position was non-zero) was
defined as the brake reaction time (BRT). These variables were used by
Lee et al. (2001, 2002) and Zhang and Smith (2004b). Eye glance
variables and performance variables were computed within a time window
that preceded the zero time point (t = 0). The time window was
re-configurable within the range of 1-30 s. As in Phase I, the results
from various time windows were similar. Because it was simpler to
implement a 4.3-s time window in a binary system (4.3 s by 30 Hz can be
implemented in 7 bits), this time window was adopted for subsequent
analyses.

![](media/image4.wmf){width="5.999305555555556in"
height="3.3006944444444444in"}

Figure 7.4. Sequence of events and definition of variables

For each reaction time event, one value each was computed for ART, BRT,
steering entropy, eye glance variables (e.g., glance frequency), and
performance variables (e.g., SDLP). Up to 16 reaction time events
occurred for each distraction condition. The values from the same
distraction condition were averaged to obtain a mean value for each of
the 12 subjects and each of the 12 conditions.

The computations were repeated using a re-configurable time window
(between 1-30 s). As shown in Figure 7.4, a time window always ended at
the zero time point at which the lead vehicle began to brake. Depending
on the time window that was used in the analysis, a window began at 1-30
s before the zero time point. Note that the time windows were not
sampled continuously from end to end. Because reaction time events were
separated by a minimum of 45 s, over 45 s of data were available for a
reaction time event. However, data beyond a time window were not
included in the analysis. If the time window was 4.3 s, for example,
only 4.3 s of data were sampled from the database for a reaction time
event and remaining data (over 40 s, or 45-4.3 s) were not analyzed. Of
course, there was some overlap between the time windows. For example,
data from the second half of a 30-s window were exactly identical to
data from a 15-s time window.

The results from different time windows were similar. To reflect
real-time representation of visual distraction and facilitate the
implementation of visual distraction algorithm in the embedded
environment, 4.3-s was adopted as the time window in the following
analyses. The following eye glance variables were computed over a 4.3-s
time window. The attention pitch and yaw angles produced by the FaceLab
system were used for the computation of all eye glance variables. Note
that all variables were time-based rather than task-based. Separate
computations were carried out for different time windows.

-   Mean glance duration (s): It was defined as the mean amount of time
    of all off-road glances (beyond a 24X24-deg rectangular forward
    area) over a time window.

    -   

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-   Glance frequency (number of glances): It was defined as the total
    number of off-road glances (beyond a 24X24-deg rectangular forward
    area) in a time window.

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-   Total eyes-off-road glance time (s): It was defined as the
    cumulative time elapsed for all eyes-off-road glance (beyond a
    24X24-deg square) over a time window. A related measure is total
    head-off-road time, which was defined as the cumulative time elapsed
    for all head-off-road glance (beyond a 24X24-deg square) over a time
    window.

-   Proportion of eyes-off-road glance time: It was defined as the ratio
    of total eyes-off-road glance time over a time window. A related
    measure is proportion of head-off-road glance time, which was the
    ratio of total head-off-road glance time over a time window.

-   Eye gaze vector (deg): It was defined as the square root of the sum
    of the squared attention yaw angle and squared attention pitch angle
    from Facelab. This reflected the distance between the focus of
    expansion and the attention coordinates (a combination of head
    orientation and eye gaze).

-   Eye gaze variability (deg^2^): Following the definition of visual
    inspection window (Recarte & Nunus, 2000), it was defined as 4 X
    (standard deviation of attention yaw angle) X (standard deviation of
    attention pitch angle).

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-   Eye gaze gamma (s): Following Donmez, Boyle, Lee, and Scott (2005),
    it was defined as (weight)X(instantaneous eyes-off-road glance
    duration at the moment of lead vehicle braking)+(1-weight)X(total
    eyes-off-road glance duration). A related measure is head pose
    gamma, which was defined as (weight)X(instantaneous head-off-road
    glance duration at the moment of lead vehicle
    braking)+(1-weight)X(total head-off-road glance duration).

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The following performance variables were computed over a 4.3-s time
window preceding the zero time point. Again, separate computations were
carried out for different time windows.

-   Standard deviation of lane position (SDLP, m): The statistical
    formula for standard deviation was applied to the lane positions
    produced by the DriveSafety simulator to determine the SDLP in a
    time window.

-   Number of lane departures: A lane departure occurred if any part of
    the subject vehicle crossed the left or right lane boundary. In the
    present study, this occurred if the lane position was greater than
    1.022 m or less than --1.022 m. The total number of lane departures
    in a time window was tallied.

The definition of steering entropy was provided by Nakayama, Futami,
Nakamura, and Boer (1999) and Boer (2001). First, steering wheel angles
from three preceding time steps were used to compute the predicted
steering angle. Because the steering angles were produced by the
simulator at 60 Hz, each time step was 1/60 ms (16.7 ms). Second, the
predicted steering angle was compared with the actual steering angle and
their difference was calculated as the prediction error. Third, the
prediction errors within a time window were divided into nine bins, and
the proportion of prediction errors in each bin, p~i~, was calculated.
Finally, -pi log~9~(pi) was computed for each bin and summed across nine
bins to derive the steering entropy measure.

