**SAfety VEhicles using adaptive**

**Interface Technology**

**(Task 4)**

**Final Report: Phase 2**

**Distraction Mitigation Evaluation**

Prepared by

Birsen Donmez

Linda N. Boyle

John D. Lee

The University of Iowa

Phone: (319) 384-0810

Email: <john-d-lee@uiowa.edu>

February 2007

Table of Contents

[4.1 Executive summary 7](#executive-summary)

[4.2 Program Overview 10](#program-overview)

[4.3 Introduction and objectives 11](#introduction-and-objectives)

[4.4 Designing feedback to mitigate distraction
11](#designing-feedback-to-mitigate-distraction)

[4.4.1 Feedback for distraction mitigation
13](#feedback-for-distraction-mitigation)

[4.4.2 Timescales of feedback 14](#timescales-of-feedback)

[4.2.4.1 Concurrent feedback 18](#concurrent-feedback)

[4.2.4.2 Delayed feedback 18](#delayed-feedback)

[4.2.4.3 Post-hoc feedback 20](#post-hoc-feedback)

[4.2.4.4 Collective Feedback 21](#collective-feedback)

[4.4.3 Combinations of different feedback timescales
21](#combinations-of-different-feedback-timescales)

[4.4.4 Interaction of feedback timing and feedback content
22](#interaction-of-feedback-timing-and-feedback-content)

[4.4.5 Conclusion 24](#conclusion)

[4.5 Drivers' Attitudes towards imperfect distraction mitigation
24](#drivers-attitudes-towards-imperfect-distraction-mitigation)

[4.5.1 Focus groups to explore acceptance and trust
26](#focus-groups-to-explore-acceptance-and-trust)

[4.5.2 Simulator experiments to assess acceptance and trust
28](#simulator-experiments-to-assess-acceptance-and-trust)

[4.5.3 Methodology 28](#methodology)

[4.3.5.1 Participants 28](#participants)

[4.3.5.2 Apparatus 29](#apparatus)

[4.3.5.3 Experimental Design
29](#experimental-design-and-independent-variables)

[4.3.5.4 Procedure 30](#procedure)

[4.3.5.5 Dependent variables 31](#dependent-variables)

[4.5.4 Results 31](#results)

[4.5.5 Discussion 34](#discussion)

[4.6 A realtime Index of visual distraction
35](#a-realtime-index-of-visual-distraction)

[4.6.1 Methodology 37](#methodology-1)

[4.1.6.1 Participants 37](#participants-1)

[4.1.6.2 Apparatus 38](#apparatus-1)

[4.1.6.3 Driving task 39](#driving-task)

[4.1.6.4 In-vehicle information system task
39](#in-vehicle-information-system-task)

[4.1.6.5 Experimental design and independent variables
40](#experimental-design-and-independent-variables-1)

[4.1.6.6 Procedure 40](#procedure-1)

[4.1.6.7 Dependent variables 40](#dependent-variables-1)

[4.6.2 Results 41](#results-1)

[4.6.3 Discussion 45](#discussion-1)

[4.7 Concurrent feedback to Mitigate Distraction
47](#concurrent-feedback-to-mitigate-distraction)

[4.7.1 Methods 49](#methods)

[4.1.7.1 Participants 49](#participants-2)

[4.1.7.2 Apparatus 49](#apparatus-2)

[4.1.7.3 Driving task 50](#driving-task-1)

[4.1.7.4 In-vehicle information system task
50](#in-vehicle-information-system-task-1)

[4.1.7.5 Experimental design and independent variables
51](#experimental-design-and-independent-variables-2)

[4.1.7.6 Dependent variables 51](#dependent-variables-2)

[4.7.2 Results 53](#results-2)

[4.7.3 Discussion 60](#discussion-2)

[4.8 feedback at different timescales: Concurrent and post-Hoc Feedback
62](#feedback-at-different-timescales-concurrent-and-post-hoc-feedback)

[4.8.1 Methods 63](#methods-1)

[4.1.8.1 Participants 63](#participants-3)

[4.1.8.2 Apparatus 63](#apparatus-3)

[4.1.8.3 Driving Task 64](#driving-task-2)

[4.1.8.4 Experimental design and independent variables
64](#experimental-design-and-independent-variables-3)

[4.1.8.5 Procedure 66](#procedure-2)

[4.1.8.6 Dependent Variables 66](#dependent-variables-3)

[4.8.2 Results 67](#results-3)

[4.2.8.1 Response to lead vehicle braking
67](#response-to-lead-vehicle-braking)

[4.2.8.2 Interaction with in-vehicle display: Eye movements and button
presses
72](#interaction-with-in-vehicle-display-eye-movements-and-button-presses)

[4.2.8.3 Subjective measures 75](#subjective-measures)

[4.8.3 Discussion 79](#discussion-3)

[4.9 References 80](#references)

Table of Figures

[Figure 4. 1. Levels of feedback timing and the magnitude of targeted
influence (indicated by arrow stroke width).
13](#__RefHeading___Toc160355579)

[Figure 4. 2. The process by which a driver responds to feedback at
different timescales. 14](#__RefHeading___Toc160355580)

[Figure 4. 3. The visual advising strategy.
30](#__RefHeading___Toc160355581)

[Figure 4. 4. Acceptance of mitigation strategies by age group and
presentation modality. 32](#__RefHeading___Toc160355582)

[Figure 4. 5. Trust on mitigation strategies by system adaptation, age
group, and presentation modality. 33](#__RefHeading___Toc160355583)

[Figure 4. 6. Acceptance of mitigation strategies embedded in current
in-vehicle systems 34](#__RefHeading___Toc160355584)

[Figure 4. 7. In-vehicle display, road scene and eye-tracking cameras
38](#__RefHeading___Toc160355585)

[Figure 4. 8. In-vehicle task displayed on the LCD monitor
40](#__RefHeading___Toc160355586)

[Figure 4. 9. Accelerator release time and transition times for
different experimental conditions and risk levels
42](#__RefHeading___Toc160355587)

[Figure 4. 10. Minimum acceleration for different experimental
conditions and risk levels 43](#__RefHeading___Toc160355588)

[Figure 4. 11 Duration of glances on the in-vehicle display for risky
and non-risky driver categories 44](#__RefHeading___Toc160355589)

[Figure 4. 12. Number of alarms for different alarm thresholds for
different risk levels 45](#__RefHeading___Toc160355590)

[Figure 4. 13. Acceptance of mitigation strategies by age group and
presentation modality. 52](#__RefHeading___Toc160355591)

[Figure 4. 14. Estimated means and 95% confidence intervals for curve
negotiation measures: (a) curve entry speed and (b) steering entropy.
55](#_Ref121981118)

[Figure 4. 15. Estimated means and 95% confidence intervals for lead
vehicle braking event response measures: (a) Speed when lead vehicle
starts to brake (b) Accelerator release time (c) Minimum acceleration
(negative maximum deceleration) (d) Minimum time to collision for each
condition. 56](#_Ref126546205)

[Figure 4. 16. Estimated means and 95% confidence intervals for (a)
number of glances (per minute) on the in-vehicle display and (b) mean
duration of roadway-glances. 57](#__RefHeading___Toc160355594)

[Figure 4. 17. Estimated mean glance duration (and 95% confidence
interval) on the in-vehicle display after (a) γ \` is exceeded and (b)
 γ \`\` is exceeded. 58](#__RefHeading___Toc160355595)

[Figure 4. 18. Subjective responses (estimated means and 95% confidence
intervals) for (a) perceived risk, (b) mental effort, and (c) acceptance
of distraction-feedback. 59](#__RefHeading___Toc160355596)

[Figure 4. 19. Trip-report (a) positive feedback for no incidents (b)
overview when there are incidents (c) detailed information on the
incident 65](#__RefHeading___Toc160355597)

[Figure 4. 20. Incident visualization: (a) speeding, (b) too close to
lead vehicle, (c) lane deviation, (d) collision with lead vehicle, (e)
collision with oncoming vehicle 66](#__RefHeading___Toc160355598)

[Figure 4. 21. Reaction to lead vehicle braking events (estimated means
and standard error bars) (a) accelerator release time (b) brake reaction
time 70](#__RefHeading___Toc160355599)

[Figure 4. 22. Safety outcomes for lead vehicle braking event response
(estimated means and standard error bars) (a) minimum TTC (b) minimum
acceleration 71](#__RefHeading___Toc160355600)

[Figure 4. 23. Eye-movements (estimated means and standard error bars)
(a) number of glances per minute on in-vehicle display (b) glance
duration on in-vehicle display (c) glance duration on the road
73](#__RefHeading___Toc160355601)

[Figure 4. 24. Number of button presses per minute (estimated means and
standard error bars) 75](#__RefHeading___Toc160355602)

[Figure 4. 25. Subjective measures (estimated means and standard error
bars) (a) perceived risk (b) mental effort
76](#__RefHeading___Toc160355603)

[Figure 4. 26. Acceptance of feedback (estimated means and 95%
confidence intervals). 78](#__RefHeading___Toc160355604)

Table of Tables

[Table 4. 1. Taxonomy of distraction mitigation strategies (Phase 1).
7](#__RefHeading___Toc160304991)

[Table 4. 2. Potential pros and cons for different feedback timescales.
16](#__RefHeading___Toc160304992)

[Table 4. 3. Taxonomy of distraction mitigation strategies.
27](#__RefHeading___Toc160304993)

[Table 4. 4. Decrement in accelerator release time from the
non-distracted to the distracted condition.
42](#__RefHeading___Toc160304994)

[Table 4. 5. Analyses of variance (F-tests) for braking response.
53](#__RefHeading___Toc160304995)

[Table 4. 6. Analyses of variance (F-tests) for eye-movements.
54](#__RefHeading___Toc160304996)

[Table 4. 7. Analyses of variance (F-tests) for questionnaire responses.
54](#__RefHeading___Toc160304997)

[Table 4. 8. Subjective responses to whether or not performance of
feedback enhanced their driving performance.
60](#__RefHeading___Toc160304998)

[Table 4. 9. Incident severity levels. 65](#__RefHeading___Toc160304999)

[Table 4. 10. Significant pair-wise comparisons for lead vehicle braking
response. 68](#__RefHeading___Toc160305000)

[Table 4. 11. Significant pair-wise comparisons for interaction with
in-vehicle display. 74](#__RefHeading___Toc160305001)

[Table 4. 12. Significant pair-wise comparisons for subjective measures.
77](#__RefHeading___Toc160305002)

[Table 4. 13. Subjective responses relating to driving performance.
79](#__RefHeading___Toc160305003)

# Executive summary

The objective of Task 4 (Distraction Mitigation) is to develop
countermeasures that mitigate distraction in a way that drivers find
acceptable.

In Phase 1, a literature review was conducted that generated a taxonomy
of distraction mitigation strategies (Table 4. 1) (Donmez, Boyle, & Lee,
2003). This taxonomy provided a classification scheme based on
dimensions identified as particularly relevant to distraction
mitigation---automation, initiation type, and the task being modulated
by the strategy. Second, the classification helped identify current gaps
in research and areas where additional strategies were needed. Focus
groups were then conducted to assess drivers' acceptance of and trust in
all the strategies (existing and innovative) as defined in the taxonomy.
A driving simulator study was conducted to further assess the
effectiveness, acceptance, and trust of two of the more promising
strategies: an advising strategy that warns drivers of roadway events
and a locking strategy that prevents the driver from continuing a
distracting task during a roadway event (Donmez, Boyle, & Lee, 2006a,
2006b).

[]{#__RefHeading___Toc160304991 .anchor}Table 4. 1. Taxonomy of
distraction mitigation strategies (Phase 1).

  ---------------- ------------- ------------ -------------- ---------------
  LEVEL OF         DRIVING                    NON DRIVING    
  AUTOMATION       RELATED                    RELATED        
                   STRATEGIES                 STRATEGIES     

                   *System       *Driver      *System        *Driver
                   Initiated*    Initiated*   Initiated*     Initiated*

  High             Intervening   Delegating   Locking &      Controls
                                              Interrupting   Presetting

  Moderate         Warning       Warning      Prioritizing & Place-keeping
                                 Tailoring    Filtering      

  Low              Informing     Perception   Advising       Demand
                                 Augmenting                  Minimizing
  ---------------- ------------- ------------ -------------- ---------------

In the second phase of this project the taxonomy in Table 4. 1 was
extended by considering how mitigation might be used to provide drivers
with feedback at different timescales. Figure 4. 1 shows how such
feedback might not only mitigate the effects associated with the
immediate driving performance impairment that distraction can cause, but
feedback can also mitigate distraction by introducing long-term changes
in driver behavior. A specific example of this feedback includes
post-hoc feedback that the driver might receive at the end of the drive.

![](media/image1.wmf){width="4.895138888888889in"
height="3.7368055555555557in"}

Figure 4. 1. Feedback at different timescales as strategies to mitigate
distraction.

Several experiments have evaluated the strategies identified in Table 4.
1 and Figure 4. 1. The first of these included two age groups: old
(65-75) and middle-aged (35-55) and investigated the effects of two
modalities (visual and auditory). Visual modality was implemented as a
red bezel on the in-vehicle display. Auditory modality was implemented
as a background clicking sound. Interestingly, the focus group and the
experimental findings suggest that older drivers accepted and trusted
the strategies more than middle-aged drivers. Regardless of age, all
drivers preferred strategies that provided alerts in a visual mode
rather than an auditory mode. When the system falsely adapted to the
road situation, trust in the strategies declined. The findings show that
display modality has a strong effect on driver acceptance and trust, and
that older drivers are more trusting and accepting of distraction
mitigation technology even when it operates imperfectly. In terms of
driving performance measures, distraction was a problem for both age
groups. Visual distractions were more detrimental than auditory ones for
curve negotiation as reflected by more erratic steering. Drivers did
brake more abruptly under auditory distractions, but this effect was
mitigated by both the advising and locking strategies. Regardless of
driver's age, both strategies resulted in longer minimum
time-to-collision under auditory distractions. The locking strategy also
resulted in longer minimum time-to-collision for middle-aged drivers
engaged in visual distractions.

A disadvantage of these strategies, when based on roadway events, is the
inability to effectively warn drivers of prolonged engagement in the
IVIS. Some distractions may degrade driving performance to safety
critical levels even on straight roads with low levels of traffic.
Providing feedback when the driver is highly distracted can help avoid
future hazardous maneuvers. Another disadvantage of mitigation
strategies based only on the roadway state concerns non-useful alarms.
Although there is a roadway event, such as a curve, the driver may
actually be focused on the driving task and be able to respond quite
appropriately. An alarm provided in this situation can degrade system
acceptance and result in frustration, which itself is a type of
distraction that can have a negative effect on traffic safety (Burns &
Lansdown, 2000). This can be avoided by giving drivers feedback based on
their attentional state rather than just the roadway state. Moreover,
compared to warnings based on roadway events, which can have an impact
on immediate performance, feedback on driver's engagement in
distractions can generate a long-term behavioral change.

To support warnings based on driver state, an experiment was conducted
to develop an algorithm to identify risky visual scanning patterns. The
algorithm defined the degree of distraction as a function of the current
off-road glance duration, *β*~1~, and the total off-road glance duration
during the last 3 sec, *β*~2~, with the relative influence of the
current glance duration as *α*.  A 3 sec moving average of glance
duration has been shown to predict distraction (Zhang & Smith., 2004).
These factors then defined a momentary value of distraction, *γ*, for
the algorithm:$\gamma = \text{αβ}_{1} + (1 - \alpha)\beta_{2}$.

This algorithm was used in a subsequent driving simulator study which
was conducted to assess whether real-time feedback on a driver's state
can influence the driver's interaction with in-vehicle information
systems (IVIS). A driving simulator experiment was designed to test
real-time feedback that alerts drivers based on their off-road eye
glances. Feedback was based on the algorithm that identified risky
visual scanning patterns. Proposed two-tier feedback used a threshold,
*γ *\`, of 2 sec for a less salient alarm, and *γ *\`\`, of 2.5 sec for
a more salient alarm with *α* of 0.2. The parameters *γ *\`, *γ *\`\`,
and *α*,  were chosen based on the results of the preliminary
experiment. Feedback was displayed in two display locations
(vehicle-centered, and IVIS-centered) to 16 young (18-21) and 13
middle-aged drivers (35-55). For IVIS-centered distraction-feedback, a
yellow strip appeared on the top portion of the display for the less
salient alarm if the 2 sec threshold was exceeded. If the 2.5 sec
threshold was exceeded, then orange strips also appeared on both sides
of the yellow strip to create the more salient alarm. Once the driver's
gaze switched back to the road, the alerts disappeared after 1 sec. The
same logic was used for the vehicle-centered distraction-feedback for
which feedback was provided through an LED strip on the dashboard.

In this experiment, distraction was observed as problematic for both age
groups with delayed responses to a lead vehicle braking event as
indicated by delayed accelerator releases. Significant benefits were not
observed for braking and steering behavior for this experiment, but
there was a significant change in drivers' interaction with IVIS. When
given feedback, drivers looked at the in-vehicle display less frequently
regardless of where feedback was displayed in the vehicle. Drivers also
had positive attitudes about feedback. This indicates that real-time
feedback based on the driver state can positively alter driver's
engagement in distracting activities, helping them attend better to the
roadway.

This showed that concurrent feedback based on driver's attentional state
can positively alter driver's willingness to engage in distracting
activities. To build on the potential of distraction mitigation
technology to change long-term behavior rather than just momentary
behavior, another experiment has been conducted to assess the effects of
providing post-hoc feedback to drivers once a trip is completed. This
type of feedback focuses on influencing driver behavior (e.g., learning
what constitutes safe driving, when to engage in distractions, what
speed to maintain in different driving conditions, and how to diminish
risk in planning a trip). It is hypothesized that post-hoc feedback can
help the drivers understand unsafe behavior that can lead to hazardous
situations better than concurrent feedback. The main objective of this
experiment was to assess the effects of post-hoc and combined concurrent
and post-hoc feedback on performance and behavior. Forty-nine young
participants between the ages of 18-21 completed the study.

This study showed that post-hoc and combined concurrent and post-hoc
feedback both resulted in significantly faster reaction to lead vehicle
braking events. In terms of driving performance measures, no significant
differences were found between the two feedback types. In general, both
post-hoc and combined feedback enhanced driving performance.

As drivers completed more drives, their glance duration to the
in-vehicle display increased and their glance duration on the road
decreased. This suggests that drivers became more comfortable performing
the task. However, task baseline had a larger increase in glance
duration from first to last drive when compared to both post-hoc and
combined feedback. This suggests that both of these feedback types can
induce a positive behavior in terms of how long the drivers look at the
in-vehicle display. Moreover, combined feedback resulted in longer
on-road glances. Even if there were no significant results for specific
drives across conditions, there seems to be a decline in the benefits of
combined feedback over time with regard to driver engagement in
distracting activities. The long term effects of these feedback types
merit further research. Drivers also accepted both post-hoc and combined
feedback. The post-hoc feedback, which is included in both feedback
conditions, was found to be useful and satisfactory. Concurrent
feedback, which is only a part of combined feedback, was perceived to be
useful.

Mitigation strategies based on roadway state, in the form of advising or
locking, can enhance immediate driving performance. Moreover, providing
feedback on driver's attentional state, where drivers receive feedback
based on driver behavior rather than roadway state can positively change
drivers' engagement with distracting tasks. Providing feedback at the
end of the drive also shows substantial promise. Simulator experiments
suggest that drivers will also accept such feedback, but additional data
collection is needed to assess whether drivers will find such strategies
acceptable over years of day-to-day use. Overall, these results suggest
that strategies to mitigate driver distraction might provide the largest
benefit if they consider mitigating the immediate effects of distraction
and guiding the long-term behavior of drivers.

# 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.

# Introduction and objectives

The objective of Task 4 (Distraction Mitigating) was to develop
approaches to help drivers avoid distraction or diminish the negative
effects of distraction. Our specific objective was to define mitigation
strategies, evaluate the effect of these strategies on driver
performance, and assess factors influencing driver acceptance of the
strategies.

The following sections first present an extension to the taxonomy of
mitigation strategies developed in the first phase of this project. This
extension considers the timing of feedback that might be used to
mitigate distraction. This timing can range from concurrent with the
distracting activity to a collective summary presented days or weeks
after the distracting activity occurs. Following this theoretical
discussion, the report includes several experiments to assess driver
response to imperfect mitigation systems, development of a real-time
distraction estimate based on eye glance behavior, and the influence of
concurrent and post-hoc feedback.

# Designing feedback to mitigate distraction

A three-dimensional taxonomy was developed in the first phase of the
SAVE-IT program to define different distraction mitigation strategies
that mainly focused on enhancing immediate driving performance (Donmez
et al., 2003; Donmez, Boyle et al., 2006a, 2006b; Donmez, Boyle, Lee, &
Scott, 2006). These dimensions include the degree of automation of the
mitigation strategy, the type of initiation, and the type of task that
is being modulated by the strategy. The degree of automation can range
from a simple driver alert signal to complete system control. These
automation levels can be initiated either by the driver or the
automation and can vary based on type of task (the driving task or the
secondary task) that is being modulated by the strategy.

This section describes another dimension in designing mitigation
strategies, the temporal dimension, which considers the immediate effect
of the system on driving performance as well as the long-term effect on
drivers' willingness to engage in a distracting activity. The
willingness to engage in a distraction has a substantial influence on
safety. However, drivers may not always realize the potential hazards
created from decisions engage in a distracting activity and may not
always make the safest choice. Therefore, there is a need to develop
design strategies that mitigate driver distraction by improving
immediate driving performance and inducing behavioral change to enhance
long-term driving habits.

When the temporal dimension is not considered, mitigation strategies
typically take the form of concurrent feedback which can be effective
for enhancing immediate driving performance but may not lead to
long-term behavioral change. For example, the drivers may become more
comfortable engaging in a phone conversation if they start depending on
a system to be the primary agent in identifying a hazardous situation.
To improve immediate driving performance as well as long-term behavior,
feedback can be provided not just concurrently but at various
timescales. Feedback on previous incidents or crashes which occurred as
a result of cell phone conversations can lead to less frequent cell
phone use while driving. If feedforward control is also incorporated
within the mitigation system, the system can delay or discontinue
distracting tasks in anticipation of high demand traffic situations.
When the distraction mitigation is viewed in the control theory
framework, distraction becomes an issue of poor feedback and feedforward
control at several timescales.