### 7.3.2. ANOVA and Correlation Results

7.3.2.1. NASA-TLX

Figure 7.5 presents mean NASA-TLX scores and standard errors for various
distraction conditions. An ANOVA analysis with all 12 conditions
indicated a main effect of distraction, F(11, 121)=27.05, p\<.001.
Post-hoc analyses revealed a significantly smaller TLX score for the
baseline condition than for other distraction conditions. There was no
significant difference among conditions 2-6, F(4, 44)=1.78, p=0.15,
indicating similar workload for reading words at the different locations
on the forward screen. Conditions 10-12 were statistically different
from one another, F(2, 22)=32.15, p\<.001, indicating that reaction
times lengthened as the number of words increased. Conditions 7-9 and 11
were statistically different from one another, F(3, 33)=7.39, p\<.001.
Additional analyses revealed smaller TLX scores for condition 7 than for
conditions 8, 9, and 11, and no difference among conditions 8, 9, and
11, indicating the effect of display eccentricity.

![](media/image5.wmf){width="5.99375in" height="3.0069444444444446in"}

Figure 7.5. Mean NASA-TLX scores (+/-standard error as error bar)

for various distraction conditions.

![](media/image6.wmf){width="5.992361111111111in" height="3.0875in"}

Figure 7.6. Mean number of missed words (+/-standard error as error bar)

for various distraction conditions.

7.3.2.2. Reading Performance

Figure 7.6 presents mean number of words missed for various distraction
conditions. There was a main effect of distraction, F(11, 121)=2.21,
p\<.05. Post-hoc analyses revealed that condition 12 was statistically
different from every other condition (p\<0.05), but there was no other
difference. This finding illustrated that subjects had difficulties
finishing reading 5 words in 5 seconds while driving.

7.3.2.3. Mean Eyes-Off-Road Glance Duration

Figure 7.7 presents mean eyes-off-road glance duration averaged across
4.3-s time windows prior to lead vehicle braking for various distraction
conditions. There was a main effect of distraction, F(11, 121)=17.34,
p\<.001. There was no difference among conditions 1-6, F(5, 55)=1.95,
p=.10. There was a statistically significant difference among conditions
1, 10-12, F(3, 33)=11.5, p\<.001, indicating an increased glance
duration with an increased number of words. Condition 7 had a shorter
glance duration than did conditions 8, 9, and 11, reflecting the effect
of display eccentricity.

![](media/image7.wmf){width="5.99375in" height="3.0520833333333335in"}

Figure 7.7. Mean eye glance duration within a 4.3-s time window

(+/-standard error as error bar) for various distraction conditions

7.3.2.4. Glance Frequency

Figure 7.8 presents eyes-off-road glance frequency within a 4.3-s time
window prior to lead vehicle braking. There was an overall main effect
of distraction, F(11, 121)=4.81, p\<.001. Post-hoc analyses revealed
that the baseline condition had significantly fewer eyes-off-road
glances than all other conditions (p\<.05) except conditions 2 (words at
the center of the forward screen) and 5 (words near the top of the
forward screen). There was no statistical difference among conditions
2-6, F(4, 44)=2.11, p=0.10. No difference was found among conditions
10-12, F(2, 22)=0.05, p=0.96, indicating that subjects did not make more
eyes-off-road glances with an increased number of words on the display.

![](media/image8.wmf){width="5.997222222222222in"
height="3.061111111111111in"}

Figure 7.8. Eyes-off-road glance frequency within a 4.3-s time window

(+/-standard error as error bar) for various distraction conditions

7.3.2.5. Proportion of eyes-off-road glance time

Figure 7.9 presents the proportion of eyes-off-road glance time within
4.3-s time windows. This measure is calculated by dividing the total
time that the subject\'s eye glance was off the forward road by the time
window (e.g., 4.3). There was an overall main effect of distraction,
F(11, 121)=30.33, p\<.001. There was no significant difference among
conditions 1-6, F(5, 55)=2.20, p=0.07. Additional analyses revealed
significant differences among conditions 1 and 10-12, F(3, 33)=30.76,
p\<.001, and significant pair-wise differences among these conditions
(p\<.05), reflecting increased eyes-off-road glance proportion with
increased number of words. There was a significant difference among
conditions 7-9 and 11, F(3, 33)=26.78, p\<.001, specifically a smaller
proportion for condition 7 than for conditions 8, 9, and 11 (p\<.05),
reflecting an effect of display eccentricity.

7.3.2.6. Eye Gaze Variability

Figure 7.10 presents eye gaze variability within 4.3-s time windows.
There was a main effect of distraction, F(11, 121)=10.32, p\<.001.
Post-hoc analyses revealed essentially two different groups: conditions
1-7 vs. conditions 8-12. Condition 10 was smaller than conditions 9 and
11.

![](media/image9.wmf){width="5.995833333333334in"
height="2.790277777777778in"}

Figure 7.9. Proportion of eyes-off-road glance time within a 4.3-s time
window

(+/-standard error as error bar) for various distraction conditions

![](media/image10.wmf){width="6.0in" height="3.0145833333333334in"}

Figure 7.10. Eye gaze variability within a 4.3-s time window

(+/-standard error as error bar) for various distraction conditions

7.3.2.7. Eye Gaze Vector

Figure 7.11 presents eye gaze vector within 4.3-s time windows. There
was a main effect of distraction, F(11, 121)=24.15, p\<.001. Conditions
1-7 were smaller than conditions 8-12 (p\<.05). Condition 10 was smaller
than conditions 8-9 and 11-12.

![](media/image11.wmf){width="5.999305555555556in"
height="3.145138888888889in"}

Figure 7.11. Eye gaze vector within a 4.3-s time window

(+/-standard error as error bar) for various distraction conditions

![](media/image12.wmf){width="5.995833333333334in"
height="3.0902777777777777in"}

Figure 7.12. Eye gaze gamma within a 4.3-s time window

(+/-standard error as error bar) for various distraction conditions

7.3.2.8. Glance Gamma

Figure 7.12 presents eye gaze gamma within 4.3-s time windows. There was
a main effect of distraction, F(11, 121)=22.6, p\<.001. Additional
analyses revealed that there was no difference among conditions 1-6,
F(5, 55)=1.82, p=.12. There was a significant pair-wise difference among
conditions 1 and 10-12 (p\<.05), indicating an increased eye gaze gamma
with an increased number of words. There was a significant difference
among conditions 7, 8, 9, and 11, F(3, 33)=17.69, p\<.001, and a smaller
gamma for condition 7 than for conditions 8, 9, and 11 (p\<.05)
indicating the effect of display eccentricity.

7.3.2.9. Standard deviation of lane position (SDLP)

Figure 7.13 presents standard deviations of lane position within 4.3-s
time windows. There was a main effect of distraction, F(11, 121)=15.27,
p\<.001. Post-hoc tests revealed that there was no significant
difference between the baseline condition and conditions 2-6, indicating
no degradation in lane keeping performance for reading words on the
forward screen. Conditions 1 and 10-12 were different, F(3, 33)=26.76,
p\<.001, reflecting an increased SDLP with an increased number of words.
The pattern of SDLP results (Figure 7.13) was very similar to that of
eyes-off-road proportion (Figure 7.9), indicating the close association
between these two variables.

![](media/image13.wmf){width="5.995138888888889in"
height="3.013888888888889in"}

Figure 7.13. Standard deviation of lane position (SDLP) within a 4.3-s
time window

(+/-standard error as error bar) for various distraction conditions

7.3.2.10. Number of Lane Departures

Figure 7.14 presents the number of lane departures within 4.3-s time
windows. There was a main effect of distraction, F(11, 121)=4.49,
p\<.001. Post-hoc tests revealed a significantly larger number of lane
departures for condition 12 than conditions 1-11, indicating marked
degradation in lane keeping performance while reading 5 words on the
center console display. There was no difference among conditions 1-6,
F(5, 55)=1.02, p=0.41, indicating no degradation in lane keeping
performance while reading words on the forward screen.

![](media/image14.wmf){width="5.997916666666667in" height="2.975in"}

Figure 7.14. Number of lane departures within a 4.3-s time window

(+/-standard error as error bar) for various distraction conditions

7.3.2.11. Steering Entropy

Figure 7.15 presents steering entropy within 4.3-s time windows prior to
lead vehicle braking. Although there was a main effect of distraction
condition, F(11, 121)=2.81, p\<.01, steering entropy did not vary
systematically with distraction. Figure 7.15 showed smaller steering
entropy values when subjects were asked to read words on the
high-mounted cluster display, right mirror, and center console display
in comparison with the baseline condition.

![](media/image15.wmf){width="5.995138888888889in"
height="3.029166666666667in"}

Figure 7.15. Steering entropy within a 4.3-s time window

(+/-standard error as error bar) for various distraction conditions

7.3.2.12. Accelerator Release Reaction Time

Although both accelerator release reaction time and brake reaction time
were acquired, only accelerator release reaction time was reported here
because these two reaction times were highly correlated (see also Zhang,
Smith, & Witt, 2006). Figure 7.16 presents the accelerator release
reaction times for 12 distraction conditions. There was a main effect of
distraction, F(11, 121)=5.92, p\<.001. Additional analysis indicated
that the baseline condition produced significantly shorter reaction time
than did the other distraction conditions (p\<.05). A comparison among
distraction conditions 1, 10, 11, and 12 illustrated that the reaction
time increased with the number of words, F(3, 33)=12.04, p\<.001. This
reaction time result resembled that of eyes-off-road proportion (Figure
7.9), indicating close association between eyes-off-road proportion and
accelerator release reaction time.

It was important to note that the reaction time was significantly
shorter for the baseline than for distraction conditions 2-6 in which
subjects were asked to read aloud words on the forward screen. As
presented earlier, however, there was no difference in eyes-off-road
proportion among conditions 1-6 (Figure 7.9). The discrepancy between
eyes-off-road proportion (Figure 7.9) and accelerator release reaction
time (Figure 7.16) for conditions 1-6 indicates a dissociation between
these two variables. Even when visual distraction was not detectably
higher (in conditions 2-6) than the baseline, accelerator release
reaction time was slower if subjects were asked to engage in a
distracting task (e.g., reading words on the forward screen). This
finding was similar to the \"change blindness\" phenomenon (Rensink,
O\'Regan, & Clark, 1997). Subjects failed to notice the activation of
the lead vehicle brake light even though their visual glance was in the
forward area.