This section describes feedback timing on a continuum that can be
classified into four major timescales: concurrent (milliseconds),
delayed (seconds), post-hoc (minutes, hours), and collective (days,
weeks, months). The proposed framework, which is shown in Figure 4. 2,
suggests that as feedback moves forward in the time continuum, the main
objective will change from improving immediate driving performance to
inducing safer driver behavior. For example, concurrent feedback can
have immediate effects on the performance of a driver who is departing
their lane and needs to discontinue a cell phone conversation.
Collectively presenting the frequency of lane departures over the past
month resulting from cell phone conversations can decrease driver's
willingness to engage in future cell phone conversations while driving.
However, this collective feedback may not necessarily have an immediate
impact on driver's lane keeping performance the next time the driver is
using the cell phone.

![](media/image1.wmf){width="4.895138888888889in"
height="3.7368055555555557in"}

[]{#__RefHeading___Toc160355579 .anchor}Figure 4. 2. Levels of feedback
timing and the magnitude of targeted influence (indicated by arrow
stroke width).

## Feedback for distraction mitigation

Driver behavior (e.g., what the driver in fact does do) is not
necessarily correlated with driver performance (e.g., perceptual and
motor skills, what the driver can do) but needs to be evaluated in
relation to each other. For example, driver performance is probably at
an optimum in young people, but among this same age group, driver
behavior leads to high levels of risk. Driver behavior can also differ
by age, gender, ethnicity, and varies over time, and is influenced by
social and cultural norms (Lajunen, Hakkarainen, & Summala, 1996).
Mitigating distraction by feedback can enhance the driver's immediate
performance as well as induce behavioral change. Enhancing immediate
driver performance with feedback is, therefore, distinguished from the
goal of promoting safer driving behavior that can only be accomplished
over time. Moreover, the enhancement in immediate performance may not
necessarily be sustained once feedback is removed unless feedback can
result in a behavioral change by updating the driver's internal model
for safe driving.

Feedback is essential for responding appropriately in an adaptive
environment. From the system-design perspective, feedback is the
information available to the operator regarding the state of the joint
operator-machine system. Figure 4. 3 characterizes the driver response
to feedback in five stages. Four of these stages (i.e., intention,
perception, cognition, and action) and the disturbances to and from the
physical/cognitive distracters are based on Sheridan (2004) who
indicates that the intention stage creates a "priority-ordered sequence
of near-term driving goals" and excludes driver intentions to engage in
distracting activities. In Sheridan's framework, distraction is seen as
perturbations to other stages such as to sensing (i.e. perception) and
deciding (i.e. cognition). However, driver intentions set goals for both
the driving and the non-driving tasks. Figure 4. 3 captures this by
creating a bi-directional connection between the intention stage and the
distracters, suggesting that distractions can also be guided by the
driver. Moreover, distracters can also create disturbances to driver's
intentions, as well as to perception, cognition and actions. Sheridan
(2004) also assumes that the basic intention of the driver is to drive
safely regardless of additional tasks undertaken while driving. However,
the definition of safe driving can change from one person to another and
can also change over time. To capture this effect, a fifth stage is
included in the model to represent the internal model of safe driving
(i.e. driver's belief of acceptable behavior while driving).
Interactions also exist among these five stages. For example, the
driver's perception of the environment can update intentions; and
cognition can guide perception by directing attention to different
aspects of the environment.

![](media/image2.wmf){width="6.572222222222222in"
height="4.127777777777778in"}

[]{#__RefHeading___Toc160355580 .anchor}Figure 4. 3. The process by
which a driver responds to feedback at different timescales.

## Timescales of feedback

Feedback can be provided to operators at different points along a
continuous timescale. Defining different levels for this continuum can
help us understand how different timescales can affect a driver's
distraction level, immediate performance, and behavior. Four levels
defined in this framework include concurrent (milliseconds), delayed
(seconds), post-hoc (minutes, hours), and collective (days, weeks,
months) (Figure 4. 2). The main objective of feedback is to help drivers
at all phases of driving: from improving immediate driving performance
to inducing safer driver behavior.

Feedback can be inherent to the system (e.g. engine sound), or it can be
designed by the system developer to provide the necessary information to
the operator. Because driving is a closed loop response, driver's
immediate performance (e.g. lane position, speed) is available to the
driver as inherent feedback. That is, the driver can extract this
information by sampling the roadway. Concurrent, delayed, post-hoc, and
collective feedback are functions that can be designed in a distraction
mitigation system. Inherent or not, feedback can correct errors, help
learning, help operators monitor system state, and change a person's
behavior. In the driving environment, changes can occur very rapidly,
and the driver may fail to track these changes, particularly if the
driver's attention is directed towards a non-driving related activity or
if the driver is cognitively loaded (Haigney & Westerman, 2001; Horrey &
Wickens, 2006; Lee, Caven, Haake, & Brown, 2001). In such situations,
feedback can help the driver respond to environmental changes more
appropriately. Feedback can provide a warning to the driver based on a
hazardous situation (e.g. lane deviation warning), can help the driver
learn what is unsafe (e.g. failure to reduce speed during bad weather
conditions), and can ultimately alter driver behavior (e.g. inhibiting
knowingly risky behavior such as speeding). Different feedback
characteristics, such as positive or negative, and concurrent or lagged
(e.g. delayed, post-hoc, or collective) feedback timing, can facilitate
these outcomes differently. Therefore, this chapter describes different
characteristics of feedback, and the implications of these
characteristics for enhancing performance and modifying driver behavior.

There has not been much research on post-hoc, collective or delayed
feedback. Most of the literature focuses on concurrent feedback, which
mostly aims to improve immediate performance. Categorizing feedback into
different timescales presents potential areas of research for behavioral
changes. The pros and cons for all four feedback timescales are
summarized in Table 4. 2 and are further discussed in this section.

[]{#__RefHeading___Toc160304992 .anchor}**Table 4. 2. Potential pros and
cons for different feedback timescales.**

+------+------------------------------+-------------------------------+
| *    | **Pros**                     | **Cons**                      |
| *Tim |                              |                               |
| esca |                              |                               |
| le** |                              |                               |
+======+==============================+===============================+
| Co   | Immediate implications for   | Driver may adapt to feedback  |
| ncur | enhancing driving            | inappropriately               |
| rent | performance when feedback is |                               |
|      | present                      | May elevate the level of      |
| feed |                              | cognitive distraction if the  |
| back | Can help the driver learn    | feedback is not intuitive to  |
|      | safe maneuvers (e.g. safe    | the driver                    |
|      | following distance)          |                               |
|      |                              | Low acceptance can lead to    |
|      |                              | disuse of feedback            |
|      |                              |                               |
|      |                              | Overreliance on feedback can  |
|      |                              | result in dangerous           |
|      |                              | situations if feedback fails  |
|      |                              |                               |
|      |                              | Can interfere with immediate  |
|      |                              | task performance              |
|      |                              |                               |
|      |                              | Unexpected lags can undermine |
|      |                              | the effect of feedback        |
|      |                              |                               |
|      |                              | Deterioration of productivity |
|      |                              | (i.e. IVIS task performance)  |
+------+------------------------------+-------------------------------+
| Del  | Informs the driver about     | Feedback is not provided at   |
| ayed | correct and incorrect        | the time of the incident and  |
|      | driving behavior while       | can therefore not enhance     |
| feed | avoiding cognitive overload  | immediate driving performance |
| back |                              |                               |
|      | Can enhance driving          | Unexpected lags can undermine |
|      | performance during a trip    | the effect of feedback        |
|      | for upcoming events          |                               |
|      |                              | Deterioration of productivity |
|      | Can help the driver learn    | (i.e. IVIS task performance)  |
|      | safe maneuvers (e.g. safe    |                               |
|      | following distance)          |                               |
+------+------------------------------+-------------------------------+
| Post | Intentions leading to unsafe | Feedback is not provided at   |
| -hoc | driving behavior can be      | the time of the incident and  |
|      | explained to the driver      | can therefore not enhance     |
| feed | without cognitive overload   | immediate driving performance |
| back |                              |                               |
|      | Can enhance driving          | Requires the driver to be an  |
|      | performance for future trips | active recipient of           |
|      |                              | information                   |
|      | Can refresh drivers' memory  |                               |
|      | on performance for the       | Driver may fail to link       |
|      | completed trip               | feedback with incident        |
|      |                              |                               |
|      | Can calibrate driver's       | Low acceptance may lead to    |
|      | subjective performance by    | disuse                        |
|      | presenting a connection      |                               |
|      | between intentions and       |                               |
|      | events that occurred during  |                               |
|      | a trip                       |                               |
+------+------------------------------+-------------------------------+
| Co   | Intentions leading to unsafe | Feedback is not provided at   |
| llec | driving behavior can be      | the time of the incident and  |
| tive | explained to the driver      | can therefore not enhance     |
|      | without cognitive overload   | immediate driving performance |
| feed |                              |                               |
| back | Can enhance driving          | Requires the driver to be an  |
|      | performance for future trips | active recipient of           |
|      |                              | information, however people   |
|      | Can refresh driver's memory  | may not take the time to      |
|      | on performance for past      | review this type of feedback  |
|      | trips                        |                               |
|      |                              | Driver may fail to link       |
|      | Can calibrate driver's       | feedback with incident        |
|      | subjective performance by    |                               |
|      | highlighting persistent      | Low acceptance may lead to    |
|      | behavior that leads to       | disuse                        |
|      | errors                       |                               |
+------+------------------------------+-------------------------------+

### Concurrent feedback

Concurrent feedback is presented to the driver in real-time when there
is a resource conflict between driving and IVIS. For example, if the
driver is distracted or if the driver fails to respond appropriately to
a roadway demand, concurrent feedback would remind the driver to
discontinue engaging in the secondary task, and direct their attention
back to the roadway. Therefore, concurrent feedback has immediate
implications for driving performance. Warnings are forms of concurrent
feedback, and the literature related to warnings in the driving domain
is vast (Deering & Viano, 1998; Hirst & Graham, 1997; Parasuraman,
Hancock, & Olofinboba, 1997).

Continuous concurrent feedback for the learning of movement skills is
shown to improve immediate performance but degrade retention (Karlin &
Mortimer, 1963; Schmidt & Wulf, 1997). This is generally explained by
the idea that concurrent feedback interferes with the extraction of
relevant (intrinsic) information that feedback is based on (Annett,
1959, 1969). Therefore, when feedback is not provided anymore, the
person is not able to extract relevant information. Such a problem may
also occur in driving, and in the long term, the drivers may become
overly dependent on feedback to identify hazardous situations and may
not respond appropriately if the feedback mechanism fails.

In contrast, unreliable feedback (both false positives: an alarm given
when no impending collision is present; and false negatives: an alarm
not given when an impending collision is present) may undermine
acceptance and lead drivers to ignore concurrent feedback. High false
alarm rates can also lead to driver frustration, which can also have a
negative impact on traffic safety (Burns & Lansdown, 2000). However, not
all false positive alarms are harmful. Such alarms can be used to train
novice drivers, and are also needed to generate driver familiarity with
the system. False positive alarms may also lead to more cautious driving
and thereby result in reduced false alarm rates (Lees & Lee, in press;
Parasuraman et al., 1997). Thus, for a warning system to be effective,
an acceptable false alarm rate should be established. The reliability of
feedback is a major issue regardless of feedback timing. The concerns
about trust and disuse also hold for feedback in larger timescales (Lee
& See, 2004).

Another reason concurrent feedback may not be completely effective in
mitigating distraction is the possibility of concurrent feedback to
interfere with immediate task performance. There has been research
showing such an effect in radar monitoring (Munro, Fehling, & Towne,
1985) and in driving (Arroyo, Sullivan, & Selker, 2006). Because of the
limited processing time and resources available during driving,
concurrent feedback may impose additional distractions on the driver.
One way to avoid possible information overload during an already
demanding situation is to delay feedback by a couple of seconds until
the demand decreases.

### Delayed feedback

Delaying feedback by even a few seconds might avoid overloading an
operator. However, this potential has only been investigated by a few
researchers (Arroyo et al., 2006; Sharon, Selker, Wagner, & Frank,
2005). In one such system, CarCOACH (Car Cognitive Adaptive Computer
Help), feedback is canceled or delayed by a scheduling system, if the
system senses that the driver has made too many mistakes or has received
too much concurrent feedback (Arroyo et al., 2006). Rather than focusing
on assistance in driving, the CarCOACH provides instructive feedback to
help drivers learn about their driving performance and skills. That is,
delaying feedback by even a few seconds will center more on altering
behavior rather than improving immediate performance. The situations of
cognitive overload in CarCoach are identified as: (1) the driver has
been making many mistakes even though they are receiving much feedback,
or, (2) the driver appears unusually busy with a particular task not
generally performed while driving, such as backing up.

For situations of cognitive overload and situations which appear to be
too dangerous (e.g. skidding), feedback is usually canceled to prevent
frustration and confusion about what exactly feedback concerns. Only
when two sequential events that require feedback occur within seconds of
each other, will the system provide feedback for both at the same time
by delaying the first. An example of this situation is the rapid
acceleration followed by a braking event. In this respect, cognitive
overload may have been inadequately defined. The drivers may be
cognitively overloaded even if they are not making a lot of mistakes.
Thus, a better way of evaluating cognitive overload may be to assess
convergent data from physiological and performance measures.

An on-road experiment as part of this study was also conducted to
evaluate driver performance and frustration with the feedback content
(i.e. positive or negative) and feedback scheduling (i.e. no feedback,
concurrent feedback and scheduled feedback) of CarCoach (Arroyo et al.,
2006). The positive feedback thanked the driver and acknowledged a good
maneuver, whereas negative feedback pointed out mistakes. The results of
the study suggest that concurrent positive feedback enhanced
performance. Positive feedback reduced frustration while negative
feedback increased frustration and degraded performance, and this
outcome was most prevalent for concurrent negative feedback. Therefore,
this study demonstrated that concurrent feedback on driver performance
can indeed result in a performance decrement. The purpose of this study
was to assess the effects of canceling or delaying feedback. However,
due to the design of the scheduling system, the delayed feedback was
presented with the concurrent feedback, and the various situations for
canceling or delaying feedback within this condition were not
distinguished.

In a separate study of CarCoach, Sharon et al. (2005) demonstrated that
guiding drivers to a certain acceleration by slightly delaying feedback
(in the order of seconds) for instructional messages, resulted in better
performance than concurrent feedback presentation. The concurrent
feedback was given during the acceleration maneuver, whereas the delayed
messages were provided once the acceleration maneuver was over. However,
this experiment did not include a baseline condition (i.e. no feedback).
Therefore, the results cannot suggest if any of the feedback timings
(i.e. concurrent, delayed) enhanced driving performance compared to no
feedback.

Because driving and in-vehicle tasks are carried out in an interlaced
fashion, these tasks can be viewed as mutually interrupting tasks (Monk,
Boehm-Davis, & Trafton, 2004). A potential concern of these
interruptions is the initial decrease in performance as the interrupted
task is resumed. In addition to the safety considerations associated
with the interruption of the primary task of driving by an in-vehicle
task, productivity issues may also arise as in-vehicle tasks are
interrupted by the need to shift attention back to road. Therefore, even
if the main objective of concurrent and delayed feedback is to enhance
safety, a successful design should also aim to enhance the driver
productivity in interacting with the IVIS, or at least protect this
productivity from deteriorating. Concurrent and delayed feedback are
likely to undermine productivity because they occur during the course of
a trip and would therefore have the potential to interfere with IVIS
interactions.

Learning of rule-based and information-integration categorizations are
mediated by explicit reasoning and procedural learning, respectively.
Maddox et al. (2003) suggest that immediate corrective feedback is very
important in the learning of information-integration categorization that
cannot be solved by applying verbalizable rules. This suggests that
concurrent or delayed feedback can help drivers understand whether a
maneuver they are performing is unsafe. For example, learning safe
following distances can be better learned with concurrent feedback
compared to post-hoc feedback (feedback provided once a trip is
completed). Gibson & Crooks (Gibson & Crooks, 1938) state that even if
the minimum stopping zone is dependent on the speed of the car, the
drivers' awareness of how fast they are going does not consist of an
estimated speed in miles/hour but a distance that they can safely stop.
A safe minimum stopping zone can be learned procedurally. This can be
best achieved with concurrent feedback. Alternatively, rules defining
unsafe driving behavior (e.g. talking on the cell-phone while changing
lanes) can be conveyed by larger timescale feedback (e.g. post-hoc) as
well as concurrently. Because driving is a time-critical task, such
information may be better presented to the driver by post-hoc or
collective feedback. Conveying the causes of unsafe situations via
concurrent or delayed feedback may increase driver distraction. The
limited processing time is not an issue if feedback is provided in a
larger timescale. If the driver is given feedback after a trip is
completed, the driver will not have to share attention between driving
and feedback.

### Post-hoc feedback 

Post-hoc feedback would be provided once a trip is complete and not
while driving. It would show the driver what was done correctly and
incorrectly during the most recently completed trip. Post-hoc feedback
and the feedback timing in later discussions (i.e. collective) focus on
influencing driver behavior. The driver can learn what constitutes safe
driving, when to engage in distractions, what speed to maintain in
different driving conditions, and how to diminish risk in planning a
trip. Near-accidents are generally forgotten very rapidly in the absence
of feedback. For example, Chapman & Underwood (Chapman & Underwood,
2000) found that an estimated 80% of near-accidents are forgotten after
two weeks. This suggests that driver behavior may be changed by
refreshing drivers' memory of their driving performance as well as
calibrating their subjective performance (i.e. how safe they think they
drive).

There is vast research in feedback timing in the learning domain
(Clariana, Wagner, & Roher-Murphy, 2000; R. W. Kulhavy, 1977; Kulik &
Kulik, 1988). However, the results do not favor immediate or post-hoc
feedback compared to one another. The effectiveness of feedback timing
depends on the task that is being learned and how feedback is presented.
In classroom studies of verbal learning , immediate feedback is found to
be more effective than lagged feedback (Kulik & Kulik, 1988). For
learning studies, lagged feedback is provided once a lecture is
completed, therefore corresponds to post-hoc feedback in the proposed
framework.

However, in studies of test acquisition, which are carried out with more
control, lagged feedback is superior to immediate feedback for knowledge
retention (R. W. Kulhavy, 1977; Kulik & Kulik, 1988). Two hypothesis
have been proposed to explain this phenomenon, which is known as the
delay retention effect (Blackbill, Blobitt, Davlin, & Wagner, 1963).
Interference-perseveration (R. W Kulhavy & Anderson, 1972) indicates
that when feedback is lagged and separated from the test acquisition
trial over time, incorrect responses tend to be forgotten and would not
interfere with the learning of correct response. The dual-trace
information processing explanation (Glover, 1989; Kulik & Kulik, 1988)
states that if feedback is provided immediately after the test
acquisition then the two trials of learning (acquisition and feedback)
are almost fused. If the feedback trial is delayed (such as in post-hoc
feedback), then the learner is given more separate trials and explicit
stimulus exposures. Although feedback timing has been extensively
researched in the learning domain, there still is no consensus on which
feedback timing (immediate or lagged) is more effective in general. For
the driving domain, because concurrent feedback mostly tends to enhance
immediate performance, in the short run it can be more effective than
post-hoc feedback. However, because post-hoc feedback is more focused on
behavioral changes, it has promise for the long term.

### Collective Feedback

Collective feedback is a comprehensive summary of past driving
performance and driver behavior and is not provided during driving.
Collective feedback integrates driving data over many trips that might
span several weeks or months. Similar to post-hoc feedback, collective
feedback has the potential to change driver behavior. Both post-hoc and
collective feedback is summative. This can help the drivers assess their
overall driving performance by highlighting the persistent behavior that
leads to errors. Neither post-hoc feedback, nor collective feedback has
been systematically studied in the driving domain and require further
research (but see, McGehee, Carney, Raby, Reyes, & Lee, in press; Tomer
& Lotan, 2006, for some preliminary examples).

## Combinations of different feedback timescales

Feedback mechanisms need not be considered as mutually exclusive. There
can be substantial benefits in integrating different combinations of
feedback. As mentioned in the previous section, research suggests that
information-integration categorization tasks, such as determining
whether a driving maneuver is safe or not, require two or more stimulus
components to be incorporated into a decision (Ashby & Gott, 1988).
Maddox, Ashby, & Bohil (Maddox et al., 2003) suggest that immediate
corrective feedback should be provided for such categorization as it
requires procedural learning. Therefore, concurrent feedback may be best
to help drivers learn what constitutes an unsafe situation (e.g. close
following distance). However, to fully understand behaviors, choices,
and traffic settings resulting in unsafe situations, a rule-based
categorization is needed which can be learned by simple explicit
reasoning process (Ashby, Alfonso-Reese, Turken, & Waldron, 1998). For
example, providing concurrent feedback in the form of a short alert
cannot explain the specific problems associated with changing lanes
while simultaneously talking on a cell-phone in a congested area. This
can be better conveyed in more detail as post-hoc feedback. However,
concurrent feedback can indicate that some response is needed
immediately. Including both feedback mechanism can create complementary
support to improve immediate performance (i.e., concurrent feedback)
while potentially changing future behavior (i.e., post-hoc feedback).

The little research that has considered feedback timing at different
timescales has compared one level to another, but has not assessed the
potential benefits of providing both levels together. Presenting
feedback at multiple timescales can provide redundancy and refresh
driver's memory about an incident. This redundancy is useful since
research suggests that drivers forget the majority of near-accidents
very rapidly (Chapman & Underwood, 2000). Receiving feedback in smaller
timescales can also help the driver understand feedback in larger
timescales. However, the support between multiple feedback timescales
may diminish as the time between them increases. That is, concurrent
feedback provided on an incident and collective feedback provided weeks
or months later regarding the same incident may not be easily connected
by the driver. If concurrent feedback has been strengthened in the
memory by the help of post-hoc feedback, the driver can better relate
collective feedback to this incident. For the driver to easily relate
different feedback timescales, the representation of feedback should
promote a consistent mental model (Vakil & Hansman, 2002). For example,
the representation of dangerous situations for post-hoc feedback and
collective feedback should share key characteristics.

The advantages of combining feedback from multiple timescales can be
summarized as follows: combined feedback timescales can (1) complement
each other in enhancing performance and changing behavior; (2) provide
redundancy and refresh driver's memory about an incident; (3) help
driver understanding of feedback in large timescales.

## Interaction of feedback timing and feedback content 

The interactions between different contents and timescales can influence
the effectiveness of feedback. The choice of feedback content can be
determined by feedback timing which can then affect immediate driving
performance and behavior. Feedback provided to the driver can be
negative, corrective or positive (Graesser, Person, & Magliano, 1995;
Lepper, Aspinwall, Mumme, & Chabay, 1990). Negative feedback indicates
that an error has occurred. Corrective feedback is also provided when
there is an error, but does not explicitly indicate that an error has
occurred. Positive feedback, also known as confirmatory feedback, is
provided for correct actions (Chi, Siler, Jeong, Yamauchi, & Hausmann,
2001).

Negative feedback identifies errors, and includes error flagging,
directive feedback, and explanatory feedback (Sanders, 2005). An example
of these errors can be observed in a study by Graesser, Person, &
Magliano (Graesser et al., 1995) who have identified different student
errors to be handled by a tutor: Error flagging includes acknowledging
and identifying the error occurrence, directive feedback includes
instructing the student how to repair the error, and explanatory
feedback includes diagnosing the bugs and misconceptions that generated
the error, and setting new goals that remediate the error, bugs and
misconceptions. If these feedback types are used in the driving domain,
error flagging and directive feedback would be more appropriate within
concurrent feedback and explanatory feedback would be more appropriate
in post-hoc feedback since this type of feedback would require more time
and resources from the driver. Explanatory feedback can show the driver
which types of behavior result in near incidents and has promise for
altering driver behavior.

Directive and explanatory feedback are also similar to the procedural
and conceptual feedback investigated by other researchers (Fiesler,
McLaughlin, Fisk, & Rogers, 2003; Mead & Fisk, 1998). Conceptual
feedback provides instructions on what needs to be done to complete a
task, whereas procedural feedback provides instructions on each action
without the reason for performing them. For example, if a driver is
speeding, then an instruction given to the driver such as "slow down" is
procedural, whereas "speed limit is 55 mph" is conceptual. Mead & Fisk
(Mead & Fisk, 1998) found that procedural feedback resulted in better
performance earlier for an ATM interaction task. This provides evidence
that procedural (i.e. directive) feedback when provided concurrently can
enhance immediate performance more than conceptual (i.e. explanatory)
feedback.

All examples above demonstrate negative feedback. Two major drawbacks of
error identification are the failure of students to discover their own
errors and the loss in students' confidence especially when they receive
substantial negative feedback and error remediation (Bangert-Drowns,
Kulik, Kulik, & Morgan, 1991; Merrill, Reiser, Ranney, & Trafton, 1992).
Similarly, the drivers may become too dependent for concurrent feedback
if they see feedback as the main agent to detect hazardous events. Also,
too much negative feedback, regardless of the timing, can undermine
driver acceptance. Research on tutoring supports gentle, indirect
feedback as opposed to harsh direct negative feedback (Lepper et al.,
1990). Graesser et al. (Graesser et al., 1995) calls this corrective
feedback. Corrective feedback can also be directive or explanatory but
does not include error flagging.

Positive feedback is provided for correct actions and can help promote
acceptance. For example, workers accept an ergonomic intervention more,
if they are provided with positive feedback (Branderburg & Mirka, 2005).

Reeves & Nass (1996) suggest that people have expectations from
technology similar to the expectations from interpersonal interactions.
People also respond socially and naturally to technology. Fogg & Nass
(1997)found that people, who got positive feedback from a computer,
performed significantly better during a computer game than people who
did not receive any evaluation. The results were the same even for
positive feedback that was baseless and when participants were told that
feedback was unreliable. Participants liked the computer better when it
praised them regardless of feedback reliability, compared to when it
criticized them. Unlike unreliable positive feedback, participants
dismissed unreliable negative feedback. However, participants took
reliable negative feedback seriously and were more critical of their
performance. Negative feedback is beneficial in enhancing performance
and learning, however positive feedback better supports the acceptance
of technology. In some situations negative feedback is necessary to
educate the driver on risky driving patterns. Including positive
feedback in addition to negative feedback can help change drivers'
attitudes towards the technology. If feedback is provided in a large
timescale (e.g. post-hoc) then the driver should be an active recipient
of the information. Active driver participation should be encouraged
otherwise the driver may disregard feedback. One approach to achieve
this participation is to include positive feedback and design the
interface in an aesthetically pleasing and easy to use manner.

Overall, the type of feedback can be as important as when and how it is
presented. This leads to several specific design considerations: (1)
Error flagging and directive feedback are more appropriate for
concurrent feedback, whereas explanatory feedback is better suited for
post-hoc and collective feedback. (2) Negative feedback is beneficial in
enhancing performance and learning. (3) If concurrent feedback is
negative (e.g. identifies hazardous situations), it can create driver
dependence on feedback in determining hazards. (4) Too much negative
feedback can degrade driver acceptance. (5) Positive feedback better
supports the acceptance of technology and is especially needed for
post-hoc and collective feedback where the driver should be an active
recipient of information.

## Conclusion

Most of the research in driving domain considers only the ability of
immediate feedback to mitigate distraction and enhance driving
performance. In addition to enhancing immediate performance, feedback
might also alter driver behavior to induce safer driving. The effects on
immediate driving performance can be observed in performance such as
braking, speed variation, and time headway maintenance. The long-term
behavior changes may be better awareness of certain safety critical
situations, greater responsiveness to the roadway environment, and
diminished willingness to engage in various types of distracting
activities. Concurrent and delayed feedback can have the greatest effect
on immediate driving performance whereas post-hoc and collective
feedback can have a greater effect on the long-term behavior. The
combination of concurrent feedback and feedback at larger timescales may
have more powerful effects than either alone.

# Drivers' Attitudes towards imperfect distraction mitigation 

In-vehicle technology that mitigates the effect of driver distraction
(e.g., warning systems) can be considered as a form of automation.
Recent reviews of automation and its effect on operator performance
provide valuable insights that can highlight the advantages and
disadvantages of various distraction mitigation strategies (Lee & See,
2004; Parasuraman, Sheridan, & Wickens, 2000; Sheridan, 2002). Although
distraction mitigation systems have great potential, these systems may
also fail to provide expected benefits. Miscalibrated trust and the
potential for misuse and disuse are among the many reasons for such
failures (Parasuraman et al., 1997). Trust is a particularly important
factor influencing reliance and the use of automation. As distrust may
lead to the disuse of the automation, mistrust can lead to inappropriate
reliance, resulting in a failure to monitor the system's behavior
properly and to recognize its limitations, thereby leading to misuse of
the system (Parasuraman & Riley, 1997).

Another concern that affects user acceptance and appropriate reliance on
mitigation strategies is false system adaptation. False adaptation
occurs when a system falsely adapts to driver state and situational
demands. That is, the system takes action when there is no need, or
takes no action or an inappropriate one when there is a need. False
system adaptation contribute to drivers' response to and acceptance of
the system, which may in turn influence system effectiveness
(Parasuraman et al., 1997). False adaptation includes both false
positives (an alarm given when no impending collision is present) and
false negatives (an alarm not given when an impending collision is
present). In these scenarios, distrust and disuse can result from high
false-alarm rates. Due to the low base rate of collision events, the
probability of a collision when a warning is given can be quite low,
while the false-positive alarm rate can be quite high, even if the
warning system is highly advanced. High false alarm rates can also lead
to driver frustration, which is itself a type of emotional distraction
that can have a negative impact on traffic safety (Burns & Lansdown,
2000).

Drivers' acceptance of the system is also a key issue and depends on
ease of system use, ease of learning, perceived value, advocacy of the
system, and driving performance (Stearns, Najm, & Boyle, 2002).
Acceptance interacts with trust, and low levels of acceptance would lead
to disuse. Therefore, driver acceptance of a distraction mitigation
strategy should be assessed before the strategy is implemented. The
presentation modality also has an impact on the acceptance, if the
strategy uses an alarm or a display to alert the driver. Some of the
most common modalities employed in warning systems and displays are
visual and auditory (Wickens & Hollands, 1999). Because visual warnings
use the more common resource with the driving task, these strategies may
lose effectiveness (Wickens, Lee, Liu, & Gordon, 2003). However, even if
the auditory warnings are omni-directional and hence may be more
effective, sound may also induce annoyance (Berglund, Harder, & Preis,
1994).

Age is also a factor that affects attitudes towards technology. In
general, older adults have relatively less positive attitudes towards
technology (Brickfield, 1984; Kantowitz et al., 1997). However, studies
have shown that older drivers may also put more trust in technology (Fox
& Boehm-Davis, 1998b). These conflicting findings may be due to the
different types of technology assessed. For example, collision warning
systems may directly compensate for cognitive impairments in older
drivers and hence increase trust, whereas navigational displays may
place greater demands and degrade the level of trust.

To further explore the relationship of acceptance and trust for
different age groups and presentation modalities, a taxonomy is needed
that will systematically identify the dimensions of mitigation
strategies and the relationships between the dimensions. This taxonomy,
which was initially discussed in Donmez, Boyle, & Lee (2003), was
refined with focus group sessions that are discussed later in this
paper. A driving simulator experiment was then conducted to assess the
acceptance and, trust of two of the mitigation strategies defined within
the final taxonomy. The objective of this study is to understand which
distraction mitigation strategies drivers prefer, as well as to assess
how age and presentation modality impact acceptance and trust.

## Focus groups to explore acceptance and trust

A preliminary taxonomy of the mitigation strategies was developed based
on three dimensions: level of automation, initiation type, and the type
of task that is being modulated by the strategy. Level of automation can
influence the effectiveness and acceptance of the strategies. Therefore,
the mitigation strategies were categorized in terms of whether they are
related to a high, moderate or low level of automation based on recent
definitions for levels of automation (Parasuraman et al., 2000;
Sheridan, 2002). The strategies were then further categorized according
to whether they address driving-related (e.g., steering, braking) or
non-driving-related tasks (e.g., tuning the radio, talking on the cell
phone) as defined by Ranney et al. (2000). Strategies that address
driving-related tasks focus on the roadway environment and directly
support driver control of the vehicle, whereas strategies for
non-driving related tasks focus on modulating driver interaction with
in-vehicle systems. Type of initiation of a strategy also guides the
level of driver acceptance and strategy effectiveness. Within the
previously defined categories, the mitigation strategies were
subcategorized as driver initiated (i.e., where the driver is the locus
of control) and system initiated (i.e., where the system is the locus of
control). These dimensions were considered critical for the development
of mitigation strategies because different levels of these dimensions
would affect drivers' response to and acceptance of distraction
mitigation strategies. Focus groups were conducted to better understand
whether or not this taxonomy provides a good indication of how people
categorize technology with respect to trust and acceptance.

Focus groups have previously been used in transportation and other
research to gain perspective and insights on an issue (Lerner, 2005;
Rivers, Sarvela, Shannon, & Gast, 1996; Rogers, Meyer, Walker, & Fisk,
1998; Yassuda, Wilson, & von Mering, 1997). Typically, focus groups are
used as a part of large research programs so that the data collected can
be integrated with data from experiments, surveys, etc. Although, the
small number of participants in focus groups limits the generalization
to a larger population (Rogers et al., 1998; Stewart & Shamdasani,
1990), the insight gained from this type of exploratory research is
valuable in developing hypotheses and in formulating more precise
research questions. This initial taxonomy was presented to two different
focus groups in the rural and urban US, Iowa City, Iowa and Seattle,
Washington, respectively. Focus groups were conducted in each city with
participants' age ranging from 22 to 64 ($\overline{X}$=37.8,
$\sigma^{2}$=11.8). The participants generated ideas to further develop
the taxonomy as well as to form hypotheses regarding how the various
strategies might affect acceptance.

The focus group moderators informed the participants about what is
considered driver distraction and included a brief overview of the
different types of distractions. Specifically, illustrations of the
visual only, visual manual, manual only, and cognitive types of
distraction were presented (Ranney et al., 2000; Wierwille, 1993). In
addition, the sources of known distraction were also presented and
include: distractions from in-vehicle technology (e.g., radio),
distractions from other passengers, and external distractions (e.g.,
billboards). A 12-minute video on driver distraction, which provided
examples of drivers engaged in various distractions were presented. The
questions asked to the participants included:

\(1\) What types of distractions have you been engaged in?

\(2\) What brought you back to reality, or out of the distracting task?

\(3\) How have passengers been helpful when you were distracted?

\(4\) What suggestions from passengers have annoyed you?

\(5\) Given some of the technology available, what could be used to help
you in a distracted situation?

\(6\) What strategies (presented in a preliminary taxonomy) would you
consider helpful and what do you think is missing?

Feedback from the focus group improved the initial taxonomy by
identifying a distinction (i.e., driving-related strategies that are
driver initiated) not previously identified. The majority of research in
mitigation strategies has centered on the driving-related strategies
that are system initiated (e.g. forward collision warning system, run
off the road). Previous research in driver-initiated systems (e.g.,
conventional cruise control) typically did not center on mitigation
strategies, but were viewed as convenience systems for drivers (Bogard,
Fancher, Ervin, Hagan, & Bareket, 1998). However, the focus group
suggests that perhaps these types of systems can be tailored to reduce
the effect of driver distraction. Moreover, titles of some mitigation
strategies were changed to reduce the ambiguity and negative
connotations relating to some of the mitigation groups (i.e., *nagging*
to *advising*). This new taxonomy is shown in Table 4. 3 and further
discussed in Donmez et al. (2003). A summary of each category is
presented here.

[]{#__RefHeading___Toc160304993 .anchor}Table 4. 3. Taxonomy of
distraction mitigation strategies.

  --------------- ------------- ------------- -------------- ----------------
  LEVEL OF        DRIVING                     NON DRIVING    
  AUTOMATION      RELATED                     RELATED        
                  STRATEGIES                  STRATEGIES     

                  System        Driver        System         Driver Initiated
                  Initiated     Initiated     Initiated      

  High            Intervening   Delegating    Locking &      Controls
                                              Interrupting   Presetting 

  Moderate        Warning       Warning       Prioritizing &  Place-keeping
                                Tailoring     Filtering      

  Low             Informing      Perception   Advising       Demand
                                Augmenting                   Minimizing 
  --------------- ------------- ------------- -------------- ----------------

System initiated strategies under the category of driving related tasks
aim to enhance safety by directing driver attention to the roadway as
well as by directly controlling the vehicle. Based on the discussion of
the focus group participants, some of the systems that were discussed
did not fit into any of the existing categories. Therefore, a new
category (driving related, driver initiated) was developed. This group
of strategies mitigates distraction by having the driver activate or
adjust system controls that relate to the driving task. Previous
research showed that drivers generally like the comfort and convenience
of the systems that fall in this group (Bogard et al., 1998). The
driver-initiated strategies that are non-driving related, rely on the
driver to modulate their non-driving tasks according to their subjective
degree of distraction. System initiated, non-driving related strategies
build upon the idea that when the driving performance is or will be
significantly deteriorated, the system would take action and change the
nature of the non-driving related task that the driver is engaged in. As
will be discussed in the next section, this category is an area of
growing concern and merits further research.

## Simulator experiments to assess acceptance and trust

The focus group helped us define some key characteristics for mitigation
strategies and what types of systems may be more acceptable for drivers.
Of the categories presented to the focus group participants, the
majority of previous research has centered on driving-related strategies
(e.g. collision warning systems, adaptive cruise control). Of the
non-driving-related strategies, only *demand minimizing* has been
investigated as a potential means of reducing distraction (Lee et al.,
2001). Because the number of non-driving related devices are growing,
and drivers indicated a preference for continued use, acceptance issues
related to non-driving related tasks was further explored.

Strategies tested include *advising* and *locking* which represent the
extreme ends of automation (high and low) under the non-driving related,
system initiated category. The system initiated category was
investigated because the driver initiated strategies depend highly on
the subjective distraction level of the driver and do not promise as
high effectiveness. It is also important to consider the impacts of
automation level therefore strategies tested represent two extreme ends
of automation. *Locking* discontinues the non-driving activities and
locks out the system that is associated with the distracting activities
while *advising* gives drivers feedback regarding the degree to which
they are engaged in a non-driving task.

## Methodology

A simulator study was designed to assess driver's acceptance and trust
of non-driving related mitigation strategies. Given the focus group's
varying opinions on automation, this categorization was further tested
based on a high or low level of automation. This study investigates
*locking* and *advising* strategies for mitigating auditory and visual
distraction because previous research has shown that both of these
non-driving tasks can distract drivers and they have different effects
on driving performance (P.J. Cooper & Zheng, 2002; P.J. Cooper et al.,
2003; Lee, McGehee, Brown, & Reyes, 2002). Therefore, it is important to
understand acceptance when strategies are presented in different
modalities.

### Participants

Twenty-eight drivers; 16 middle-aged (Range: 35 to 55;
$\overline{X}$=45, $\sigma$=4.27) and 12 older drivers (Range: 65 to 75;
$\overline{X}$= 69, $\sigma$=3.26) participated in this study. The
participants were monetarily rewarded depending on their performance on
the task. This enabled the experimental task to more realistically
simulate drivers' interaction with in-vehicle systems by ensuring that
the secondary task was important to the driver.

### Apparatus

The experiment was conducted in a fully integrated, fixed based driving
simulator. The simulator has a 1992 Mercury Sable vehicle cab equipped
with force feedback steering wheel, actual gauges, and a rich audio
environment. The driving scenarios were created using HyperDrive™
Authoring Suite, were projected onto a screen with a 50 degree field of
view. The fully textured graphics were generated with 60 Hz frame rate
at 1024 x 768 resolution. All graphics for roadway layouts, markings,
and signage conform to American Association of State Highway and
Transportation Officials (AASHTO) and Manual of Uniform Traffic Control
Devices (MUTCD) design standards. Driving data were collected at 60 Hz.

A 7 inch LCD (60 Hz frame rate at 640 x 480 resolution) mounted on the
dashboard with a small stand was used in the experiment for the
presentation of the visual messages used in the secondary task. The
viewing angle from the driver's eye point is approximately 18 degrees.
Auditory messages used in the secondary task were converted into .wav
audio files through the Ultra Hal Text-to-Speech Reader, Version 1.0,
created by Zabaware, Inc. An adult male, low-accented North American
English native voice was mastered using Microsoft SAP14 Text-to-Speech
Synthesis Machine. The message systems (visual and auditory) were
controlled with Microsoft Visual Basic.

### Experimental design and independent variables

The experiment was a 2^4^ repeated measures design with day and drive as
repeated measures. There were two levels for each of the four
independent factors: age (middle-aged/old), mitigation strategy
(advising/locking), secondary task (visual/auditory), and system
adaptation (true/false). Age was the only between subjects factor.

Two distraction mitigation strategies were implemented in the system to
either advise the driver to discontinue the non-driving related task
(*advising*) or to lock out the interaction with the system completely
(*locking*). Both of the strategies were mapped to the driving events
that require appropriate response from the driver. These two events were
the lead vehicle braking and the curve entry ahead. Curve entry ahead
refers to the road section consisting of two seconds long drive straight
section before the curve and three seconds long drive section of the
curve. The participant was told that the system would take actions when
he/she should attend to the roadway, specifically when the lead vehicle
was braking or there was a curve ahead. The mitigation strategies were
implemented between scenarios. That is, each mitigation strategy was
tested with a separate experimental drive.

For the visual secondary task, *advising* was implemented with a red
bezel around the screen (Figure 4. 4). The red bezel illuminated
whenever there was a lead vehicle braking or curve entry ahead (five
seconds for both conditions). With the *advising*, the driver was still
able to interact with the system. The *locking* strategy blanked the
screen and illuminated the red bezel. The red bezel and the lockout
remained in effect until the triggering condition was over (i.e. lead
vehicle braking or curve entry). For the auditory secondary task,
*advising* was implemented with a periodic clicking noise (1 Hz)
whenever there was a lead vehicle braking or curve entry ahead. With
*advising*, the driver was still able to interact with the system. The
*locking* strategy stopped the task message presentation and presented
the periodic clicking noise to the driver. The lockout remained in
effect until the triggering condition was over. There were separate
experimental drives for each level of the secondary task
(visual/auditory).

[]{#__RefHeading___Toc160355581 .anchor}Figure 4. 4. The visual advising
strategy.

Figure 4. 4 Advising strategy in visual mode The system adaptation
(true, false) was implemented between days with the order of
presentation counterbalanced between two days. That is, a random half of
the participants began with the true system adaptation on the first day
whereas the other half received the false adaptation on the first day.
True system adaptation refers to the system properly adapting to the
environment or driver state. False system adaptation occurs when the
system fails to adapt appropriately, producing both false alarms (i.e.,
takes action when it is not supposed to) as well as misses (i.e., not
taking action when it was supposed to). These two types of imperfections
within false adaptation might affect the driver acceptance, trust, and
use of the system and should be further explored. However, for this
initial investigation, the effects of the misses and false alarms within
the false adaptation condition are not differentiated. For the purpose
of creating an inaccurate system, both of these imperfection types were
implemented together under the condition of false system adaptation to
form a 50% accuracy rate. The duration of alarms (*advising* and
*locking*) were equal for each scenario drive.

### Procedure

After subjects signed the informed consent and given a practice drive,
the participant was instructed to drive at a comfortable speed which was
not above the speed limit of 45 mph and to follow a lead vehicle which
periodically braked at a mild rate of deceleration (0.2 g) for five
seconds. All driving scenarios took place on simulated two-lane rural
roads with 12 braking events in each driving scenario. Half of the
braking events were on curves and half were on the straight sections of
the drive. To make the scenario more realistic, different radius curves
were used; half of the curves were 400 meter radius (three left turn,
three right turn) and the other half were 200 meter radius (three left
turn, three right turn). The braking events and the radius of curves
were randomized through the drives.