![](media/image16.wmf){width="5.996527777777778in"
height="3.1847222222222222in"}

Figure 7.16. Accelerator release reaction times (standard errors as
error bars)

for various distraction conditions.

7.3.2.13. Correlation Analysis With All 12 Conditions

Correlation analysis can be employed to demonstrate the associations
among different variables. First, means were computed for every subject,
condition, and variable. Second, grand mean values were obtained for
every condition and variable by averaging across twelve subjects. Third,
Pearson correlation coefficients were computed using the grand mean
values. Table 7.1 presents Pearson correlation coefficients among
accelerator release reaction time, SDLP, number of lane departures,
steering entropy, TLX, and several eye glance measures. It was clear
that all eye glance measures were strongly correlated with each other,
and they were highly correlated with TLX, SDLP, and number of lane
departures. Steering entropy had a low and negative correlation with all
other variables, probably because steering entropy did not show a clear
trend of distraction effect (see Figure 7.15).

Table 7.1. Pearson correlation coefficients using twelve distraction
conditions (N=12)

![](media/image17.wmf){width="6.497222222222222in"
height="2.6638888888888888in"}

7.3.2.14. Correlation Analysis With Conditions 1, 7-12

Accelerator release reaction time had a low correlation with all other
variables. This low correlation could have been caused by the
aforementioned dissociation between accelerator release reaction time
and eye-off-road proportion among conditions 1-6. To verify this
hypothesis, Pearson correlation coefficients were calculated for
accelerator release reaction time after excluding conditions 2-6. Table
7.2 presents the results. It was evident that accelerator release
reaction time was highly correlated with TLX, SDLP, number of lane
departures, and all eye glance measures. This supported the hypothesis
that low correlations for accelerator release reaction time in Table 7.1
were mainly due to the dissociation between accelerator release reaction
time and eyes-off-road proportion for conditions 1-6. This dissociation
will be discussed in more detail later. Conditions 1 and 7-12 will be
used for all subsequent analyses with accelerator release reaction time.

Table 7.2. Pearson correlation coefficients using distraction conditions
1 and 7-12 (N=7).

![](media/image18.wmf){width="6.497222222222222in"
height="0.7861111111111111in"}

7.3.2.15. Diagnostic Measures

Table 7.1 illustrates that several eye glance measures, including mean
eye glance duration, eyes-off-road proportion, eye gaze vector, and eye
gaze gamma, were highly correlated with SDLP and lane departures and
therefore could be reasonable diagnostic measures of visual distraction.
Table 7.2 indicates that these eye glance measures were strongly
correlated with accelerator release reaction time, excluding Tasks 2-6.

Similarly, Zhang and Smith (2004b) discovered that eyes-off-road
proportion and eye gaze vector were potential diagnostic measures of
visual distraction. However, eye gaze vector requires a very precise eye
tracking system that may be available for on-road evaluation in the
SAVE-IT program (Task 14). Eyes-off-road proportion does not require
precise determination of eye gaze. The only information that is required
is whether the eye gaze is forward or not, which is feasible in
automotive-grade eye tracking systems such as Delphi\'s Driver State
Monitor. Eye gaze gamma is a promising measure that warrants further
examinations.

From the preceding discussion, it appears that eyes-off-road proportion
and eye gaze gamma are most promising measures of visual distraction.
Because it is more difficult to determine eye gaze than head pose, some
researchers have proposed the use of head pose measures. To assess the
effect of head pose-based glance measures, Pearson correlation
coefficients were computed between head pose-based glance measures
(e.g., head-off-road proportion and head pose gamma) and accelerator
release reaction time or SDLP. As shown in Table 7.3, head-off-road
proportion or head pose gamma is highly correlated with accelerator
release reaction time or SDLP, supporting the claim of using head
pose-based measures to detect visual distraction.

Table 7.3. Pearson correlation coefficients between head pose measures
and accelerator release reaction time or SDLP.

![](media/image19.wmf){width="2.9756944444444446in"
height="1.3138888888888889in"}

### 7.3.3. Subject-by-Subject Analyses

A major emphasis of Task 7C is to determine whether diagnostic measures
of visual distraction apply to individual subjects. A subject-by-subject
analysis is therefore performed between eye glance measures and
accelerator release reaction time or SDLP. We analyzed two eye glance
measures (i.e., eyes-off-road proportion and eye gaze gamma) and two
corresponding head pose-based measures (i.e., head-off-road proportion
and head pose gamma).

7.3.3.1. Eyes-off-road proportion

Table 7.3 presents Pearson correlation coefficients between
eyes-off-road proportion and accelerator reaction time or SDLP for each
of the twelve subjects. Conditions 1 and 7-12 were used in the
calculations for accelerator release reaction time, and all twelve
conditions were used in the SDLP calculations. For accelerator release
reaction time, ten out of twelve subjects had a moderate or high
correlation, with Pearson correlation coefficients larger than 0.30. For
eight subjects, the correlation coefficients were greater than 0.50. For
two subjects (subjects 3 and 8), the correlation coefficients were
negative. A further examination of these two subjects revealed noisy
accelerator release reaction times. Even though as many as sixteen
reaction time events were presented for every distraction condition,
reliable reaction times were not obtained.