The secondary task was based on the working memory span task defined by
Baddeley, Logie, & Nimmo-Smith (1985), and was displayed to the
participant on an LCD display for the visual task and by a synthetic
voice for the auditory task. The secondary task required the participant
to determine if a short sentence was meaningful or not (response by
pushing steering wheel buttons) and then to recall the subjects of three
consecutive sentences (verbal response). For example "the policeman ate
the apple" is meaningful and its subject is "policeman", whereas "the
apple ate the policeman" is not meaningful and its subject is "apple".
The button-press and verbal recall tasks provided a controlled exposure
to the visual, auditory, motor, and cognitive distraction associated
with in-vehicle information system interaction and was similar to the
tasks used in other driver distraction studies (Radeborg, Briem, &
Hedman, 1999). Feedback regarding performance with the secondary task
was provided to the participant at the end of each drive.

### Dependent variables

An acceptance questionnaire based on Van Der Laan, Heino, & De Waard
(1997) was given to the participants after each drive. The questionnaire
composed of nine questions investigating two dimensions of acceptance:
usefulness and satisfying. Before analysis, the acceptance questionnaire
was recoded to fall along a scale of -2 to +2 (-2 representing lowest
level of acceptance and +2 representing the highest). These numbers were
then averaged to obtain a metric for usefulness and satisfying as
defined in Van Der Laan et al. (1997). Additional acceptance
questionnaires were also filled out by the participants. These
questionnaires aimed to assess the acceptance of the *advising* and
*locking* strategies if they were embedded in current in-vehicle system
features (radio, cell phone, email).

A system trust questionnaire based on Wiese (2003) was given to the
participants. Two statements used from the questionnaire were 'I trust
the safety system' and 'The performance of the safety system enhanced my
driving'. A -2 (strongly disagree) to +2 (strongly agree) Likert scale
was used to code the responses. The overall trust metric was obtained by
averaging the responses for these two questions.

## Results 

The mixed procedure in SAS 9.0 with Sattherwaitte's approximation for
unequal variance was used to analyze the data. This approximation will
result in degrees of freedoms for the error term reported in decimals.
Our results show that middle-aged and older participants differed in
their response to the strategies. Older participants perceived the
strategies to be more useful (*F*(1,26.5) = 9.43, *p*\<0.005) and were
more satisfied (*F*(1,26.7) = 11.43, *p*\<0.005) compared to the
middle-aged group (Figure 4. 5). Older drivers tended to accept
non-driving related, system initiated mitigation strategies more than
middle-aged drivers. However, regardless of age group, visual strategies
were perceived to be more satisfying (*F*(1,160) = 40.3, *p*\<0.0001)
and more useful (*F*(1,158) = 21.66, *p*\<0.0001) than the auditory
based strategies. These finding confirm the focus group results
regarding the preferred display modality. Focus group participants
preferred visual compared to auditory based strategies.

![](media/image3.wmf){width="6.496527777777778in"
height="3.938888888888889in"}

[]{#__RefHeading___Toc160355582 .anchor}Figure 4. 5. Acceptance of
mitigation strategies by age group and presentation modality.

Older participants trusted the systems more than the middle-aged
participants (F(1,26.8) = 9.84, p\<0.005) (Figure 4. 6). As expected,
systems that were 100% accurate resulted in higher trust than the 50%
accurate systems (F(1,27.2) = 6.21, p\<0.05). The system accuracy
emerged as an important issue from the focus group findings and the
experimental data support that system accuracy would guide trust in the
systems. Participants trusted the visual strategies more than the
auditory strategies (*F*(1,160) = 10.07, *p*\<0.005). There were no
significant differences between the advising and locking strategies
neither for acceptance (*p*\>0.05) nor for trust (*p*\>0.05).

![](media/image4.wmf){width="6.093055555555556in"
height="3.5395833333333333in"}

[]{#__RefHeading___Toc160355583 .anchor}Figure 4. 6. Trust on mitigation
strategies by system adaptation, age group, and presentation modality.

Pearson correlation coefficients for three variables, level of trust in
the driver distraction mitigation strategy, usefulness, and satisfying,
were investigated. As the level of usefulness increased, so did the
driver's level of trust (ρ = 0.731, p\<0.0001). Likewise as satisfaction
increased, so did the level of trust (ρ = 0.629, p\<0.0001).

After driving in the simulator and experiencing the various mitigation
strategies, drivers rated their acceptance of these strategies as
applied to current and likely in-vehicle information systems.
Participants were asked to assess the preferences for these strategies
given current technology including cellular phones, voice activated
e-mail messages, and radio controls. These in-vehicle systems were
evaluated so that drivers' experiences in the simulator could be
extrapolated to other in-vehicle technologies. The older participants
perceived the strategies embedded in in-vehicle systems to be more
useful (F(1,160) = 4.63, p\<0.05) and more satisfactory (F(1,160) =
4.58, p\<0.05) than the middle-aged group (Figure 4. 7). There were
significant differences between different systems in terms of
satisfaction (*F*(5,160) = 2.42, *p*\<0.05). In general, all
participants were more satisfied with the operation of a visual
*advising* strategy (such as a red bezel) on their radio when compared
to an auditory *locking* strategy in a cell phone (*t*(160) = -3.35,
*p*\<0.001) or email (*t*(160) = -2.28, *p*\<0.05). Therefore, a visual
based alert which does not lock the in-vehicle systems task appears to
be more acceptable than an auditory alert.

![](media/image5.wmf){width="6.093055555555556in"
height="3.5597222222222222in"}

[]{#__RefHeading___Toc160355584 .anchor}Figure 4. 7. Acceptance of
mitigation strategies embedded in current in-vehicle systems

## Discussion

The experiment revealed that older drivers accepted strategies more than
middle-aged drivers. Older drivers generally have decrements in their
driving, task switching and divided attention performance (P.J. Cooper,
1990; Kray & Lindenberger, 2000; Verhaeghen, Steitz, Sliwinski, &
Cerella, 2003) and other studies have also shown that this age group
tend to trust in-vehicle systems more than other driver age groups (Fox
& Boehm-Davis, 1998a). These decrements in performance may lead to
greater workload and less self-confidence which may then lead to higher
acceptance and trust of strategies that can help older drivers maintain
safer driving. Middle-aged drivers may accept such strategies less
because they get fewer benefits in terms of safer driving and are
annoyed with the interventions in their own non-driving related
activities.

Auditory strategies were shown to be less accepted than the visual
strategies. Low levels of system accuracy resulted in lower levels of
trust. Trust was also found to be positively correlated with acceptance
measures: usefulness and satisfying. This is an important issue because
the trust in a system is likely to guide drivers' reliance on the
system. Information from systems with imperfect reliability may be best
conveyed to drivers using visual displays rather than auditory displays
because auditory displays tends to be trusted and perceived as generally
less useful.

The focus group and driving simulator studies provide complementary
perspectives as to whether strategies of adaptive in-vehicle systems to
mitigate distraction will be accepted by drivers and thereby reduce the
number of crashes and fatalities that occur each year. The insights
gained from the focus group helped develop a taxonomy of distraction
mitigation strategies. Two mitigation strategies from this taxonomy were
evaluated in a simulator experiment. Even though many focus group
participants indicated that they have been distracted while driving,
they do not want to give up their in-vehicle devices unless laws were in
place. Therefore, research in non-driving related strategies that will
help reduce the impact from these in-vehicle devices appears to be of
great value.

Little research has been done on trust and acceptance of in-vehicle
systems as it relates to age. The results of the driving simulator
experiment showed that older drivers accept and trust strategies that
autonomously modulate their in-vehicle system interaction (e.g. cell
phone conversation) more than the middle-aged drivers. Therefore, when
designing systems for middle-aged drivers, maintaining driver control of
the in-vehicle system interactions may be necessary if the system is to
be accepted.

Focus groups suggested that mitigation strategies presented in the
auditory format can be very annoying. The experiment verified this
finding. The auditory mitigation strategies were accepted less than the
visual based strategies. Therefore, when appropriate, warnings should be
conveyed visually rather than as a sound alert. However, when drivers
are cognitively distracted an auditory warning may be more effective
than a visual one, and a tradeoff between effectiveness and acceptance
would develop. In an imminent danger the system should aim for higher
effectiveness.

Another topic that was frequently mentioned in the focus group
discussions was the system accuracy. Drivers believed that the
mitigation strategies should be as accurate as possible. The experiment
showed that the system trust depends on the accuracy. Lower accuracy
resulted in less trust and studies show that distrust undermines
reliance and the benefits of a system (Lee & See, 2004). However, not
all false positive alarms are harmful. Such alarms can help drivers
understand the limits and capabilities of the system. If the first time
the driver receives a warning is in a true collision situation, the
driver may not respond to it in the amount of time available. False
positive alarms may also lead to more cautious driving and thereby
result in reduced false alarm rates (Parasuraman et al., 1997). Thus,
for a mitigation strategy to be effective, some acceptable false alarm
rate should be established.

A significant difference was not observed between high and low levels of
automation for the strategies tested. This may be because, for
strategies that modulate non-driving related tasks, the level of
automation does not have as great an impact as other factors. However,
based on the focus group insights, a difference may be found between
other types of automation, such as those that directly influence vehicle
control. Therefore, the acceptance and trust on the different levels of
automation for driving related strategies should also be assessed in
future studies.

# A realtime Index of visual distraction 

Driving involves complex interactions between the driver, vehicle, and
environment. Breakdowns in any of these interactions undermine driving
safety. The introduction of in-vehicle and carried-in devices (e.g.
navigational displays and iPods) raises concerns that the demands of
such systems may conflict with the demands of driving. Driver
distraction can be defined as diminished attention of the driver to the
driving task. A driver's willingness to engage in a non-driving task and
the attentional demands placed on the driver by that task can contribute
to distraction. Vehicular crashes caused by driver inattention and
resulting from driver distraction are a major concern (Brookhuis, De
Waard, & Fairclough, 2003; Stutts & Hunter, 2003; Wang, Knipling, &
Goodman, 1996). The growing number of potentially distracting devices
can further undermine safety (Verwey, Brookhuis, & Janssen., 1996.) and
pose even greater hazards to young drivers.

A multi-level description of driver behavior that describes driving at
strategic, tactical, and operational levels identifies reasons why
younger drivers may be particularly vulnerable (Lee & Strayer, 2004;
Poysti, Rajalin, & Summala, 2005). At the operational level, younger
drivers may fail to modulate their attention appropriately. Distracting
activities at this level may pose a particular risk to younger drivers
because they may neglect hazards and direct their attention to the
roadway less effectively (Fisher et al., 2002). Inexperienced drivers
make longer fixations, more pursuit eye movements, and scan smaller
areas of the visual scene compared to experienced drivers (Mourant &
Rockwell, 1972). With experience, their scanning adjusts to reflect the
spatial-temporal characteristics of hazardous situations (Brown &
Groeger, 1988). As an example, drivers' scanning becomes more sensitive
to road type with experience (Underwood, Chapman, Brocklehurst,
Underwood, & Crundall, 2003). Young drivers are typically inexperienced
or novice drivers, given the limited possible number of licensed driving
years. Crash data support on-road and simulator experiments in showing
that many young drivers crash as a result of failures of attention and
visual search (McKnight & McKnight, 2003). With respect to sharing the
attentional demands of the roadway with a secondary task, Wilkman et al.
showed that young drivers tend to look away from the road with longer
and more variable glances compared to those of more experienced drivers
(Wikman, Nieminen, & Summala, 1998). Specifically, 29% of the young
drivers had glances longer than three seconds, while none of the more
experienced drivers did.

At the tactical level, young drivers also have a higher risk for crash
involvement, in part because they are generally more likely to take
risks while driving (Deery, 1999). More specifically, young drivers may
be particularly vulnerable because of their greater propensity to engage
in distracting activities (Olsen, Lerner, Perel, & Simons-Morton, 2005).
Substantial evidence suggests that stable attitudes and behavioral
differences influence crash involvement (Parker, Manstead, Stradling, &
Reason, 1992). Specifically, a survey of college students' driving
behavior showed that those students who reported a high level of
sensation seeking were more likely to not wear seat belts and to drive
aggressively. High-sensation-seeking drivers were also more likely to
report that they would drive faster on highways and on wet roads if
driving a vehicle with anti-lock brakes (Jonah, Thiessen, & Au-Yeung,
2001). A survey of 198 drivers between the ages of 16 and 19 revealed
several distinct types of drivers, which varied according to risk-taking
propensity. Such risk-taking included speeding, driving-related
aggression, and hostility (Deery & Fildes, 1999). A subsequent simulator
study showed impaired attention-management performance of these drivers
in high-workload situations (Deery & Fildes, 1999). Overall, individual
differences associated with risk taking may have a strong effect on the
degree to which young drivers engage in distracting activities.

At the strategic level, young drivers make imprudent choices about when
to initiate trips and when to include passengers. While substantial
effort is made to train younger drivers, there is little evidence that
driver training programs actually serve to improve driver safety, and
they may actually undermine it if these programs allow drivers to be
licensed at a younger age (Evans, 1991). Such training can also increase
drivers' confidence in their abilities without enhancing their skill
(Gregersen, 1996); however, training young drivers to judge risks holds
promise (Fisher et al., 2002). As a complement to training, raising the
minimum driving age and introducing graduated licensing have both
reduced crash rates. Graduated licensing, which restricts the driving
privileges of young drivers during their first years of driving, is a
particularly promising approach. Crash risk is low during the learner
period (when drivers are accompanied by an adult) and particularly high
immediately after licensure, at night, with passengers, and after
consuming alcohol (McKnight & Peck, 2003). Graduated licensing
restrictions reduce risky elements of driving for young drivers with the
following restrictions: limiting nighttime driving, allowing only
driving only to and from school or work, permitting no young passengers,
and driving only when accompanied by an adult (Williams, 2003).
Restricting nighttime driving and eliminating young passengers appear to
be particularly successful (Lin & Fearn, 2003). These restrictions
safely extend the training of the young driver for several years and
reduce the crash rate for 16-year-old drivers by approximately 25%
(McCartt, 2001; Shope & Molnar, 2003). The success of graduated
licensing programs demonstrates how guiding young drivers at the
strategic level can enhance safety. Modulating when and what new
technologies young drivers use while driving could have an important
influence on driving safety.

Appropriate feedback can help modulate the impact of risk-taking
behavior. However, the poor feedback offered in most driving situations
gives drivers little opportunity to learn from their dangerous behavior.
In fact, even the dramatic feedback drivers receive when they are
involved in a fatal crash affects them only a little and for only a
short time (Rajalin & Summala, 1997)\`. Emerging technology makes it
possible to track glances of drivers and provide them with appropriate
feedback regarding their attention to in-vehicle devices. Ideally, a
system designed to provide such feedback would clearly discriminate
between risky and non-risky scanning behavior, otherwise drivers are
likely to regard the system as a source of annoyance rather than of
useful information. The objective of the present study is to assess the
glance behavior of a group of young drivers and assess how well an
algorithm provides feedback to guide risky drivers to more appropriate
glance behavior. For example, safe drivers with effective scanning
strategies should receive little feedback and risky drivers with poor
scanning strategies should receive substantial feedback. Our hypothesis
is that the interaction of the tactical and operational levels may lead
to systematic differences between young drivers that should be
considered in the algorithm used to generate feedback.

## Methodology

### Participants

Sixteen participants were recruited for this study. Each participant was
involved in the study for approximately 2 hours. Participants were paid
\$15 per hour, and then had the opportunity to earn a bonus of up to
\$10. The participants were college-age students between 18 and 21.
Drivers recruited had to be frequent drivers (i.e., they drove more than
three days a week) with at least 2 years of driving experience. The eye
tracker system failed to accurately track the eye movements of five
subjects. Therefore, these five subjects' data were excluded from the
analysis.

### Apparatus 

The experiments were conducted with a medium-fidelity, fixed-based
simulator powered by Global Sim, Inc.'s DriveSafety™ Research Simulator
(Figure 4. 8). The simulator has a 1992 Mercury Sable vehicle cab with a
50-degree visual field. The cab is equipped with force feedback steering
wheel, actual gauges, and a rich audio environment. A state-of-the-art
PC Hardware is employed to generate fully textured graphics with a 60-Hz
frame rate at 1024 x 768 resolution. The driving scenarios are created
by HyperDrive™ Authoring Suite. All graphics for roadway layouts,
markings, and signage conform to American Association of State Highway
and Transportation Officials (AASHTO) and Manual of Uniform Traffic
Control Devices (MUTCD) design standards. Driving data were collected at
60 Hz. A 7-inch LCD (60-Hz frame rate at 640 x 480 resolution) mounted
on the dashboard by a small stand was used in the experiment for the
presentation of the visual messages used in the secondary task. The
viewing angle from the driver's eye point is approximately 18 degrees.

![](media/image6.jpeg){width="4.815277777777778in"
height="3.6118055555555557in"}

[]{#__RefHeading___Toc160355585 .anchor}Figure 4. 8. In-vehicle display,
road scene and eye-tracking cameras

A Seeing Machines eye tracker was used to collect eye movement using the
FaceLab 4.0™ program (Figure 4. 8). FaceLab 4.0™ is an eye and head
tracking system that enables analysis of natural behavior by using a set
of cameras as a passive measuring device. The program allows the
researcher to create a virtual world consisting of the participant's
head-model, display screen, and in-vehicle device. The eye tracker used
both head and eye position to track participants' gaze direction. Thus,
the eye tracker recognized whether the participant was looking at the
road or at the in-vehicle display.

### Driving task

Participants completed one practice drive in addition to two
experimental drives: one under the distraction and the other under the
no-distraction condition. The experimental scenarios took place on a
two-lane rural road with oncoming traffic. Participants were instructed
to drive between 45 mph (72 km/h) and 50 mph (80 km/h), and to follow a
lead vehicle that periodically braked at a mild rate of deceleration
(0.2 g) for five seconds. Before a lead vehicle braking event, the lead
vehicle adjusted its speed to obtain a 1.8-second time headway. Twelve
braking events took place in each experimental drive. Half of these
braking events were on curves and the other half were on straight
sections. To make the scenario more realistic, different radius curves
were used; half of the curves had a 400-meter radius (three left and
three right turns) and the other half had a 200-meter radius (three left
and three right turns). A constant level of fog (sight distance: 300
meters) was employed during the scenario to decrease the drivers'
ability to anticipate an approaching curve. The order of curves and the
location of braking events on a road section were randomized for each
drive to reduce driver anticipation of an event.

### In-vehicle information system task

The distracted condition included a visual-manual secondary task. This
in-vehicle task provided a controlled exposure to the visual, motor, and
cognitive distraction associated with system interaction such as
observed when a driver is selecting a satellite radio station or
scanning an MP3 playlist. The experimental task consisted of selecting a
pre-defined song title from a list presented to the participant on the
in-vehicle touch screen display. A chime informed the driver when he or
she could begin the next task. The participant initiated the task by
touching the start button located on the upper middle of the display
(top gray box in Figure 4. 9). After 300 milliseconds, the task appeared
on the display. The three word target song title "Discover Project
Missions" was used throughout the experiment to simulate a real-life
search task in which the target is recalled from memory. Specifically,
the secondary task required drivers to locate a song title that has at
least one word in the same order as the target song name given. The
driver would then scroll through a list of ten closely related song
titles for a title that has either "Discover" first, "Project" second,
or "Missions" third. For example the song title "Project Discover
Misguide" will not be a match because no word is in the same order as
the target song. Whereas the song title "Discover Missions Project" is a
match because "Discover" is in the correct position. The participant
could scroll through the list using the up and down arrows and make a
selection by pressing "select" (Figure 4. 9). There was a 300 msec.
delay before a new string appeared on the display. Both the delay for
scrolling (300 msec.) and the task initiation (300 msec.) were used to
represent potential lags associated with the actual in-vehicle task. The
participants received an automatic auditory feedback regarding correct
and incorrect selections and subsequent compensation was dependent on
task performance. Participants received \$0.10 for each correct response
and lost \$0.03 for each incorrect response. This increased the
secondary task importance for the participants and gave them incentive
to perform to their best ability. There was no time limit on any task.
However, faster participants could perform more interactions and receive
more compensation. Therefore, the bonus system also provided an
incentive to be engaged in the task. For each task, the matching string
was randomly placed in the list. Once the participant identified a
match, the auditory feedback was provided. A five-second delay occurred
before the next task was displayed.

![](media/image7.png){width="3.35in" height="2.2333333333333334in"}

[]{#__RefHeading___Toc160355586 .anchor}Figure 4. 9. In-vehicle task
displayed on the LCD monitor

### Experimental design and independent variables

The experiment was a repeated measures design with one independent
factor consisting of two levels: non-distracted and distracted driving.
Every participant completed both of these experimental conditions with
twelve braking events each. The non-distracted condition assessed the
driving performance when no distraction was imposed. The distracted
condition investigated the effects of visual-manual distractions on
driving performance and eye glance behavior. The order of drivers was
counterbalanced across participants.

### Procedure 

Upon arrival, participants read and signed the study consent form and
were briefed on the activities that were to be completed. Participants
were initially asked to complete a five-minute practice drive. The
practice drive was designed to increase participant familiarity with the
simulator, the driving environment, and the in-vehicle task. After
completing the practice drives, participants performed the two
experimental drives (non-distracted or distracted), each lasting about
20 minutes. Participants also took a short break in between drives. At
the end of the study, participants were debriefed. The total test time
was approximately two hours.

### Dependent variables

Driving performance was assessed by accelerator release time and maximum
braking. Accelerator release time is defined by the time between when
the lead vehicle started to brake and when the participant released the
accelerator. The transition time represents the time between when the
driver released the accelerator and when the driver depressed the brake
over 2%. Maximum braking indicates the braking intensity and is defined
as the highest deceleration value reached by the participant.

The eye glance duration on the in-vehicle display was also analyzed to
investigate how drivers modulate their interactions with the in-vehicle
system and how these interactions affect driving performance. A glance
to the display was recorded when the driver's gaze vector intersected
with the in-vehicle display.

## Results

The distraction from the secondary task had a significant effect on
accelerator release times. In the distracted condition, drivers took an
average 0.33 seconds longer to release the accelerator in response to a
braking lead vehicle (*F*(1,254) = 20.84, *p*\<0.0001). The mean value
for the non-distracted condition was 0.84 seconds (standard
error =0.04), whereas the mean value for the distracted condition was
1.17 seconds (standard error =0.06).

In contrast to the longer accelerator release times, the drivers
implemented fast transition times from the accelerator to the brake
pedal in the distracted condition (*F*(1,256) = 3.88, *p*=0.05). When
distracted, drivers had 0.08-second shorter transition times compared to
when they were not distracted. Distractions also resulted in more abrupt
braking (*F*(1,252) = 15.22, *p*\<0.0005). Moreover, distracted drivers
had 0.38 m/s^2^ larger maximum decelerations compared to when they were
not distracted. Therefore, the faster transition time was not sufficient
to compensate for the late accelerator release.

To further explore the effects of distractions on driving performance of
different individuals, subjects were categorized as "risky" or
"non-risky" based on accelerator release time data. The decrement in
accelerator release time from the non-distracted to the distracted case
was calculated for each driver and used to rank the drivers. The
variability observed earlier is apparent in the risky driver group. For
example, one of the drivers had a 1.25 second longer mean accelerator
release reaction time when distracted compared to when not distracted.
The four drivers with the highest decrements and the four drivers with
the lowest decrements were categorized as risky and non-risky drivers,
respectively (Table 4. 4).

Risky and non-risky drivers were defined by their accelerator release
reaction times while engaged in the distracting task. Accordingly, it is
not surprising that risky drivers had a significantly longer reaction
time in that condition (*t*(8.71) = 3.69, *p*\<0.01). However, it is
interesting to note that the mean accelerator release times for the two
groups under the no-distraction condition were not significantly
different (*p*\>0.05) (Figure 4. 10). The difference between the two
groups seems to reflect poor management of the distracting task rather
than a generally slow reaction time.

When distracted, the risky group did brake more abruptly than the
non-risky group (*t*(6.56) = -4.71, *p*\<0.005) (Figure 4. 11). This is
not surprising since a late accelerator response would create a more
demanding situation requiring the driver to brake more abruptly. The
level of engagement in the distracting activity was assessed by an
analysis of the number of task sets completed per minute. The results
showed only a marginal difference (*t*(6) = 1.95, *p*=0.1). Drivers in
the risky group completed, on average, 1.74 sets per minute, whereas the
non-risky drivers had an average of 1.01 sets.

[]{#__RefHeading___Toc160304994 .anchor}Table 4. 4. Decrement in
accelerator release time from the non-distracted to the distracted
condition.

+-----------------------------+---------+-----------------------------+
|                             | Subject | ∆ Accelerator release time  |
|                             | Number  | (sec)                       |
|                             |         |                             |
|                             |         | (∆ = No distraction -       |
|                             |         | Distraction)                |
+-----------------------------+---------+-----------------------------+
| Risky:                      | 1       | -1.253                      |
|                             |         |                             |
| Larger decrement in driving |         |                             |
| performance under           |         |                             |
| distraction                 |         |                             |
+-----------------------------+---------+-----------------------------+
|                             | 2       | -0.704                      |
+-----------------------------+---------+-----------------------------+
|                             | 3       | -0.321                      |
+-----------------------------+---------+-----------------------------+
|                             | 4       | -0.301                      |
+-----------------------------+---------+-----------------------------+
| Transition group            | 5       | -0.282                      |
+-----------------------------+---------+-----------------------------+
|                             | 6       | -0.222                      |
+-----------------------------+---------+-----------------------------+
|                             | 7       | -0.175                      |
+-----------------------------+---------+-----------------------------+
| Non-risky:                  | 8       | -0.120                      |
|                             |         |                             |
| Smaller decrement in        |         |                             |
| driving performance under   |         |                             |
| distraction                 |         |                             |
+-----------------------------+---------+-----------------------------+
|                             | 9       | -0.053                      |
+-----------------------------+---------+-----------------------------+
|                             | 10      | 0.006                       |
+-----------------------------+---------+-----------------------------+
|                             | 11      | 0.019                       |
+-----------------------------+---------+-----------------------------+

![](media/image8.wmf){width="4.846527777777778in"
height="3.4229166666666666in"}

[]{#__RefHeading___Toc160355587 .anchor}Figure 4. 10. Accelerator
release time and transition times for different experimental conditions
and risk levels

![](media/image9.wmf){width="4.821527777777778in"
height="2.892361111111111in"}

[]{#__RefHeading___Toc160355588 .anchor}Figure 4. 11. Minimum
acceleration for different experimental conditions and risk levels

Figure 4. 12 shows a frequency distribution of the duration of glances
away from the road. The risky driver group had a higher number of longer
glances compared to the drivers in the non-risky group. Sixty three
percent (63%) of drivers made glances to the in-vehicle display for
three seconds or more. Of these, the risky drivers had 12.5 times more
glances to the in-vehicle display for three seconds or more. Thus, the
distribution of glance duration for the risky group appears to have a
longer tail. As categorized in Table 4. 4, three subjects (5, 6, and 7)
were excluded from the risk groups and were included in a transition
group. This was based on these subjects' transitory accelerator release
responses and eye glance patterns that were not clearly separable from
those of the two risk groups.

![](media/image10.wmf){width="5.996527777777778in"
height="3.3097222222222222in"}

[]{#__RefHeading___Toc160355589 .anchor}Figure 4. 12. Duration of
glances on the in-vehicle display for risky and non-risky driver
categories

An algorithm to identify risky scanning patterns was developed based on
eye glance durations. The ultimate purpose of this algorithm is to help
drivers recognize dangerous interactions with in-vehicle devices in real
time. To meet this need, an algorithm identified the degree of
distraction as a function of the current glance duration away from the
road, β~1~, and the total glance duration away from the road during the
last three seconds, β~2~. A three-second moving average of glance
duration has been shown to predict distraction (Zhang & Smith., 2004).
The relative influence of the current glance duration is dependent on
α.  These factors then define a threshold for the algorithm, γ, as:

$$\gamma = \text{αβ}_{1} + (1 - \alpha)\beta_{2}$$

The eye glance behavior of drivers was used to assess how the threshold,
γ, and weighting of current glance duration, α,  influenced the
frequency of alarms for both the risky and the non-risky groups of
drivers. Figure 4. 13 shows the average number of alarms per minute for
various levels of algorithm parameters for both the risky and non-risky
drivers. As one might expect, for a broad range of α and threshold, the
risky group received more warnings per minute than the non-risky group.

![](media/image11.wmf){width="5.997222222222222in"
height="3.060416666666667in"}

[]{#__RefHeading___Toc160355590 .anchor}Figure 4. 13. Number of alarms
for different alarm thresholds for different risk levels

## Discussion

This experiment demonstrates that the visual, cognitive, and motor
demands of an in-vehicle task, which is similar to the demands of
selecting a song from an MP3 playlist or a satellite radio station, can
distract young drivers. This task increased the average brake reaction
time by over 300 ms and is comparable to other studies of driver
distraction (Lamble, Kauranen, Laakso, & Summala, 1999; Lee et al.,
2001). This task also led drivers to look away from the road for long
periods. Similar to previous research, which found 29% of young drivers
looked away from the road for over three seconds (Wikman et al., 1998),
this study found that 63% of the drivers similarly engaged in glances of
such duration.

Unlike previous studies, this study demonstrated consistent differences
among drivers. One group behaved in a risky manner, looking away from
the road for long periods, responding more slowly to a braking lead
vehicle, and braking more abruptly. No significant differences were
observed in the secondary task performance. This result suggests that
the risky behavior was not a result of placing greater priority on the
secondary task at the tactical level of behavior. Instead, it seems that
the risky behavior stems from poor modulation at the operational level
of behavior. Because of the behavior, risky drivers compensated for the
interaction with the secondary task with decreases in performance in the
driving task. These results suggest that young drivers are not a
homogenous population and respond to distractions quite differently. One
limitation of this study is the small sample size. Despite the limited
sample size, however, there were strong differences between the risk
groups. Field validation with larger samples under actual driving
conditions would provide stronger support for the conclusions.

Previous studies on young drivers have shown substantial differences
between gender (Laapotti, Keskinen, Hatakka, & Katila, 2001) and among
personality variables such as sensation seeking (Jonah, 1997; Jonah et
al., 2001), as well as significant differences as young drivers begin to
accumulate driving experience (Williams, 2003). Substantial research
suggests that young drivers take more risks than mature drivers.
However, several studies also cast doubt on this assumption. There is
research suggesting that age and gender may play a minor role compared
to stable individual differences (Rimmo, 2002). Analysis of the driving
records of 149,000 British Columbian novice drivers between the ages of
16 and 55 found no evidence that younger drivers were more risky
compared to older novice drivers (P. J. Cooper, Pinili, & Chen, 1995).
However, drivers 16 to 18 carrying passengers did show a
disproportionate crash risk during their first year of driving. A
similar study compared the crash and conviction rates of 28,500 Finnish
novice drivers in age brackets of 18 to 20, 21 to 30, and 31 to 50 years
old. The results show young novice drivers, particularly males, had a
greater number of strategic and tactical errors, and that females tended
to have greater problems at the operational level of driving behavior
(Laapotti et al., 2001). Overall, research suggests drivers differ in
their tendency to take risks and that these differences influence
driving safety. Substantial evidence suggests that young male drivers
are particularly likely to drive in a risky manner. The strong
differences between the risky and non-risky young drivers in this study
suggest that a subgroup of young drivers exist that might particularly
benefit from feedback regarding their risky behavior.

The most effective means of delivering feedback to this group of risky
drivers depends on the factors that produce the behavior. If the risky
behavior stems from knowingly incurring greater risk at the level of
tactical driving behavior, then analysis of drivers' decision making
capabilities and better training might be most effective. If risky
behavior stems from poor modulation of attention at the level of
operational driving behavior, then real-time feedback that directs
drivers' attention back to the road might be most effective.

Non-risky drivers with effective scanning strategies should receive less
feedback, while risky drivers with poor scanning strategies should have
more substantial feedback. Therefore, an algorithm, sensitive to these
differences would make it possible to provide risky drivers with greater
feedback than relatively non-risky drivers. Assessing a range of
algorithm parameters shows that parameters can be selected so that
drivers with risky behavior receive a great deal of feedback and
non-risky drivers receive relatively little. Our study demonstrated a
simple, real-time algorithm that is quite sensitive to differences in
risky behavior and thus provides non-risky drivers with relatively
little feedback.

The manner in which real-time feedback is delivered could have dramatic
consequences for effectiveness and driver acceptance. Donmez et al.
(2003) described a taxonomy of distraction mitigation techniques that
demonstrates a wide range of alternate methods for delivering feedback.
*Locking* is one such strategy; *advising* is another. With the locking
strategy, feedback could be delivered to the driver by blanking the
screen of the in-vehicle device, or in the case of a cell phone, cutting
off the conversation. In contrast, with the advising strategy, a bezel
surrounding the screen of the device could be illuminated when the
driver appears dangerously distracted. With advising, drivers can
continue the interaction, but with locking they cannot. A recent study
compared efficacy and acceptance of advising and locking strategies that
guided drivers' attention back to the roadway during high-demand events
such as a braking lead vehicle or the approach to a curve (Donmez, Boyle
et al., 2006b). Both advising and locking enhanced the performance of
distracted drivers. This suggests that these same strategies might be
most effective in providing drivers with real-time feedback regarding
risky glance behavior.

Even with these strategies, a critical concern is how drivers might
respond to a large amount of feedback. Too many alerts, even for drivers
with risky behavior, might be inappropriate in that it could annoy
drivers and result in disuse of the system. Likewise, too few alarms for
relatively non-risky behavior may be insufficient for drivers to develop
clear expectations for how the system behaves. The alerted monitor
concept and likelihood alarm displays offer a promising solution to this
issue (Sorkin, Kantowitz, & Kantowitz, 1988). For example, a proposed
methodology for a two-tier advising strategy could use a threshold of
120 milliseconds for a subtle alarm, and 150 for a more salient alarm.
Although the results of this experiment are very promising, issues of
driver acceptance and willingness to adapt will determine whether
providing drivers with feedback regarding their eye glance behavior can
mitigate the growing problem of distraction for young drivers.

# Concurrent feedback to Mitigate Distraction

Feedback can help an operator learn how a system or the environment is
changing (Vakil & Hansman, 2002) and is especially important in complex
environments and systems such as observed in aviation, nuclear power
plants, and driving. The driving environment can change very rapidly and
the driver may fail to track these changes, particularly if the driver's
attention is captured by a non-driving related activity or if the driver
is cognitively loaded (Haigney & Westerman, 2001; Horrey & Wickens,
2006). In such situations, feedback can help the driver adhere to
environmental changes more appropriately. It is important to note that
feedback should not increase the cognitive load of the driver because
concurrent feedback can interfere with ongoing task performance (Arroyo
et al., 2006; Corbett & Anderson, 2001).

Previous studies have evaluated the potential benefits of different
driver distraction mitigation strategies to alert the drivers of roadway
events (Donmez, Boyle et al., 2006a, 2006b). These studies presented a
taxonomy of distraction mitigation strategies, of which two have been
evaluated: an advising strategy that warned drivers of a safety-critical
roadway event (i.e., a lead vehicle braking or an approaching curve),
and a locking strategy that stopped drivers from engaging in an IVIS
during these same events. The study showed that a locking strategy was
beneficial in improving driving performance during engagements in visual
distractions (Donmez, Boyle et al., 2006b). However, a disadvantage of
these aforementioned strategies is the inability to effectively warn
drivers of prolonged engagement in the IVIS. Some distractions may
degrade driving performance to safety critical levels even on straight
roads with low levels of traffic. Moreover, high levels of distraction
can be an indicator of a possible performance decrement. Providing
feedback when the driver is highly distracted can help avoid future
hazardous maneuvers.

Another disadvantage of mitigation strategies based only on the roadway
state concerns non-useful alarms. Although there is a roadway event,
such as a curve, the driver may actually be focused on the driving task
and be able to respond quite appropriately. An alarm provided in this
situation can degrade system acceptance and result in frustration, which
itself is a type of emotional distraction that can have a negative
effect on traffic safety (Burns & Lansdown, 2000). This can be avoided
by giving drivers feedback based on their attentional state rather than
just the roadway state. Moreover, compared to warnings based on roadway
events, which can have an impact on immediate performance, feedback on
driver's engagement in distractions can generate a long-term behavioral
change.

Research investigating ways to direct a driver's attention to the
roadway is mostly focused on providing drivers with alerts based on
roadway events, such as forward collision (Lee et al., 2002) or lane
departure warning systems (Suzuki & Jansson, 2003). Although the concept
of adaptive automation has been previously investigated (Parasuraman &
Hancock, 2001; Rouse, 1994), the application in the driving domain has
been limited. Emerging technologies, such as non-intrusive eye tracking
systems, make it possible to monitor the level of driver distraction and
adjust feedback provided to the driver accordingly. Such systems may be
able to monitor the vigilance levels of the driver (e.g., an
alertometer) (Knipling et al., 1993). Other systems may be capable of
filtering incoming phone calls when a real-time workload estimate
exceeds some threshold value (Piechulla, Mayser, Gerkhe, & Konig, 2003).

A system that monitors distraction would ideally generate a continuous
indicator that identifies when the driver is too distracted to respond
appropriately. This indicator could be used to warn drivers, or even to
take over vehicle control. There are several variables that can be used
to identify the distraction level while driving (e.g. heart rate
variability, eye movements, and driving performance measures). However,
the nature of the task will have an influence on the appropriate
measure. For example, off-road glances would not be a good indicator of
auditory or cognitive distractions, but are quite useful for visual
distractions. Off-road glances have also been widely used in the
evaluation of distractions for non-driving tasks (Hoffman, Lee, McGehee,
& Gellatly, 2005; Sodhi, Reimer, & Llamazares, 2002), driver experience
(Underwood et al., 2003), and driver fatigue (Ji, Zhu, & Lan, 2004).

The location of the feedback may also influence its effectiveness.
Visual feedback embedded in IVIS can be more effective for mitigating
IVIS distractions since that is where the drivers' attention is
centered. However, visual feedback presented elsewhere may mitigate
distractions other than those associated with the IVIS. For example, if
the driver is talking to a passenger or viewing a map, visual feedback
that is embedded in an IVIS may be less effective. Thus, different
distractions may require different feedback mechanisms. To avoid the
potential information overload due to multiple feedback types, one
feedback central to the vehicle can be implemented.

This study investigates whether real-time visual feedback regarding
drivers' off-road glances can alter drivers' interactions with IVIS and
enhance driving performance. It is hypothesized that the distraction
associated with visual tasks will undermine driving performance and that
real-time feedback based on eye movements will lead drivers to glance
away from the road less frequently and for shorter periods. The more
frequent sampling of the road can result in better timesharing between
driving and the distracting task, and therefore enhance driving
performance. Two locations are tested for feedback in this study:
IVIS-centered, i.e. incorporated within the IVIS, and vehicle-centered,
i.e. displayed on the dashboard. It is hypothesized that IVIS-centered
feedback will be more effective than vehicle-centered feedback.

##  Methods

### Participants 

Equal numbers of participants were recruited in two age groups: young
(18-21) and middle aged (35-55). There were 36 drivers in the study.
However, due to the reliability of the eye tracker, seven participants'
data had to be omitted. The final analysis consisted of 29 subjects: 16
young (mean: 19.5, S.D.: 0.9, 7 male and 9 female) and 13 middle-aged
drivers (mean: 43.6, S.D.: 5.5, 7 male and 6 female). All participants
possessed a valid U.S. driver's license, had at least one year of
driving experience, were native English speakers, and had no driving
simulator experience in the last two years. Participants were
compensated \$15/hour for their participation and had the opportunity to
receive up to \$10 extra based on their secondary task performance.

### Apparatus 

The experiment was conducted with a medium fidelity, fixed-based
simulator powered by Global Sim, Inc.'s DriveSafety™ Research Simulator.
The simulator used a 1992 Mercury Sable vehicle cab with a 50-degree
visual field. The cab is equipped with force feedback steering wheel and
actual gauges. The driving scenarios were created by HyperDrive™
Authoring Suite. All graphics for roadway layouts, markings, and signage
conform to American Association of State Highway and Transportation
Officials (AASHTO) and Manual of Uniform Traffic Control Devices (MUTCD)
design standards.

A FaceLab 4.1™ eye tracker, which uses cameras as passive measuring
devices, was used to collect eye movement data. The eye tracker used
both head and eye position to track participants' gaze direction. Thus,
the eye tracker recognized in real time whether the participant was
looking at the road or at the in-vehicle display. Eye-tracking and
driving data were collected at 60 Hz.

A 7-inch touch-screen LCD (60-Hz frame rate at 640 x 480 resolution)
mounted on the dashboard above the center instrument panel was used for
presentation of the visual messages for the secondary task as well as
feedback (for IVIS-centered distraction-feedback). The display was
positioned 40 cm (15 in) to the right of the center of the steering
wheel and 8 cm (3 in) above the center of the speedometer. With respect
to the driver, the screen was turned approximately 15˚ toward the driver
and located 33˚ lateral to and 15˚ below the driver's line of sight. The
messages displayed on this LCD were designed using Courier New font type
with 21- point size. The approximate visual angle for text height was
1˚.

An LED strip, positioned on top of the dashboard in line with the
steering wheel, was used to present feedback separate from the
in-vehicle display (i.e. vehicle-centered distraction-feedback).

### Driving task 

Participants completed one practice drive in addition to four
experimental drives (each approximately 15 minutes). The drives took
place on two-lane curvy rural roads with oncoming traffic. The
participants were instructed to drive at a comfortable speed that was
not above the speed limit of 73 km/h (\~ 45 mph) and follow a lead
vehicle that periodically braked at a rate of 0.2 g (gravitational
acceleration) for five seconds. Before a lead vehicle braking event, the
lead vehicle speed was smoothly adjusted to obtain constant time
headways of 1.8 seconds. Twelve braking events took place in a drive.
The participants were told that the lead vehicle would be braking
periodically. Half of these braking events were on curves and the other
half were on the straight sections of the drive. To make the scenario
more realistic different radius curves were used; half of the curves
were 400 meter radius (three left turn, three right turn) and the other
half were 200 meter radius (three left turn, three right turn). A
constant level of fog (sight distance: 300 m) was employed during the
scenario to decrease the drivers' ability to anticipate an approaching
curve.

For analysis purposes, the dependent variables for lead vehicle braking
events and curve negotiations were averaged over 12 braking events and
12 curves, respectively. This was done to overcome any possible learning
effects during a drive and obtain a more accurate estimate of a general
driver response.

### In-vehicle information system task 

The secondary task provided a controlled exposure to the visual, motor,
and cognitive distraction typical of many IVIS interactions (e.g.,
selecting a satellite radio station or scanning an MP3 play-list). The
task consisted of selecting a match with the phrase, "Discover Project
Missions", from a list presented on the in-vehicle display. A chime
indicated when the task was ready. The participant initiated the task by
touching the start button. After 300 milliseconds, the task appeared on
the display. The three word target phrase "Discover Project Missions"
was used throughout the experiment to simulate a real-life search task
in which the target is recalled from memory. The participant then
scrolled through a list of ten closely related phrases, for a title that
has either "Discover" first, "Project" second, or "Missions" third. For
example, "Discover Missions Project" is a match because "Discover" is in
the correct position whereas "Project Discover Misguide" is not.