There was no established threshold in the literature for a reliable
correlation coefficient value. For SDLP, all correlation coefficients
were positive. With the exception of one subject (subject 6), all
correlation coefficients exceeded 0.50. Overall, these results suggested
that eyes-off-road proportion was a reasonable diagnostic for nearly all
individuals.

Table 7.3. Pearson correlation coefficients between eyes-off-road
proportion and accelerator release reaction time or SDLP

7.3.3.2. Head-off-road proportion

Table 7.4 presents Pearson correlation coefficients between
head-off-road proportion and accelerator release reaction time or SDLP.
Ten out of twelve subjects had a positive correlation and two subjects
(subjects 3 and 8) had a negative correlation, which was likely due to
noisy reaction times. Eight subjects had a correlation coefficient of
0.30 or higher, and five subjects had a correlation coefficient of 0.5
or higher. For SDLP, eleven out of twelve subjects had a positive
correlation, and eight subjects had a correlation coefficient of 0.35 or
higher. Because these correlation coefficients were smaller than those
for eyes-off-road proportion (see Table 7.3), it was preferable to use
eyes-off-road proportion as a diagnostic measure when eye gaze
information was available.

Table 7.4. Pearson correlation between head-off-road proportion and
accelerator release reaction time or SDLP

7.3.3.3. Eye gaze gamma

Table 7.5 presents Pearson correlation coefficients between eye gaze
gamma and accelerator release reaction time or SDLP for each of the
twelve subjects. Again, reaction time calculations used conditions 1 and
7-12 and SDLP calculations used all twelve conditions. For accelerator
release reaction time, ten out of twelve subjects had a correlation
coefficient of 0.25 or higher, and eight subjects had a correlation
coefficient of 0.50 or higher. Again, two subjects (subjects 3 and 8)
had a negative correlation coefficient, largely because of noisy
reaction times. For SDLP, all but one subject (subject 6) had a
correlation coefficient of 0.5 or higher. These results indicated that
eye gaze gamma was a reasonable diagnostic of visual distraction for
nearly all individuals.

7.3.3.4. Head pose gamma

Table 7.6 presents Pearson correlation coefficients between head pose
gamma and accelerator release reaction or SDLP. For accelerator release
reaction time, nine out of twelve subjects had a positive correlation,
and six subjects had a correlation coefficient of 0.5 or higher. For
SDLP, eleven out of twelve subjects had a positive correlation, and six
subjects had a correlation coefficient of 0.35 or higher. These
coefficients were smaller than those for eye gaze gamma (see Table 7.5),
suggesting the use of eye gaze gamma as a diagnostic measure when it was
available.

Table 7.5. Pearson correlation coefficients between eye gaze gamma and
accelerator release reaction time or SDLP

Table 7.6. Pearson correlation coefficients between head pose gamma and
accelerator release reaction time or SDLP

### 7.3.4. Across-Conditions Analyses 

7.3.4.1. Eyes-off-road proportion

Because we have collected a large amount of data in this experiment,
next we collapsed data across twelve subjects and seven conditions
(conditions 1 and 7-12) to further investigate the relations between
eyes-off-road proportion and reaction times. Figure 7.17 presents
accelerator release reaction time as a function of eyes-off-road
proportion. The data were grouped into 5 bins, each with approximately
20% of the data points. The reaction time increase was small when
eyes-off-road proportion was below 0.50, and the increase was
considerable when eyes-off-road proportion exceeded 0.50. Therefore it
appeared that the threshold value should be set at 0.5.

The instantaneous eye gaze at the moment of lead vehicle braking was
classified into \"forward\" and \"not forward\". Accelerator release
reaction times were plotted against eyes-off-road proportions for
forward (green line in Figure 7.17) and non-forward eye gaze separately
(red line in Figure 7.17). When the instantaneous eye gaze was forward
(green line), the accelerator release reaction time increased with the
eyes-off-road proportion. The increase was smaller initially, and large
when eyes-off-road proportion exceeded 0.50. When the instantaneous eye
gaze was not forward (red line), the accelerator release reaction time
was long when eyes-off-road proportion was small (0-0.1) or large
(\>0.5) and short when eyes-off-road proportion was intermediate
(0.3-0.5).

![](media/image20.wmf){width="5.8277777777777775in"
height="5.389583333333333in"}

Figure 7.17. Accelerator release reaction time as a function of
eyes-off-road proportion

7.3.4.2. Head-off-road proportion

Figure 7.18 presents accelerator release reaction time averaged across
conditions 1 and 7-12 as a function of head-off-road proportion. The
average results illustrated that accelerator release reaction time
increased with head-off-road proportion. Reaction time increase was more
marked when head-off-road proportion exceeded 0.5. A value of 0.5 seemed
to be a reasonable threshold.