The participant could scroll through the list using the up and down
arrows and make a selection by hitting the select button. There was a
300 milliseconds (ms) delay before a new string appeared on the display.
Both the delay for scrolling (300 ms) and the task initiation (300 ms)
were used to represent potential lags associated with actual IVIS tasks.
The participants received an automatic auditory notification regarding
correct and incorrect selections and subsequent compensation was
dependent on task performance. Participants received \$0.10 for each
correct response and lost \$0.03 for each incorrect response. This
increased the secondary task importance for the participants and gave
them incentive to perform to their best ability. There was no time limit
on any task. However, faster participants could perform more
interactions and receive more compensation. Therefore, the bonus system
also provided an incentive to be engaged in the task. For each task, the
matching string was randomly placed in the list. Once the participant
identified a match, the auditory notification was provided. A
five-second delay occurred before the next task was displayed.
Participants were also instructed to interact with IVIS only when they
felt comfortable doing so while driving. That is, drivers were able
choose when to interact with the IVIS, and postpone interactions when
needed. This made it possible to assess how feedback affects their
interactions with distracting tasks.

### Experimental design and independent variables

The experiment was a 2 (age) x 4 (feedback) mixed factorial design with
feedback as a repeated measure. Age included two groups: young and
middle aged. Feedback had four levels: no secondary task, secondary
task, IVIS-centered distraction-feedback, and vehicle-centered
distraction-feedback. The no secondary task condition assessed
performance without the IVIS task. The secondary task condition
investigated the effects of the IVIS task on driving and eye glance
behavior. The distraction-feedback was based on an algorithm that
identified risky visual scanning patterns. The algorithm defined the
degree of distraction as a function of the current off-road glance
duration, *β*~1~, and the total off-road glance duration during the last
3 sec, *β*~2~, with the relative influence of the current glance
duration as *α*.  A 3 sec moving average of glance duration has been
shown to predict distraction (Zhang & Smith., 2004). These factors then
defined a momentary value of distraction, *γ*, for the
algorithm:$\gamma = \text{αβ}_{1} + (1 - \alpha)\beta_{2}$. The proposed
two-tier distraction-feedback used a threshold, *γ *\`, of 2 sec for a
less salient alarm, and *γ *\`\`, of 2.5 sec for a more salient alarm
with *α* of 0.2. The parameters *γ *\`, *γ *\`\`, and *α*,  were chosen
based on the results of a preliminary experiment conducted by the
authors (Donmez, Boyle, Lee et al., 2006). For IVIS-centered
distraction-feedback, a yellow strip appeared on the top portion of the
display for the less salient alarm if the 2 sec threshold was exceeded.
If the 2.5 sec threshold was exceeded, then orange strips also appeared
on both sides of the yellow strip to create the more salient alarm. Once
the driver's gaze switched back to the road, the alerts disappeared
after 1 sec. The same logic was used for the vehicle-centered
distraction-feedback for which feedback was provided through an LED
strip on the dashboard.

### Dependent variables 

For lead vehicle braking events, accelerator release times, minimum TTC
(i.e., shortest time to collision if the vehicles are to proceed with
their current speeds) and minimum acceleration (or maximum deceleration)
were analyzed. As outlined in Figure 4. 14, the response process can be
explained in three phases. A pre-event phase defines how the driver
adapts, and how that adaptation influences initial conditions at the
event onset. An event-response phase describes the driver's closed-loop
behavior in accommodating the event. The outcome of the response process
defines the safety parameters. The mitigation strategy might have an
influence at each phase of the driving process and each are investigated
with different dependent measures as shown in Figure 4. 14.

![](media/image12.wmf){width="6.0993055555555555in"
height="3.4770833333333333in"}

[]{#__RefHeading___Toc160355591 .anchor}Figure 4. 14. Acceptance of
mitigation strategies by age group and presentation modality.

Accelerator release time is defined by the time between when the lead
vehicle started to brake and when the participant released the
accelerator. Compared to other measures, the accelerator release time is
a more direct measure of initial reaction because the reactions that
follow accelerator release, such as transition time from the accelerator
to brake pedal, are dependent on this initial reaction. The primary
safety measures for the braking events are defined by the minimum
time-to-collision and minimum acceleration. Minimum TTC is the shortest
time-to-collision during a braking event if the participant were to
continue in the same path at the same velocity. Thus, an increase in
this variable would indicate a safety benefit. TTC has been proposed and
used as a crash-avoidance metric in forward collision avoidance systems
(Minderhoud & Bovy, 2001; Vogel, 2003). Minimum acceleration is defined
as the lowest acceleration (or highest deceleration) value reached by
the participant, indicating the braking intensity. An increase in the
minimum acceleration would also indicate a safety benefit. More severe
braking associated with a greater maximum deceleration increases the
probability of being struck from the rear (Lee et al., 2002). Driver
speed before the lead vehicle started to brake is a measure of driver's
speed maintenance behavior while following a lead vehicle.

For curve negotiation performance, steering entropy (Nakayama, Futami,
Nakamura, & Boer, 1999) was used to assess erratic steering. Steering
entropy links steering performance with smoothness of control. The
algorithm uses data from the previous 0.5 seconds and a second-order
Taylor series expansion to predict the next steering wheel position. The
entropy increases with more error corrective maneuvers which cannot be
predicted from previous steering maneuvers and driver's fundamental
steering behavior (as assessed from baseline data). The speed while
entering curves was also investigated and evaluated at the two seconds
before a curve.

Eye glances on the in-vehicle display were used to assess the level of
IVIS interaction. Perceived risk and mental effort questionnaires were
completed after each drive. The risk questionnaire requested that
drivers match the scenario they just drove to one of ten driving
situations (e.g. 1: driving on an easy road with no traffic,
pedestrians, or animals while perfectly alert; 4: driving 10 miles and
hour faster than traffic on an expressway; 8: driving just under the
legal alcohol limit with observed weaving in the lane; 10: driving with
my eyes closed -- a crash is bound to happen every time I do this). The
rating scale mental effort questionnaire assessed subjective mental
effort by having participants indicate their level of mental effort on a
continuum that went from 0 to 150. An acceptance questionnaire (Van Der
Laan et al., 1997), which assessed two components of acceptance:
usefulness and satisfaction, was completed for the distraction-feedback
conditions. For analysis, the responses were coded from -2 (lowest) to
+2 (highest). The participants were also asked to indicate if feedback
has enhanced their driving performance. The response was collected on a
5 point Likert scale, ranging from 'strongly disagree' to 'strongly
agree' with the middle response being 'neutral'.

##  Results

The mixed linear model (PROC MIXED statement in SAS 9.1) was used to
analyze continuous dependent measures. This model can accommodate
unbalanced designs (e.g. different number of subjects) and repeated
measures on subjects where the residuals are not independent. The mixed
linear model also enables a variance-covariance matrix to be adequately
incorporated for repeated measures. There are different types of
variance covariance matrices (e.g. compound symmetry, unstructured,
first order autoregressive). The best fit to the data can be obtained by
using an information criterion such as Akaike or Bayesian information
criteria. These criteria maximize likelihood while penalizing models
with many parameters to keep the model parsimonious (Akaike, 1979;
Littell, Henry, & Ammerman, 1998). A compound symmetry covariance
structure was chosen for repeated measures based on these two criteria.
Tables 4.5, 6, 7 include F-test results on main and interaction terms
for all dependent variables presented in the methods section. In order
to control for Type 1 error rate, pair-wise comparisons were performed
only on the effects that were significant. The significant pair-wise
comparisons of the least square means, with the p-values adjusted using
the Tukey-Kramer procedure are further discussed.

[]{#__RefHeading___Toc160304995 .anchor}Table 4. 5. Analyses of variance
(F-tests) for braking response.

![](media/image13.wmf){width="7.211111111111111in"
height="1.5472222222222223in"}

Note: n.s. indicates not significant, or p\>0.05

[]{#__RefHeading___Toc160304996 .anchor}Table 4. 6. Analyses of variance
(F-tests) for eye-movements.

![](media/image14.wmf){width="6.997222222222222in"
height="1.7944444444444445in"}

Note: n.s. indicates not significant, or p\>0.05

[]{#__RefHeading___Toc160304997 .anchor}Table 4. 7. Analyses of variance
(F-tests) for questionnaire responses.

![](media/image15.wmf){width="6.997222222222222in"
height="1.4958333333333333in"}

Note: n.s. indicates not significant, or p\>0.05

Drivers with a secondary task maintained lower speeds while entering a
curve (*t*(80.1) = -3.54, *p*\<0.001, ∆[^1] = -0.71 m/s, CI[^2]: -1.11,
-0.31) (Figure 4. 15a), and when the lead vehicle started to brake
(*t*(80.1) = 2.92, *p*\<0.005, ∆ = -0.75 m/s, CI: -1.27, -0.24) (Figure
4. 16a). However, no significant difference in driver performance was
observed when drivers were given a feedback with the distracting task
(*p*\>0.05). Middle aged drivers generally had higher steering entropy,
i.e. more steering instability, when compared to younger drivers
(*t*(27.1) = 2.67, *p*\<0.05, ∆ = 0.08, CI: 0.02, 0.14) (Figure 4. 15b).
Regardless of age, drivers performing the secondary task had greater
steering entropy (*t*(80.4) = 12.41, *p*\<0.0001, ∆ = 0.22, CI: 0.18,
0.25), even if they maintained lower speeds entering a curve as
presented above. This instability was not mitigated by the
distraction-feedback (*p*\>0.05).

[]{#_Ref121981118
.anchor}![](media/image16.wmf){width="4.500694444444444in"
height="2.1819444444444445in"}

![](media/image17.wmf){width="4.502083333333333in"
height="2.147222222222222in"}

Figure 4. 15. Estimated means and 95% confidence intervals for curve
negotiation measures: (a) curve entry speed and (b) steering entropy.

The secondary task also resulted in degraded braking performance as
depicted by both longer accelerator release times (*t*(80.2) = 3.95,
*p*\<0.0005, ∆ = 0.40 sec, CI: 0.20, 0.60) (Figure 4. 15b) and smaller
minimum accelerations (*t*(80.1) = -2.52, *p*\<0.05, ∆ = -0.24 m/s^2^,
CI: -0.43, -0.05) (Figure 4. 15c) when compared to the no secondary task
condition. Intense braking could not compensate for the late accelerator
release times resulting in shorter minimum TTC for drivers who performed
the secondary task (*t*(80.2) = -3.18, *p*\<0.005, ∆ = -1.84 sec, CI:
-3.0, -0.69) (Figure 4. 15d). Feedback generated no benefits in lead
vehicle braking response or speed maintenance. When drivers were
presented with the distraction feedback, their accelerator release time,
minimum acceleration, and minimum TTC were not significantly different
than the secondary task condition (*p*\>0.05). Even if a significant
benefit was not observed, there was also no evidence suggesting that
feedback was imposing additional distraction on drivers or degrading
their driving safety.

![](media/image18.wmf){width="4.000694444444444in"
height="1.9083333333333334in"}![](media/image19.wmf){width="3.9993055555555554in"
height="1.9618055555555556in"}

![](media/image20.wmf){width="4.001388888888889in"
height="1.9256944444444444in"}

[]{#_Ref126546205
.anchor}![](media/image21.wmf){width="3.9993055555555554in"
height="1.9527777777777777in"}

Figure 4. 16. Estimated means and 95% confidence intervals for lead
vehicle braking event response measures: (a) Speed when lead vehicle
starts to brake (b) Accelerator release time (c) Minimum acceleration
(negative maximum deceleration) (d) Minimum time to collision for each
condition.

The distraction feedback led drivers to look at the in-vehicle display
less frequently (Figure 4. 17a) (*IVIS-centered vs. secondary task:*
*t*(53.2) = -2.88, *p*\<0.01, ∆= -1.9 glances/min, CI: -3.3, -0.6;
*vehicle-centered vs. secondary task:* *t*(53.2) = -3.96, *p*\<0.0005, ∆
= -2.7 glances/min, CI: -4.0, -1.3). Moreover, the duration of glance to
the roadway between each in-vehicle glance was longer given the feedback
(Figure 4. 17b) (*IVIS-centered vs. secondary task:* *t*(53.2) = 2.53,
*p*\<0.05, ∆= 0.18 sec, CI: 0.04, 0.33; *vehicle-centered vs. secondary
task:* *t*(53.2) = 2.85, *p*\<0.0001, ∆ = 0.21 sec, CI: 0.06, 0.35).
Thus, drivers' engagement in distracting activities appear to be
positively altered.

![](media/image22.wmf){width="4.499305555555556in"
height="2.6243055555555554in"}

![](media/image23.wmf){width="4.501388888888889in"
height="2.642361111111111in"}

[]{#__RefHeading___Toc160355594 .anchor}Figure 4. 17. Estimated means
and 95% confidence intervals for (a) number of glances (per minute) on
the in-vehicle display and (b) mean duration of roadway-glances.

To assess the glance behavior after feedback was displayed, glance
durations to the in-vehicle display after the value of distraction
(*γ*) exceeded the thresholds (*γ *\`,* γ *\`\`) were analyzed. Once
*γ *\` was exceeded, younger drivers looked at the display longer than
middle-aged drivers (Figure 4. 18a) (*t*(27.2) = 2.26, *p*\<0.05, ∆ =
0.13 sec, CI: 0.01, 0.24). When *γ *\`\` was also exceeded, the younger
drivers again looked at the in-vehicle display longer than the
middle-aged drivers (Figure 4. 18b) (*t*(27.3) = 3.63, *p*\<0.005, ∆ =
0.20 sec, CI: 0.09, 0.31). Therefore, regardless of feedback presence,
younger drivers had longer glances on the in-vehicle display once the
feedback thresholds were exceeded. For both first and second level
feedback, the IVIS-centered and vehicle-centered feedback appeared to
have lower means of additional glance duration when compared to the
secondary task condition (Figure 4. 18). However, these did not reach
statistical significance.

![](media/image24.wmf){width="4.497222222222222in"
height="2.6395833333333334in"}

![](media/image25.wmf){width="4.500694444444444in"
height="2.676388888888889in"}

[]{#__RefHeading___Toc160355595 .anchor}Figure 4. 18. Estimated mean
glance duration (and 95% confidence interval) on the in-vehicle display
after (a) γ \` is exceeded and (b)  γ \`\` is exceeded.

![](media/image26.wmf){width="4.496527777777778in" height="2.175in"}
![](media/image27.wmf){width="4.5in" height="2.1743055555555557in"}

![](media/image28.wmf){width="4.500694444444444in"
height="3.702777777777778in"}

[]{#__RefHeading___Toc160355596 .anchor}Figure 4. 19. Subjective
responses (estimated means and 95% confidence intervals) for (a)
perceived risk, (b) mental effort, and (c) acceptance of
distraction-feedback.

Drivers also perceived greater risk (*secondary task vs. no secondary
task:* *t*(53.2) = 2.53, *p*\<0.05, ∆= 0.18, CI: 0.04, 0.33;
*vehicle-centered vs. secondary task:* *t*(53.2) = 2.85, *p*\<0.0001, ∆
= 0.21, CI: 0.06, 0.35) and mental effort (*secondary task vs. no
secondary task:* *t*(81) = 3.24, *p*\<0.0001, ∆= 29.6, CI: 23.2, 36.1)
given the secondary task (Figure 4. 19a, Figure 4. 19b). Neither the
perceived risk nor the mental effort was significantly different between
the secondary task and feedback conditions (*p*\>0.05). In general,
drivers had a positive attitude towards feedback as indicated by the 95%
confidence intervals for the estimated mean usefulness for each feedback
condition (i.e., greater than zero) (Figure 4. 19c). The mean
satisfaction responses were also greater than zero, however the 95%
confidence intervals included zero and thus cannot be conclusively
stated.

Drivers were asked if the performance of feedback enhanced their driving
performance. The responses indicate that 32 'agreed' or 'strongly
agreed' that feedback enhanced driving performance, 19 were 'neutral',
and only 5 'disagreed' (Table 4. 8). A multinomial logit model was fit
to the data to assess if there were any differences between the two
different feedback types and the two age groups. PROC GLIMMIX in SAS 9.1
was used to analyze the data with multinomial distribution and
cumulative logit link function. The results showed no significant
differences (p\>0.05). This further suggests that in addition to finding
feedback useful, the drivers generally believed that feedback enhanced
their driving performance regardless of feedback location.

[]{#__RefHeading___Toc160304998 .anchor}Table 4. 8. Subjective responses
to whether or not performance of feedback enhanced their driving
performance.

+-----------+---------------+---------+--------+------+-----+--------+
| Age       | Feedback      | R       |        |      |     |        |
|           |               | esponse |        |      |     |        |
+-----------+---------------+---------+--------+------+-----+--------+
|           |               | S       | Di     | Neu  | Ag  | St     |
|           |               | trongly | sagree | tral | ree | rongly |
|           |               |         |        |      |     |        |
|           |               | d       |        |      |     | agree  |
|           |               | isagree |        |      |     |        |
+-----------+---------------+---------+--------+------+-----+--------+
| Young     | IVIS-centered | 0       | 2      | 7    | 5   | 2      |
+-----------+---------------+---------+--------+------+-----+--------+
|           | Veh           | 0       | 1      | 5    | 10  | 0      |
|           | icle-centered |         |        |      |     |        |
+-----------+---------------+---------+--------+------+-----+--------+
| Mi        | IVIS-centered | 0       | 1      | 4    | 7   | 1      |
| ddle-aged |               |         |        |      |     |        |
+-----------+---------------+---------+--------+------+-----+--------+
|           | Veh           | 0       | 1      | 3    | 7   | 0      |
|           | icle-centered |         |        |      |     |        |
+-----------+---------------+---------+--------+------+-----+--------+
|           | Total         | 0       | 5      | 19   | 29  | 3      |
+-----------+---------------+---------+--------+------+-----+--------+

## Discussion

The distraction-feedback based on drivers' off-road eye glances, was
evaluated in a driving simulator at two display locations (IVIS-centered
and vehicle-centered). Distractions had detrimental effects on driving
performance. Regardless of display location, the distraction-feedback
altered driver's interaction with IVIS, resulting in less frequent
glances on the display and longer roadway glances. Surprisingly there
were no significant benefits in their braking or steering response, even
though drivers looked at the device less frequently and glanced at the
road for longer periods. The lack of significance may be due to the low
demands placed on the driver in this study. A higher demand driving
condition with unexpected events may provide better assessment of
performance by creating more hazardous situations. Moreover, compared to
a warning system, feedback might take many hours of exposure before
substantial change in behavior is noted. This study found that feedback
changed glance behavior, but not directly curtailed glances. Warnings
for mitigating distraction effects can have an immediate effect on
performance as observed by Donmez, Boyle, & Lee (2006b), however may not
necessarily have long-term effects.

Feedback did not result in longer mean durations on the in-vehicle
display. Therefore, there was no evidence suggesting that feedback
imposed additional distraction on the driver. This is important because
there is a possibility that concurrent feedback can interfere with
ongoing task performance (Arroyo et al., 2006; Corbett & Anderson, 2001)
and eventually degrade performance rather than improving it.
Subjectively, the drivers also believed that their driving performance
was enhanced by feedback.

The drivers also found the strategies to be useful. This finding, in
conjunction with less frequent glances on the IVIS, suggests that the
drivers will comply with the mitigation strategies by adjusting their
engagement in distracting tasks. There were no differences between the
IVIS-centered and vehicle-centered feedback indicating that drivers
benefited from a visual alert regardless of where it was located. It
would therefore appear that a mitigation system can provide feedback to
the driver for IVIS distractions as well as other distraction types.

Younger drivers tend to glance longer at IVIS (when compared to middle
aged drivers) when the momentary value of distraction reached a certain
threshold value (*γ *\` and *γ *\`\`). This was observed whether or not
feedback was provided along with the distraction. Wikman, Nieminen, &
Summala (1998) also showed that young drivers tend to look away from the
road with longer and more variable glances compared to those of more
experienced drivers. However, both age groups still benefited from
feedback as suggested by less frequent off-road glances and longer
on-road glances. This shows that the system can work for the two age
groups evaluated. Subjectively, drivers also perceived a benefit that
was supported with the outcomes of the eye tracking data. Even if a
significant driving performance improvement was not observed with
feedback, the drivers still found feedback to be beneficial and changed
their behavior. The acceptance of a system is important as it may in
turn influence effectiveness. This experiment showed that there is a
relation between acceptance and the perception of effectiveness. That
is, drivers, who perceived their performance to be enhanced, also
accepted feedback.

The age groups evaluated in this study was limited. Because this is one
of the first studies to assess effects of distraction-feedback, it was
important to control as many variables as possible including the age
group to discern whether differences exist in mitigating distraction
based on the driver state. For this reason, the sample of young drivers
was limited to18 to 21 because studies have shown that there are
significant differences between the driving performance of drivers 18 to
21 years old compared to other age groups (Arnett, 2002; Mayhew,
Simpson, & Pak, 2003). With this, the driving performance variability
due to the differences in the young age group was aimed to be minimized.
Middle-aged drivers, between 35-55 years of age are included in this
study as a baseline since this group typically have the lowest crash
risk (P. J. Cooper, 1990; McGwin & Brown, 1999). It is also important to
understand how a higher crash risk group will respond to distraction
mitigation strategies. Unlike the middle-aged drivers, young drivers
have higher crash risks and overestimate their own skills (Deery, 1999;
Gregersen, 1996; Williams, 2003). In addition, compared to more
experienced drivers, young drivers have prolonged off-road glances when
they have to perform a non-driving related visual task (Wikman et al.,
1998). Other age groups should also be investigated in future research.

In this study, the mitigation strategy was based on off-road eye
glances. As stated in the introduction, a distraction mitigation
strategy can be designed to consider many different factors that
influence driver performance. It is important to establish which of
these indicators is best to alert drivers, and what thresholds these
alerts should use. For example, Lee et al. (2002) found that early
warnings enhance the effectiveness of forward collision systems.
However, early warnings can also be annoying and non-useful given that
the drivers are more likely to avoid a dangerous situation on their own.
Such problems will also occur in distraction mitigation systems.
Therefore, there is a need to understand how the situation will evolve
based on driver's previous behavior as well as the traffic condition
surrounding the driver. This information will lead to predictive models
that will result in more accurate system response and a more trustworthy
system (Lee & See, 2004). Identifying triggers or states that directly
influence safety is essential to mitigate the effects of distractions.

# feedback at different timescales: Concurrent and post-Hoc Feedback

Donmez, Boyle, & Lee (Donmez, Boyle et al., 2006b) showed that
concurrent feedback can be beneficial in helping drivers modulate their
distracting activities. However, there is a possibility that concurrent
feedback may interfere with immediate task performance and may not be
completely effective in mitigating distraction. There has been research
showing such an effect in radar monitoring (Munro et al., 1985) and in
driving (Arroyo et al., 2006). Because of the limited processing time
and resources available during driving, concurrent feedback undermine
driving performance. The limited time that can be allocated to
concurrent feedback also makes it impossible to provide very much
information. As a consequence concurrent feedback may not be able to
convey the information necessary to guide behavior. For example, if
there is a dependency from one driving incident to another then
presenting this link via concurrent feedback may be too complex for the
driver to interpret while driving. However, presenting this information
is important since it can help the drivers assess their overall driving
performance by highlighting the persistent behavior that leads to
errors.

Moreover, near-accidents are generally forgotten very rapidly in the
absence of feedback. Chapman & Underwood (2000) found that an estimated
80% of near-accidents are forgotten after two weeks. This suggests that
driver behavior may be changed by refreshing drivers' memory of their
performance as well as calibrating their subjective performance (i.e.
how safe they think they drive). Such information is better suited for
post-hoc feedback. Post-hoc feedback has not been studied systematically
in the driving domain and merits further research. Because concurrent
feedback has also been shown to help distracted drivers, a system that
combines both concurrent and post-hoc feedback might have additional
benefits such as providing redundancy of information to help refresh
memory of incidents as well as helping the driver better understand
post-hoc feedback.

The main objective of this current experiment was to assess the effects
of post-hoc and combined concurrent and post-hoc feedback on driving
performance and engagement in distractions. This was assessed by
comparing these two feedback conditions to a baseline group where the
drivers performed the same driving and distracting tasks without
feedback. Specific questions investigated included whether providing
post-hoc feedback regarding drivers' performance helps them adopt safer
driving behavior and whether combined feedback provides additional
benefits. The effects of feedback were assessed with measures of
subjective and objective driving performance, as well as engagement in
distractions.

## Methods

The following sections describe the methodology used in the experiment.
This experiment used the same secondary task and the same concurrent
feedback algorithm and presentation as Donmez, Boyle, & Lee (2006b),
therefore, a detailed discussion on these topics can be found there.

### Participants

Forty-eight young participants, 23 females and 25 males, between the
ages of 18-21 (female: $\overline{X}$ = 20.2, *S* = 0.73; male:
$\overline{X}$= 20.3, *S* = 0.89) completed the study. The participants
possessed a valid U.S. driver's license, and had at least one year of
driving experience. They were native English speakers and had not driven
a driving simulator in the last two years. Participants were compensated
\$15/hour for their participation and had the opportunity to receive up
to \$10 extra based on their secondary task performance.

### Apparatus

The experiment was conducted with a medium fidelity, fixed-based
simulator powered by Global Sim, Inc.'s DriveSafety™ Research Simulator.
The simulator used a 1992 Mercury Sable vehicle cab equipped with a
force feedback steering wheel and had a 50-degree visual field. The
driving scenarios were created by HyperDrive™ Authoring Suite. A FaceLab
4.1™ eye tracker, which uses cameras as passive measuring devices, was
used to collect eye movement data. The eye tracker recognized in real
time whether the participant was looking at the road or at the
in-vehicle display. Eye-tracking and driving data were collected at 60
Hz. A 7-inch touch-screen LCD (60-Hz frame rate at 640 x 480 resolution)
mounted on the dashboard above the center instrument panel was used for
presentation of the visual messages for the secondary task as well as
feedback. The display was positioned 40 cm (15 in) to the right of the
center of the steering wheel and 8 cm (3 in) above the center of the
speedometer. With respect to the driver, the screen was turned
approximately 15˚ toward the driver and located 33˚ lateral to and 15˚
below the driver's line of sight.

### Driving Task 

Participants completed one practice drive in addition to four
experimental drives (each approximately seven minutes). The drives took
place on two-lane rural roads with oncoming traffic and curves (*three
400 meter radius and three 200 meter radius*). The participants were
instructed to drive at a comfortable speed that was not above the speed
limit of 73 km/h (\~ 45 mph) and follow a lead vehicle that periodically
braked at a rate of 0.2 g (gravitational acceleration) for five seconds.
Before a lead vehicle braking event, the lead vehicle speed was smoothly
adjusted to obtain constant time headways of 1.8 seconds. Eleven braking
events took place in a drive. A constant level of fog (sight distance:
300 m) was employed during the scenario to decrease the drivers' ability
to anticipate an approaching curve.

### Experimental design and independent variables

The experiment was a mixed factorial design with three between-subject
treatments: baseline (17 participants), post-hoc feedback (17
participants), and combined concurrent and post-hoc feedback (14
participants). Each subject completed four drives. This was done to
increase the amount of exposure to feedback. Drive was a within-subject
variable.

The participants in the baseline condition completed four experimental
drives while performing the in-vehicle task. The participants in the
post-hoc feedback condition also completed the same four drives while
performing the secondary task. After each drive, the post-hoc feedback
group received a trip-report. If there were no critical incidents during
a drive, positive feedback was provided (Figure 4. 20a). If there were
any incidents, a timeline showing incidents (red bars), appropriate
responses to lead vehicle braking (green bars), and distractions were
presented (Figure 4. 20b). The high and medium levels of distraction
were based on the algorithm referred to in the introduction. The
incident button brought up a new frame which presented the incident in
more detail (Figure 4. 20c). The incident type (Figure 4. 21), the
distraction level during the incident (low/none, medium, or high) and
the severity level of the incident (low/none, medium, or high) (Table 4.
9) were included in this detailed frame. The "overview" button took the
participant back to the trip-report overview for which the red bar of
the viewed incident was grayed out. Once the participants went through
all incidents, they were given the ability to start over. The combined
feedback (i.e. concurrent and post-hoc) group was presented with the
trip-report at the end of each drive and received concurrent feedback
during each drive.

[]{#__RefHeading___Toc160304999 .anchor}Table 4. 9. Incident severity
levels.

  ---------------------- ------------------- ---------- ---------- ---------
                                             Incident              
                                             Severity              

  Incident type          Variable of         Low/none   Medium     High
                         interest                                  

  Speeding               Speed               25-27 m/s  27-29 m/s  \>29 m/s

  Too close to lead      Time to collision   1.8-3 s    1-1.8 s    \<1 s
  vehicle                                                          

  Lane deviation         Duration of         \<1.5 s    1.5-4 s    \>4 s
                         deviation                                 

  Collision with lead    Crash (binary)      No         No         Yes
  vehicle                                                          

  Collision with         Crash (binary)      No         No         Yes
  oncoming traffic                                                 
  ---------------------- ------------------- ---------- ---------- ---------

![](media/image29.png){width="3.05in" height="2.15in"}

![](media/image31.png){width="3.0708333333333333in"
height="2.1458333333333335in"}

[]{#__RefHeading___Toc160355597 .anchor}Figure 4. 20. Trip-report (a)
positive feedback for no incidents (b) overview when there are incidents
(c) detailed information on the incident

![](media/image32.jpeg){width="1.8291666666666666in"
height="1.4958333333333333in"}![](media/image33.jpeg){width="1.6694444444444445in"
height="1.5041666666666667in"}![](media/image34.jpeg){width="1.71875in"
height="1.5006944444444446in"}![](media/image35.jpeg){width="1.7895833333333333in"
height="1.5013888888888889in"}![](media/image36.jpeg){width="1.6708333333333334in"
height="1.4986111111111111in"}

[]{#__RefHeading___Toc160355598 .anchor}Figure 4. 21. Incident
visualization: (a) speeding, (b) too close to lead vehicle, (c) lane
deviation, (d) collision with lead vehicle, (e) collision with oncoming
vehicle

### Procedure

After the participant signed the informed consent, the eye-tracker was
calibrated for the participant\'s facial characteristics. After
eye-tracker calibration the participants practiced the secondary task
until they became comfortable with the secondary task. This was followed
by a short acclimation drive (approximately 5 minutes long) in which the
participants became familiar with the driving simulator while performing
the secondary task. The participants then completed four drives (each
approximately 7 minutes long). After each drive the participants filled
out a series of questionnaires which asked them about the amount of
workload they experienced, their perceived risk, and their general
attitudes towards the safety systems. Participants were then debriefed
and appropriately compensated. Overall, the study took approximately two
hours.

### Dependent variables

The experiment assessed the differences in driving performance and
eye-movement patterns between different drives and compared these across
treatments. Lead vehicle braking response, interaction with in-vehicle
display (i.e. eye movements and button presses) and subjective responses
to questionnaires were analyzed.

For analysis purposes, the dependent variables for lead vehicle braking
event response were averaged over 11 braking events. This was done to
overcome any possible learning effect during a drive and obtain a more
accurate estimate of a general driver response. The dependent variables
analyzed for lead vehicle braking event response are accelerator release
time, brake reaction time, minimum time-to-collision (TTC) and minimum
acceleration. Starting from the lead vehicle braking, the time until the
driver releases the accelerator is the accelerator release time, and the
time until the driver starts braking is the brake reaction time. Minimum
TTC is the shortest time-to-collision during a braking event if the
driver were to continue in the same path at the same velocity. Minimum
acceleration is the minimum acceleration (or maximum deceleration) value
reached by the driver during the braking event. Both minimum TTC and
minimum acceleration are indicators of the safety outcome of a braking
event.

For eye movement behavior, three variables were analyzed that define
driver's scanning process: duration and number (per minute) of eye
glances to the in-vehicle display, and duration of glances on the road
in between glances on the in-vehicle display. A decrease in the first
two variables and an increase in the third one suggest a positive
modulation of the distracting activity.

After each drive, all participants were asked if their driving was safe
and if the distracting task worsened their driving performance.
Participants in feedback conditions were also asked if feedback enhanced
their driving performance. The responses were collected on a five point
Likert scale. Mental effort (Zijlstra, 1993), and perceived risk
(Tsimhoni, Smith, & Green, 2003) questionnaires were given to every
participant after each drive. Mental effort and perceived risk
questionnaires were on a scale of 0-150 and 1-10, respectively. A system
acceptance questionnaire (Van Der Laan et al., 1997) was also given to
participants in feedback conditions. This questionnaire composed of nine
questions along a scale of -2 to +2, investigating two dimensions of
acceptance: usefulness and satisfying.

## Results

The analysis on the continuous dependent variables was performed with
SAS 9.1 Mixed procedure. A compound symmetry covariance structure was
chosen for the repeated measure 'drive' based on Akaike information
criterion (Akaike, 1979). Two comparisons that were of greatest interest
were the difference between conditions on the last drive and the
difference between last and first drives. The difference on the last
drive shows the effect of feedback after the driver is exposed to it for
some amount of time. The difference between the last and first drive
assesses the cumulative effect of feedback. It should be noted that the
first drive is identical for task baseline and post-hoc feedback
conditions since post-hoc feedback is presented only after a drive is
completed. However, the first drive for combined feedback also includes
concurrent feedback which is presented during a drive.

### Response to lead vehicle braking 

Figure 4. 22 and Figure 4. 23 show the adjusted means for reaction times
and safety outcomes for lead vehicle braking events. The accelerator
release and brake reaction times suggest that both post-hoc and combined
feedback are beneficial to the driver. For accelerator release time the
main effect of drive (*F*(3,134)= 12.55, *p*\<.0001) and the interaction
of drive and condition (*F*(6,135)= 3.04, *p*= .008) were significant.
For the analysis of this variable inverse headway distance at the onset
of lead vehicle braking was used as a covariate (Donmez, Boyle, & Lee,
In press). Inverse headway distance also had a significant effect
(*F*(1,163)= 44.16, *p*\<.0001). Pair-wise comparisons between levels of
significant effects showed that accelerator release time was longer in
drive 1 compared to the following drives (Table 4. 10). This suggests
that there was a learning effect associated with the driving task. For
drive 4, both post-hoc and combined feedback resulted in shorter
accelerator release times than task baseline. Therefore, with
accumulated exposure, both feedback types resulted in faster response to
lead vehicle braking. Appropriate contrasts were used to compare the
change from drive 1 to drive 4 across different conditions. The
difference between drive 4 and drive 1 for task baseline was larger than
both combined and post-hoc feedback suggesting that both feedback types
resulted in a response enhancement over time.

[]{#__RefHeading___Toc160305000 .anchor}Table 4. 10. Significant
pair-wise comparisons for lead vehicle braking response.

  ----------------------------------------------------------------------------------
  Pair-wise comparison             Estimate   t       df     p         95%
                                                                       Confidence
                                                                       Interval
  -------------------------------- ---------- ------- ------ --------- -------------
  Accelerator release time:                                            

  drive 1 vs. drive 2              0.33 s     5.30    134    \<.0001   0.21, 0.45

  drive 1 vs. drive 3              0.32 s     5.19    135    \<.0001   0.20, 0.45

  drive 1 vs. drive 4              0.27 s     4.31    135    \<.0001   0.15, 0.39

  task baseline vs. post-hoc       0.34 s     2.02    86.6   .046      0.006, 0.67
  feedback on drive 4                                                  

  task baseline vs. combined       0.41 s     2.37    85     .02       0.07, 0.76
  feedback on drive 4                                                  

  task baseline vs. post-hoc       0.46 s     3.11    134    .002      0.17, 0.76
  feedback for the difference                                          
  between drive 4 and drive 1                                          

  task baseline vs. combined       0.61 s     3.95    134    .0001     0.30, 0.91
  feedback for the difference                                          
  between drive 4 and drive 1                                          

  Brake reaction time:                                                 

  drive 1 vs. drive 2              0.35 s     5.03    134    \<.0001   0.21, 0.49

  drive 1 vs. drive 3              0.38 s     5.38    135    \<.0001   0.24, 0.51

  drive 1 vs. drive 4              0.33 s     4.76    135    \<.0001   0.20, 0.47

  task baseline vs. post-hoc       0.44 s     2.03    72.8   .046      0.008, 0.87
  feedback on drive 4                                                  

  task baseline vs. combined       0.58 s     2.56    71.8   .01       0.13, 1.03
  feedback on drive 4                                                  

  task baseline vs. post-hoc       0.54 s     3.23    135    .0002     0.21, 0.87
  feedback for the difference                                          
  between drive 4 and drive 1                                          

  task baseline vs. combined       0.63 s     3.68    133    .0003     0.29, 0.97
  feedback for the difference                                          
  between drive 4 and drive 1                                          

  Minimum TTC:                                                         

  task baseline vs. post-hoc       -2.02 s    -2.52   134    .01       -3.61, -0.43
  feedback for the difference                                          
  between drive 4 and drive 1                                          

  task baseline vs. combined       -2.25 s    -2.69   134    .008      -3.91, -0.60
  feedback for the difference                                          
  between drive 4 and drive 1                                          

  Minimum acceleration:                                                

  task baseline vs. combined       0.63       2.68    134    .008      0.16, 1.09
  feedback for the difference      m/s^2^                              
  between drive 4 and drive 1                                          
  ----------------------------------------------------------------------------------

![](media/image37.wmf){width="5.008333333333334in"
height="3.0006944444444446in"}

![](media/image38.wmf){width="4.997222222222222in"
height="2.997916666666667in"}

[]{#__RefHeading___Toc160355599 .anchor}Figure 4. 22. Reaction to lead
vehicle braking events (estimated means and standard error bars) (a)
accelerator release time (b) brake reaction time

![](media/image39.wmf){width="4.990972222222222in"
height="2.998611111111111in"}

![](media/image40.wmf){width="4.990277777777778in"
height="3.002083333333333in"}

[]{#__RefHeading___Toc160355600 .anchor}Figure 4. 23. Safety outcomes
for lead vehicle braking event response (estimated means and standard
error bars) (a) minimum TTC (b) minimum acceleration

The results for brake reaction time were aligned with accelerator
release time. Both the main effect of drive (*F*(3,134)= 12.89,
*p*\<.0001), the interaction of drive and condition (*F*(6,134)= 3.2,
*p*= .006) and the covariate inverse headway distance (*F*(1,176)=
72.57, *p*\<.0001) were significant. Brake reaction time was longer in
drive 1 compared to the following drives. For drive 4, both post-hoc
feedback and combined feedback resulted in shorter brake reaction time
than task baseline. The difference between drive 4 and drive 1 for task
baseline was larger than both post-doc and combined feedback. For
minimum TTC, the difference between drive 4 and drive 1 for task
baseline was smaller than both combined and post-hoc feedback. This
suggests that both combined and post-hoc feedback resulted in a larger
enhancement in minimum TTC over time (Figure 4. 23b). For minimum
acceleration, the difference between drive 4 and drive 1 for task
baseline was larger than combined feedback. Task baseline appears to
result in more intense braking over time whereas combined feedback
results in a less intense braking response.

### Interaction with in-vehicle display: Eye movements and button presses

For the number of glances on the in-vehicle display, the difference
between drive 4 and drive 1 was larger for combined feedback compared to
both task baseline and post-hoc feedback (Figure 4. 24a). For combined
feedback, there seems to be an increasing trend in number of glances to
the in-vehicle display. For the duration of glances to the in-vehicle
display, the main effect of drive was significant (*F*(3,134)= 12.24,
*p*\<.0001) (Figure 4. 24b). Duration of glances on the in-vehicle
display was shorter in drive 1 and drive 2 compared to drive 3 and drive
4 (Figure 4. 24c), suggesting that the participants got more comfortable
performing the secondary task. The difference in glance duration to the
in-vehicle display between drive 4 and drive 1 for task baseline was
significantly longer than those of combined and post-hoc feedback. It
appears that the increase in glance duration over time is the largest
for task baseline.

![](media/image41.wmf){width="3.9972222222222222in"
height="2.3986111111111112in"}

![](media/image42.wmf){width="3.997916666666667in"
height="2.3986111111111112in"}![](media/image43.wmf){width="4.0in"
height="2.4in"}

[]{#__RefHeading___Toc160355601 .anchor}Figure 4. 24. Eye-movements
(estimated means and standard error bars) (a) number of glances per
minute on in-vehicle display (b) glance duration on in-vehicle display
(c) glance duration on the road

The two main effects drive (*F*(3,134)= 3.75, *p*= .01) and condition
(*F*(2,45.1)= 3.83, *p*= .03) were significant for glance duration on
the road (Figure 4. 24c). Duration of glances on the road was longer in
drive 1 and drive 2 compared to drive 4. Therefore, in line with the
increase in glance duration on the in-vehicle display, there was a
decrease in glance duration on the road as participants became more
comfortable performing the secondary task. In general, combined feedback
resulted in longer glances on the road compared to task baseline and
post-hoc feedback. Therefore, combined feedback had an overall positive
impact on drivers' engagement in the distracting activity. The
difference between drive 4 and drive 1 was smaller (larger in absolute
value) for combined feedback compared to both task baseline and post-hoc
feedback. Number of button presses per minute had a significant main
effect of drive (*F*(3,134)= 44.33, *p*\<.0001) (Figure 4. 25). Drive 4
resulted in the largest number of button presses followed by drive 3,
providing more support for the increase in drivers' comfort level in
performing the secondary task (Table 4. 11).

[]{#__RefHeading___Toc160305001 .anchor}Table 4. 11. Significant
pair-wise comparisons for interaction with in-vehicle display.

  -----------------------------------------------------------------------------------
  Pair-wise comparison             Estimate   t        df     p         95%
                                                                        Confidence
                                                                        Interval
  -------------------------------- ---------- -------- ------ --------- -------------
  Number of glances per minute on                                       
  in-vehicle display:                                                   

  post-hoc feedback vs. combined   -2.56      -2.23    134    .03       -4.84, -0.29
  feedback for the difference                                           
  between drive 4 and drive 1                                           

  task baseline vs. combined       -2.80      2.45     134    .02       -5.06, -0.54
  feedback for the difference                                           
  between drive 4 and drive 1                                           

  Glance duration on in-vehicle                                         
  display:                                                              

  drive 1 vs. drive 3              -0.08 s    -3.41    134    .0009     -0.13, -0.04

  drive 2 vs. drive 3              -0.12 s    -4.65    134    \<.0001   -0.16, -0.07

  drive 1 vs. drive 4              -0.09 s    -3.73    134    .0003     -0.14, -0.04

  drive 2 vs. drive 4              -0.12 s    -4.97    134    \<.0001   -0.17, -0.07

  task baseline vs. post-hoc       0.12 s     2.00     134    .047      0.001, 0.08
  feedback for the difference                                           
  between drive 4 and drive 1                                           

  task baseline vs. combined       0.16 s     2.55     134    .02       0.04, 0.28
  feedback for the difference                                           
  between drive 4 and drive 1                                           

  Glance duration on road:                                              

  drive 1 vs. drive 4              0.12 s     2.36     134    .02       0.02, 0.23

  drive 2 vs. drive 4              0.17 s     3.15     134    .002      0.06, 0.27

  task baseline vs. combined       -0.3 s     -2.63    45     .01       -0.54, -0.07
  feedback                                                              

  post-hoc feedback vs. combined   -0.25 s    -2.17    45.1   .04       -0.48, -0.02
  feedback                                                              

  post-hoc feedback vs. combined   0.34 s     2.59     134    .01       0.08, 0.60
  feedback for the difference                                           
  between drive 4 and drive 1                                           

  task baseline vs. combined       0.33 s     2.50     134    .01       0.07, 0.58
  feedback for the difference                                           
  between drive 4 and drive 1                                           

  Number of button presses per                                          
  minute:                                                               

  drive 1 vs. drive 4              -2.46      -10.07   134    \<.0001   -2.95, -1.98

  drive 2 vs. drive 4              -2.37      -9.66    134    \<.0001   -2.85, -1.88

  drive 3 vs. drive 4              -1.25      -5.11    134    \<.0001   -1.74, -0.77

  drive 1 vs. drive 3              -1.21      -4.99    134    \<.0001   -1.70, -0.73

  drive 2 vs. drive 3              -1.11      -4.58    134    \<.0001   -1.59, -0.63
  -----------------------------------------------------------------------------------

![](media/image44.wmf){width="4.991666666666666in"
height="2.995138888888889in"}

[]{#__RefHeading___Toc160355602 .anchor}Figure 4. 25. Number of button
presses per minute (estimated means and standard error bars)

### Subjective measures

Both condition (*F*(2, 45)= 10.76, *p*= .0002) and drive (*F*(3,135)=
7.46, *p*= .0001) had significant effect on perceived level of risk
(Figure 4. 26a). In general, drivers in the task baseline condition
perceived greater risk when compared to post-hoc and combined feedback
(Table 4. 12.) This suggests that the drivers were aware of their
performance decrement with the secondary task (i.e. late accelerator
release when compared to feedback conditions). Drivers' perceived risk
level dropped as the number of drives increased. Mental effort was
highest for drive 1, and drive 2 resulted higher mental effort than the
last drive (Figure 4. 26b). These results can be attributed to increased
driver familiarity with the secondary and the driving tasks.