![](media/image21.wmf){width="5.99375in" height="2.9097222222222223in"}

Figure 7.18. Accelerator release reaction time as a function of
head-off-road proportion

7.3.4.3. Eye gaze gamma

Eye gaze gamma seems to be a reasonable diagnostic because it combines
eyes-off-road proportion and instantaneous eye gaze. The weighting
factor for instantaneous eye gaze was varied to investigate its effect
on reaction time. Figure 7.19 presents accelerator release reaction time
as a function of eye gaze gamma and scaling weight for instantaneous eye
gaze. When the scaling weight was 0.6 or smaller, reaction time
increased with eye gaze gamma. When the scaling weight was 0.8 or
larger, reaction time increased with eye gaze gamma initially, decreased
in the middle, and then increased at large gamma values. In order to use
eye gaze gamma as a diagnostic measure, a monotonic function would be
preferred. Therefore, the scaling weight should not be larger than 0.60.

Accelerator release reaction time was considerably larger when eye gaze
gamma exceeded 2. It appeared that the threshold value should be set at
2.

![](media/image22.wmf){width="5.996527777777778in"
height="5.300694444444445in"}

Figure 7.19. Accelerator release reaction time as a function of eye gaze
gamma and scaling weight for instantaneous eye gaze

7.3.4.4. Head pose gamma

Figure 7.20 presents accelerator release reaction time as a function of
head pose gamma and various scaling factors. When the scaling factor was
0-0.4, accelerator release reaction time increased gradually with head
pose gamma. When the scaling factor was 0.6-1, however, the reaction
time increase was not monotonic. Because a monotonic increase of
reaction time as a function of head pose gamma was preferred, the
scaling factor should be no larger than 0.6. Because accelerator release
reaction time jumped when head pose gamma exceeded 2, a value of 2
should be adopted as the threshold.

![](media/image23.wmf){width="5.995138888888889in"
height="5.418055555555555in"}

Figure 7.20. Accelerator release reaction time as a function of head
pose gamma and scaling weight for instantaneous eye gaze

# 7.4. Discussion

In this simulator experiment, twelve subjects took part in two 3-hr.
sessions in which they followed a lead vehicle that braked
non-imminently at random points. Twelve experimental conditions were
employed in which subjects were asked to read aloud words that were
presented at different locations including the left mirror, rearview
mirror, high-mounted head-down display, center console display, and
forward screen of the simulator. Reading words on the forward screen did
not produce different eye glance behaviors (except for eyes-off-road
glance frequency) than did the baseline condition. The eyes-off-road
proportion, mean eyes-off-road glance duration, eye gaze variability,
eye gaze vector, and eye gaze gamma did not vary depending on whether or
not subjects read words on the forward screen. Except using
eyes-off-road glance frequency, it is difficult to discriminate reading
in the forward area from the baseline condition using an automatic eye
tracking system. The lane keeping performance resembled the eye glance
behaviors. The standard deviation of lane position (SDLP) and number of
lane departures did not vary depending on whether or not subjects read
aloud words on the forward screen. The similarity between eye glance
measures and lane keeping variables substantiate the claim that driving
is mainly a visual-manual task and lateral vehicle control is largely
determined by visual attention or lack of it (visual distraction).

The accelerator release reaction time was markedly longer when subjects
read aloud words on the forward screen than the baseline condition. This
pattern appears to correspond to the NASA-TLX results, indicating that
reaction time results are associated with subjective workload ratings.
The reaction time results did not match the eye glance measures that
were investigated in this experiment, however, indicating a dissociation
between reaction times and these eye glance measures and between
reaction times and lane keeping performance. There are several possible
explanations for this dissociation. One explanation is that detecting a
lead vehicle braking event may require foveal vision whereas lane
keeping may be sufficient with peripheral vision (Horrey, & Wickens,
2004a). When the eye glance is not at the focus of expansion but in
general near the forward area (e.g., when reading words in conditions
2-6), the peripheral vision is sufficient for maintaining lane positions
but insufficient for responding to a lead vehicle braking event. Changes
in eye glance behaviors associated with reading words near the focus of
expansion may be too small to be detected by non-contact eye tracking
systems such as Facelab. A more accurate and precise eye tracking system
that can detect smaller changes in eye glance behaviors may require
physical contact with subjects\' head and is beyond the scope of the
SAVE-IT program. Change blindness is another explanation (Rensink,
O\'Regan, & Clark, 1997). Because attention was directed at a reading
task, subjects did not notice the activation of the lead vehicle braking
light that ultimately slowed down subjects\' reaction times to the lead
vehicle braking event. Related to change blindness is cognitive
distraction. In reality, visual distraction and cognitive distraction
are related to each other. When subjects read aloud words in the forward
area, they are processing the word information and cognitively
distracted from the driving task. Cognitive distraction has been shown
to slow down reaction times in driving (Horrey, & Wickens, 2004b; Lee,
Caven, Haake, & Brown, 2001; Lee, Reyes, Smyser, Liang, & Thornburg,
2004). The practical implication of this dissociation is that
distraction cannot always be detected using a non-contact eye tracking
system.

Excluding the forward distraction conditions, the reaction time pattern
is similar to that of eye glance measures or lane keeping performance.
Eye glance measures including eyes-off-road proportion, mean
eyes-off-road glance duration, eye gaze vector, and eye gaze gamma
increase as the number of words increases. So do the number of lane
departures, SDLP, and accelerator release reaction time. The similarity
was captured in Pearson correlation coefficients. High correlations were
reliably obtained between eye glance measures and SDLP or accelerator
release reaction time. This result validated eyes-off-road proportion
and eye gaze vector as real-time diagnostic measures of visual
distraction. It supported the use of eye gaze gamma as a potential
diagnostic measure of visual distraction.

Eyes-off-road proportion and eye gaze gamma are especially attractive
because they do not require highly precise measurements of eye gaze.
They only require a binary classification in terms of forward and
non-forward eye gaze that is feasible in automotive-grade eye tracking
system including Delphi\'s Driver State Monitor (Edenborough, Hammoud,
et al., 2004). Because the eyes may be sometimes occluded due to
sunglasses or head movements, it may be advantageous to use head pose to
detect visual distraction. Both eye gaze-based and head pose-based
versions of these two eye glance measures have been investigated in
detail in this experiment.

A subject-by-subject analysis was performed for eyes-off-road
proportion, head-off-road proportion, eye gaze gamma, and head pose
gamma. For a majority of subjects, there was a positive correlation
between these glance measures and accelerator release reaction time
(excluding the forward distraction conditions) or SDLP. This indicates
that these glance measures may be used as diagnostic measures of visual
distraction for most subjects. For some subjects, the correlations were
negative or small. We have not been able to determine subject profiles
for high-correlation and low-correlation groups. Such a determination
may require a sample size larger than that is employed in this
experiment (12 subjects). In general, eye-gaze-based measures had more
positive and higher correlations than did head-pose-based measures. In
addition, eyes-off-road proportion had more positive and higher
correlations than did eye gaze gamma, and head-off-road proportion had
more positive and higher correlations than did head pose gamma.

Although many research-intent driver monitoring technologies are able to
monitor eye gaze, detecting eye-gaze is a difficult task using
affordable automotive-grade technology. Figure 7.21 demonstrates how
head pose and eye-gaze measures correlate with the driver performance
measures: accelerator release time (ART) and standard deviation of lane
position (SDLP)^7^. Although the eye-gaze measures are able to capture
slightly more of the variance than head-pose measures, head pose still
provides a good estimate of driver distraction, especially for SDLP.
Furthermore, it is likely that non-forward glances that cannot be
detected using head pose only, are likely to be less severe than
non-forward glances that produce a detectable deviation in head pose
too. For either prolonged or large-eccentricity head poses, because it
is more comfortable, most drivers are likely to use a combination of
head and eye movements to direct their gaze to the target. Therefore,
because monitoring the head pose is more feasible from a technology
perspective than monitoring eye gazes, and because monitoring the head
pose is likely to be able to detect the worst-case visual distractions,
the SAVE-IT program utilized the head-pose-monitoring DSM system.

![](media/image24.png){width="6.497222222222222in"
height="3.783333333333333in"}

Figure 7.21 Correlations between head pose or eye gaze and performance
measures: Accelerator Release Time (ART) and Standard Deviation of Lane
Position (SDLP).

In this experiment, these glance measures were computed over a 4.3-s
time window. It is simpler to implement this time window in a binary
computing environment that is running at 30 Hz such as Delphi\'s Driver
State Monitor (Edenborough, Hammoud, et al., 2004). This time window
seems to strike a reasonable balance between timeliness and reliability.

For eye gaze gamma and head pose gamma, it seems important to use a
small scaling factor for the instantaneous eye glace (or head pose).
With a small scaling factor (e.g., \<0.6), accelerator release reaction
time increases with eye gaze gamma and head pose gamma monotonically.
With a larger scaling factor, the monotonic pattern disappears. Because
accelerator release reaction time is markedly longer when eye gaze gamma
or head pose gamma exceeds 2, a value of 2 is a reasonable threshold.
When eye gaze gamma or head pose gamma exceeds this threshold,
distraction feedback should be provided to re-direct more of a driver\'s
attention to the forward road.

Averaging across seven distraction conditions (excluding the forward
distraction conditions), both eyes-off-road proportion and head-off-road
proportion yield an increase in accelerator release reaction time. The
increase was small (150 ms or smaller) when the proportion was 0.5 or
lower, and considerable (400 ms or larger) when the proportion exceeded
0.5. This reaction time pattern suggests that the threshold for both
eyes-off-road proportion and head-off-road proportion be set around 0.5.
Distraction feedback should be provided to drivers when eyes-off-road or
head-off-road proportion exceeds 0.5 to re-direct driver attention to
the forward road.

It is important that distraction feedback does not create a new source
of distraction. If a driver is looking forward already, it may be
annoying and of little benefit to provide distraction feedback even if
eyes-off-road or head-off-road proportion exceeds a threshold value.
Therefore, distraction feedback should be provided only if eyes-off-road
or head-off-road proportion exceeds a threshold and instantaneous eye
gaze or head pose is non-forward. This experiment has shown that
accelerator release reaction time was larger when eye gaze or head pose
is non-forward than forward. If it is done carefully, distraction
feedback will reduce eyes-off-road or head-off-road glance and speed up
reaction times to lead vehicle braking events and enhance traffic
safety.

This experiment has been carried out to validate diagnostic measures of
visual distraction. Several promising glance measures have been
identified. This experiment does not provide any distraction feedback to
evaluate the safety effectiveness and driver acceptance issues.
Preliminary assessments have been performed in SAVE-IT Task 4 (Donmez,
Boyle, Lee, & McGehee, 2004b). These issues will be further investigated
in subsequent tasks in the SAVE-IT program (Task 14).

# 7.5. References

Boer, E. R. (2001). Behavioral entropy as a measure of driving
performance.

*Proceedings of the First International Driving Symposium on Human
Factors in*

> *Driver Assessment, Training and Vehicle Design,* 225-229.

Donmez, B., Boyle, L., Lee, J. D., & McGehee, D. V. (2004a). *SAfety
VEhicles using*

*adaptive Interface Technology (Task 4): A Literature Review of
Distraction*

*Mitigation Strategies*.
<http://www.volpe.dot.gov/opsad/saveit/docs.html>.

Donmez, B., Boyle, L., Lee, J. D., & McGehee, D. V. (2004b). SAfety
VEhicles using

adaptive Interface Technology (Task 4): Experiments for Distraction
Mitigation

Strategies. <http://www.volpe.dot.gov/opsad/saveit/docs.html>.

Donmez, B., Boyle, L., Lee, J. D., & Scott, G. (2005). *Assessing
differences in young*

*drivers' engagement in distractions*. Unpublished manuscript.

Edenborough, N., Hammoud, R., Harbach, A., Ingold, A., Kisačanin, B.,
Malawey, P.,

Newman, T., Scharenbroch, G., Skiver, S., Smith, M., Wilhelm, A., Witt,
G.,

Yoder, E., & Zhang, H. (2004). Drowsy driver monitor from Delphi.
*Proceedings*

*of IEEE CVPR 2004*, Washington, D.C., June 2004.

Hart, S. G., & Staveland, L. E. (1988). Development of NASA-TLX (Task
Load Index):

Results of experimental and theoretical research. In P. A. Hancock & N.
Meshkati

> (Eds.), *Human mental workload* (pp. 139-183). Amsterdam: North
> Holland.

Heinzmann, J., & Zelinsky, A. (1998). 3-D facial pose and gaze point
estimation using a robust real-time tracking paradigm (pp. 142-147).
*Proceedings of the*

*International Conference on Automatic Face and Gesture Recognition.*

Horrey, W. J., & Wickens, C. (2004a). *Driving and side task
performance: The effects of*

*display clutter, separation, and modality*. Technical Report
AHFD-02-13/GM-02-

2\.

Horrey, W. J., & Wickens, C. (2004b). *The impact of cell phone
conversations on*

*driving: A meta-analytic approach*. Technical Report
AHFD-04-02/GM-04-1.

Lee, J. D., Caven, B., Haake, S., & Brown, T. (2001). Speech-based
interaction with in-

vehicle computers: The effect of speech-based e-mail on drivers\'
attention to the

roadway. *Human Factors, 43,* 631-640.

Lee, J. D., McGehee, D. V., Brown, T. L., & Reyes, M. (2002). Collision
warning timing,

driver distraction, and driver response to imminent rear-end collisions
in a high-

fidelity driving simulator. *Human Factors, 44,* 314-334.

Lee, J., Reyes, M., Smyser, T., Liang, Y., & Thornburg, K. (2004).
SAfety VEhicles

using adaptive Interface Technology (Task 5). Final Report: Phase 1.

<http://www.volpe.dot.gov/opsad/saveit/docs/dec04/finalrep_5b.pdf>.

Nakayama, O., Futami, T., Nakamura, T., & Boer, E. R. (1999).
Development of a

steering entropy method for evaluating driver workload. *SAE
1999-01-0892*.

Recarte, M. A., & Nunes, L. M. (2000): Effects of verbal and
spatial--imagery tasks on

eye fixations while driving. *Journal of Experimental Psychology:
Applied*

*, 6,*

31-43

52. .

Rensink, R. A., O\'Regan, J. K., & Clark, J. J. (1997). To see or not to
see: The need for

attention to perceive changes in scenes. *Psychological Science, 5,*
368-373.

Victor, T., Blomberg, O., & Zelinsky, A. (2001). Automating driver
visual. In A. G. Gales,

et al. (Editors). *Vision in Vehicles--VIIII*, Elsevier Science
Publishers B. V. (North

Holland).

Wang, J.-S., Knipling, R. R., & Goodman, M. J. (1996). The role of
driver inattention in

crashes; New statistics from the 1995 crashworthiness data system (pp.
377-

39). *40th Annual Proceedings of the Association for the Advancement of*

*Automotive Medicine.* Vancouver, British Columbia.

Zhang, H. & Smith, M. (2004a). *SAfety VEhicles using adaptive Interface
Technology*

> *(Task 7): A literature review of visual distraction research*.
> <http://www.volpe.dot.gov/opsad/saveit/docs.html>.

Zhang, H. & Smith, M. (2004b). *A Final Report of SAfety VEhicles using
adaptive*

> *Interface Technology (Phase I: Task 7): Visual Distraction Research*.
> <http://www.volpe.dot.gov/opsad/saveit/docs.html>.

Zhang, H**.**, Smith, M. R. H., & Witt, G. J. (2006). Identification of
real-time diagnostic

measures of visual distraction with an automatic eye tracking system.
*Human*

*Factors, 48,* 805-821.

[^1]: Forward is defined as 24x24 deg^2^ around the focus of expansion.