![](media/image45.wmf){width="4.991666666666666in"
height="2.995138888888889in"}![](media/image46.wmf){width="4.990972222222222in"
height="2.998611111111111in"}

[]{#__RefHeading___Toc160355603 .anchor}Figure 4. 26. Subjective
measures (estimated means and standard error bars) (a) perceived risk
(b) mental effort

[]{#__RefHeading___Toc160305002 .anchor}Table 4. 12. Significant
pair-wise comparisons for subjective measures.

  -------------------------------- ---------- ------- ------ --------- -------------
  Pair-wise comparison             Estimate   t       df     p         95%
                                                                       Confidence
                                                                       Interval

  Perceived risk:                                                      

  task baseline vs. post-hoc       1.96       4.02    45     .0002     0.99, 3.05
  feedback                                                             

  task baseline vs. combined       2.02       3.94    45     .0003     0.98, 2.94
  feedback                                                             

  drive 1 vs. drive 2              0.45       2.01    135    .046      0.01, 0.89

  drive 1 vs. drive 3              0.79       3.52    135    .0006     0.4, 1.23

  drive 1 vs. drive 4              0.97       4.41    135    \<.0001   0.54, 1.43

  drive 2 vs. drive 4              0.54       2.40    135    .02       0.09, 0.98

  Mental effort:                                                       

  drive 1 vs. drive 2              4.55       2.12    135    .04       0.31, 8.80

  drive 1 vs. drive 3              7.15       3.33    135    .001      2.91, 11.39

  drive 1 vs. drive 4              9.55       4.45    135    \<.0001   5.31, 13.97

  drive 2 vs. drive 4              5.00       2.33    135    .02       0.75, 9.24

  Usefulness:                                                          

  drive 1 vs. drive 3              -0.17      -2.33   86.1   .02       -0.32, -0.03

  drive 1 vs. drive 4              -0.21      -2.86   86.1   .005      -0.36, -0.06

  Satisfaction:                                                        

  drive 1 vs. drive 2              -0.29      -2.42   87     .02       -0.53, -0.05

  drive 1 vs. drive 3              -0.38      -3.18   87     .002      -0.62, -0.14

  drive 1 vs. drive 4              -0.47      -3.91   87     .0002     -0.72, -0.23
  -------------------------------- ---------- ------- ------ --------- -------------

The 95% confidence intervals for the mean acceptance scores, which
exclude zero, reveal that drivers generally found both feedback types to
be useful (Figure 4. 27). The trip-report was also found to be
satisfying by both the drivers in post-hoc and combined feedback
conditions. The satisfaction response for concurrent feedback (presented
as part of combined feedback) is not significantly different than zero
since the 95% confidence interval for the mean includes zero. Still,
there is a general positive attitude towards both feedback types.

Driver acceptance of the trip-report was compared across the two
conditions which included the trip-report (i.e. post-hoc and combined
feedback). Acceptance of the trip-report as part of post-hoc and
combined feedback were not significantly different (*p*\>.05). However,
first drive resulted in a lower level of acceptance when compared to
following drives. This suggests that drivers' acceptance level of the
trip-report increased with exposure. The acceptance data for the
concurrent feedback (presented as part of combined feedback) could not
be included in this analysis because only the combined feedback group
received this type of feedback and comparison with another condition was
not feasible.

![](media/image47.wmf){width="3.75in" height="3.6041666666666665in"}

[]{#__RefHeading___Toc160355604 .anchor}Figure 4. 27. Acceptance of
feedback (estimated means and 95% confidence intervals).

The descriptive statistics on participants' subjective driving
performance revealed that the participants considered the secondary task
as worsening their driving (Table 4. 13). Most of the participants also
thought that both post-hoc and concurrent feedback enhanced their
driving performance.

[]{#__RefHeading___Toc160305003 .anchor}Table 4. 13. Subjective
responses relating to driving performance.

  ----------------- --------------- ---------- --------- ------- -------------
  Condition         Response                                     

                    Strongly        Disagree   Neutral   Agree   Strongly
                    disagree                                     agree

  My driving was                                                 
  safe                                                           

  Task Baseline     9               32         11        16      0

  Post-hoc Feedback 1               6          22        25      14

  Combined Feedback 0               10         12        33      1

  Distracting task                                               
  worsened my                                                    
  driving                                                        

  Task Baseline     0               0          1         38      29

  Post-hoc Feedback 0               7          6         41      14

  Combined Feedback 0               1          7         36      12

  Performance of                                                 
  trip-report                                                    
  enhanced my                                                    
  driving                                                        

  Post-hoc Feedback 2               11         19        33      3

  Combined Feedback 0               4          18        34      0

  Performance of                                                 
  concurrent                                                     
  feedback enhanced                                              
  my driving                                                     

  Combined Feedback 2               6          16        31      0
  ----------------- --------------- ---------- --------- ------- -------------

## Discussion

This study assessed the effects of post-hoc and combined concurrent and
post-hoc feedback on driving performance and driver engagement in
distracting activities. The results showed that driving performance
improved from first to last drive, suggesting a learning effect.
Moreover, as the number of drives increased drivers became more
comfortable with performing the secondary task. As assessed by the
difference between last and first drive, driving performance improved
more for the post-hoc and combined feedback compared to task baseline.
For the last drive, after drivers had several exposures to feedback,
both feedback types resulted in significantly faster reaction to lead
vehicle braking events. In terms of driving performance measures, no
significant differences were found between the two feedback types. In
general, both post-hoc and combined feedback enhanced driving
performance.

As drivers completed more drives, their glance duration to the
in-vehicle display increased and their glance duration on the road
decreased. This suggests that drivers became more comfortable performing
the task. However, task baseline had a larger increase in glance
duration from first to last drive when compared to both post-hoc and
combined feedback. This suggests that both of these feedback types can
induce a positive behavior in terms of how long the drivers look at the
in-vehicle display. Moreover, combined feedback resulted in longer
on-road glances. Even if there were no significant results for specific
drives across conditions, there seems to be a decline in the benefits of
combined feedback over time with regard to driver engagement in
distracting activities. The long term effects of these feedback types
merit further research.

Subjective driver responses were in line with objective data. Drivers in
the secondary task condition perceived more risk and also had worse
driving performance compared to feedback conditions. Most of the
participants considered the secondary task as worsening their driving
performance, and both feedback types as enhancing their driving
performance. Drivers also accepted both post-hoc and combined feedback.
Trip-report, which is included in both feedback conditions, was found to
be useful and satisfactory. Concurrent feedback, which is only a part of
combined feedback, was perceived to be useful. Acceptance of these
feedback types is important as acceptance can play a role in the use of
a system and hence in its effectiveness.

# References

Akaike, H. (1979). A Bayesian extension of the minimum AIC procedure of
autoregressive model fitting. *Biometrika, 66*(2), 237-242.

Annett, J. (1959). Learning a pressure under conditions of immediate and
delayed knowledge of results. *Quarterly Journal of Experimental
Psychology, 11*, 3-15.

Annett, J. (1969). *Feedback and human behavior*. Baltimore: Penguin.

Arnett, J. J. (2002). Developmental sources of crash risk in young
drivers. *Injury Prevention,, 8*(Suppl II), ii17-ii23.

Arroyo, E., Sullivan, S., & Selker, T. (2006). CarCoach: a polite and
effective driving coach. In *Proceedings of the CHI: Conference on Human
Factors in Computing Systems* (pp. 357-362).

Ashby, F. G., Alfonso-Reese, L. A., Turken, A. U., & Waldron, E. M.
(1998). A neuropsychological theory of multiple systems in category
learning. *Psychological Review, 105*, 442-481.

Ashby, F. G., & Gott, R. E. (1988). Decision rules in the perception and
categorization of multidimensional stimuli. *Journal of Experimental
Psychology: Learning, Memory, and Cognition, 14*, 33-53.

Baddeley, A., Logie, R., & Nimmo-Smith, I. (1985). Components of fluent
reading, *Journal of Memory and Language* (Vol. 24, pp. 119-131).

Bangert-Drowns, R. L., Kulik, C. C., Kulik, J. A., & Morgan, M. T.
(1991). The instructional feedback in test-like events. *Review of
Educational Research, 61.*, 213-238.

Berglund, B., Harder, K., & Preis, A. (1994). Annoyance perception of
sound and information extraction. *Journal of the Acousticcal Society of
America, 95*, 1501-1509.

Blackbill, Y., Blobitt, W. E., Davlin, D., & Wagner, J. E. (1963).
Amplitude of response and the delay-retention effect. *Journal of
Experimental Psychology, 66*(1), 57-64.

Bogard, S., Fancher, P., Ervin, R., Hagan, M., & Bareket, Z. (1998).
Intelligent Cruise Control Field Operational Test: National Highway
Traffic Safety Administration.

Branderburg, D. L., & Mirka, G. A. (2005). Assessing the effects of
positive feedback and reinforcement in the introduction phase of an
ergonomic intervention. *Human Factors, 47*(3), 526-535.

Brickfield, C. F. (1984). Attitudes and perceptions of older people
toward technology. In J. E. Birren (Ed.), *Aging and technological
advances* (pp. 31-38). New York: Plenum Press.

Brookhuis, K. A., De Waard, D., & Fairclough, S. H. (2003). Criteria for
driver impairment. *Ergonomics, 46*(5), 433-445.

Brown, I. D., & Groeger, J. A. (1988). Risk perception and decision
taking during the transition between novice and experienced driver
status. *Ergonomics, 31*(4), 585-597.

Burns, P. C., & Lansdown, T. C. (2000). *E-distraction: the challenges
for safe and usable internet services in vehicles*. Retrieved July, 2004

Chapman, P., & Underwood, G. (2000). Forgetting near-accidents: The
roles of severity, culpability and experience in the poor recall of
dangerous driving situations. *Applied Cognitive Psychology, 14*, 31-44.

Chi, M. T. H., Siler, S. A., Jeong, H., Yamauchi, T., & Hausmann, R. G.
(2001). Learning from human tutoring. *Cognitive Science, 25*, 471-533.

Clariana, R. B., Wagner, D., & Roher-Murphy, L. C. (2000). Applying a
connectionist description of feedback timing. *Educational Technology
Research and Development, 48*(3), 5-21.

Cooper, P. J. (1990). Differences in accident characteristics among
elderly drivers and between elderly and middle-aged drivers, *Accident
Analysis & Prevention* (Vol. 22, pp. 499-508).

Cooper, P. J. (1990). Differences in accident characteristics among
elderly drivers and between elderly and middle-aged drivers. *Accident
Analysis & Prevention, 22*(5), 499-508.

Cooper, P. J., Pinili, M., & Chen, W. J. (1995). An Examination of the
Crash Involvement Rates of Novice Drivers Aged 16 to 55. *Accident
Analysis and Prevention, 27*(1), 89-104.

Cooper, P. J., & Zheng, Y. (2002). Turning gap acceptance
decision-making: the impact of driver distraction, *Journal of Safety
Research* (Vol. 33, pp. 321-335).

Cooper, P. J., Zheng, Y., Richard, C., Vavrik, J., Heinrichs, B., &
Siegmund, G. (2003). The impact of hands-free message reception/response
on driving task performance, *Accident Analysis & Prevention* (Vol. 35,
pp. 23-35).

Corbett, A. T., & Anderson, J. R. (2001). Locus of feedback control in
computer-based tutoring: Impact on learning rate, achievement, and
attitudes. In *CHI Conference on Human Factors in Computing Systems*.
Seattle, WA.

Deering, R. K., & Viano, D. C. (1998). Critical success factors for
crash avoidance countermeasure implementation. In R. K. Jurgen (Ed.),
*Object Detection, Collision Warning and Avoidance Systems* (pp.
173-178). Warrendale, PA: Society of Automotive Engineers, Inc.

Deery, H. A. (1999). Hazard and risk perception among young novice
drivers. *Journal of Safety Research, 30*(4), 225-236.

Deery, H. A., & Fildes, B. N. (1999). Young novice driver subtypes:
Relationship to high-risk behavior, traffic accident record, and
simulator driving performance. *Human Factors, 41*(4), 628-643.

Donmez, B., Boyle, L., & Lee, J. D. (2003). Taxonomy of mitigation
strategies for driver distraction. In *Human Factors and Ergonomics
Society 47th Annual Meeting* (pp. 1865-1869). Santa Monica, CA: Human
Factors and Ergonomics Society.

Donmez, B., Boyle, L., & Lee, J. D. (In press). Accounting for the
covariate effects in driving simulator studies. *Theoretical Issues in
Ergonomics Science*.

Donmez, B., Boyle, L. N., & Lee, J. D. (2006a). Drivers\' attitudes
toward imperfect distraction mitigation strategies. *Transportation
Research Part F: Traffic Psychology, 9*(6), 387-398.

Donmez, B., Boyle, L. N., & Lee, J. D. (2006b). The impact of driver
distraction mitigation strategies on driving performance. *Human
Factors, 48*(4), 785-804.

Donmez, B., Boyle, L. N., Lee, J. D., & Scott, G. (2006). Safety
implications of providing real-time feedback to distracted drivers.
*Accident Analysis and Prevention*.

Evans, L. (1991). *Traffic Safety and the Driver*. New York: Van
Nostrand Reinhold.

Fiesler, C. L., McLaughlin, A. C., Fisk, A. D., & Rogers, W. A. (2003).
Conceptual and procedural feedback in training of a home medical device.
In *Proceedings of the Human Factors and Ergonomics Society 47th Annual
Meeting* (Vol. 2, pp. 1810-1814). Santa Monica, CA: Human Factors and
Ergonomics Society.

Fisher, D. L., Laurie, N. E., Glaser, R., Connerney, K., Pollatsek, A.,
Duffy, S. A., et al. (2002). Use of a fixed-base driving simulator to
evaluate the effects of experience and PC-based risk awareness training
on drivers\' decisions. *Human Factors, 44*(2), 287-302.

Fogg, B. J., & Nass, C. (1997). Silicon sycophants: the effects of
computers that flatter. *International Journal of Human-Computer
Studies, 46*(4), 551-561.

Fox, J. E., & Boehm-Davis, D. (1998a). Effects of age and congestion
information accuracy of advanced traveler information systems on user
trust and compliance, *Transportation Research Record 1621 (Safety and
Human Performance)* (pp. 43-49).

Fox, J. E., & Boehm-Davis, D. (1998b). Effects of age and congestion
information accuracy of advanced traveler information systems on user
trust and compliance. *Transportation Research Record 1621 (Safety and
Human Performance)*, 43-49.

Gibson, J. J., & Crooks, L. E. (1938). A theoretical field-analysis of
automobile driving. *American Journal of Psychology, 51*, 453-471.

Glover, J. A. (1989). The \"testing\" phenomenon: Not gone but nearly
forgotten. *Journal of Educational Psychology, 81*(3), 392-399.

Graesser, A. C., Person, N. K., & Magliano, J. P. (1995). Collaborative
dialogue patterns in naturalistic one-to-one tutoring. *Applied
Cognitive Psychology, 9*, 495-522.

Gregersen, N. P. (1996). Young drivers\' overestimation of their own
skill - An experiment on the relation between training strategy and
skill. *Accident Analysis and Prevention, 28*(2), 243-250.

Haigney, D., & Westerman, S. J. (2001). Mobile (cellular) phone use and
driving: a critical review of research methodology. *Ergonomics, 44*(2),
132-143.

Hirst, S., & Graham, R. (1997). The format and presentation of collision
warnings. In I. Noy (Ed.), *Ergonomics and Safety of Intelligent Driver
Interfaces* (pp. 203-219). Mahwah, NJ: Erlbaum.

Hoffman, J. D., Lee, J. D., McGehee, D. V., & Gellatly, A. W. (2005).
Visual sampling of in-vehicle text messages: The effects of number of
lines, page presentation and message control. *Transportation Research
Record, 1937*, 22-31.

Horrey, W. J., & Wickens, C. D. (2006). Examining the impact of cell
phone conversations on driving using meta-analytic techniques. *Human
Factors, 48*(1), 196-205.

Ji, Q., Zhu, Z., & Lan, P. (2004). Real-time nonintrusive monitoring and
prediction of driver fatigue. *IEEE Transactions on Vehicle Technology,
53*(4), 1052-1068.

Jonah, B. A. (1997). Sensation seeking and risky driving: A review and
synthesis of the literature. *Accident Analysis and Prevention, 29*(5),
651-665.

Jonah, B. A., Thiessen, R., & Au-Yeung, E. (2001). Sensation seeking,
risky driving and behavioral adaptation. *Accident Analysis and
Prevention, 33*(5), 679-684.

Kantowitz, B. H., Lee, J. D., Becker, C. A., Bittner, A. C., Kantowitz,
S. C., Hanowski, R. J., et al. (1997). *Development of human factors
guidelines for advanced traveler information systems and commercial
vehicles: Identify and explore driver acceptance of in-vehicle ITS
information.* (No. FHWA-RD-96-143). Washington, DC: Federal Highway
Administration.

Karlin, L., & Mortimer, R. G. (1963). Effect of verbal, visual, and
auditory augmenting cues on learning a complex motor skill. *Journal of
Experimental Psychology, 65*, 75-79.

Knipling, R. R., Mironer, M., Hendricks, D. L., Tijerina, L., Everson,
J., Allen, J. C., et al. (1993). *Assessment of IVHS countermeasures for
collision avoidance: rear-end crashes* (No. DOT HS 807 995). Washington,
DC: National Highway Traffic Safety Administration.

Kray, J., & Lindenberger, U. (2000). Adult age differences in task
switching. *Psychology and Aging, 15*(1), 126-147.

Kulhavy, R. W. (1977). Feedback in written instruction. *Review of
Educational Research, 47*, 211-232.

Kulhavy, R. W., & Anderson, J. R. (1972). Delay-retention effect with
multiple-choice tests. *Journal of Educational Psychology, 63*(5),
505-512.

Kulik, J. A., & Kulik, C. C. (1988). Timing of feedback and verbal
learning. *Review of Educational Research, 58*(1), 79-97.

Laapotti, S., Keskinen, E., Hatakka, M., & Katila, A. (2001). Novice
drivers\' accidents and violations - a failure on higher or lower
hierarchical levels of driving behaviour. *Accident Analysis and
Prevention, 33*(6), 759-769.

Lajunen, T., Hakkarainen, P., & Summala, H. (1996). The ergonomics of
road signs: Explicit and embedded speed limits. *Ergonomics, 39*(8),
1069-1083.

Lamble, D., Kauranen, T., Laakso, M., & Summala, H. (1999). Cognitive
load and detection thresholds in car following situations: safety
implications for using mobile (cellular) telephones while driving.
*Accident Analysis and Prevention, 31*(6), 617-623.

Lee, J. D., Caven, B., Haake, S., & Brown, T. L. (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. L. (2002).
Collision warning timing, driver distraction, and driver response to
imminent rear-end collisions in a high-fidelity driving simulator.
*Human Factors, 44*(2), 314-334.

Lee, J. D., & See, K. A. (2004). Trust in technology: Designing for
appropriate reliance. *Human Factors, 46*(1), 50-80.

Lee, J. D., & Strayer, D. L. (2004). Preface to a special section on
driver distraction. *Human Factors, 46*, 583-586.

Lees, M. N., & Lee, J. D. (in press). The influence of distraction and
driving context on driver response to imperfect collision warning
systems. *Ergonomics*.

Lepper, M. R., Aspinwall, L. G., Mumme, D. L., & Chabay, R. W. (1990).
Self-perception and social-perception processes in tutoring: subtle
social control strategies of expert tutors. In J. M. Olson & M. P. Zanna
(Eds.), *Self-inference Process: The Ontario Symposium* (pp. 217-237).
Hillsdale, NJ: Lawrence Erlbaum Associates.

Lerner, N. (2005). *Deciding to be distracted.* Paper presented at the
Driving Assessment Conference, Maine.

Lin, M. L., & Fearn, K. T. (2003). The provisional license: nighttime
and passenger restrictions - a literature review. *Journal of Safety
Research, 34*(1), 51-61.

Littell, R. C., Henry, P. R., & Ammerman, C. B. (1998). Statistical
analysis of repeated measures data using SAS procedures. *Journal of
Animal Science, 76*, 1216-1231.

Maddox, W. T., Ashby, F. G., & Bohil, C. J. (2003). Delayed feedback
effects on rule-based and information-integration category learning.
*Journal of Experimental Psychology: Learning, Memory, and Cognition,
29*(4), 650-662.

Mayhew, D. R., Simpson, H. M., & Pak, A. (2003). Changes in collision
rates among novice drivers during the first months of driving. *Accident
Analysis & Prevention, 35*, 683-691.

McCartt, A. T. (2001). Graduated driver licensing systems - Reducing
crashes among teenage drivers. *Journal of the American Medical
Association, 286*(13), 1631-1632.

McGehee, D. V., Carney, C., Raby, M., Reyes, M. L., & Lee, J. D. (in
press). The impact of an event-triggered video intervention on rural
teenage driving: The first six months. *Driving Assessment*.

McGwin, G., & Brown, D. B. (1999). Characteristics of traffic crashes
among young, middle-aged, and older drivers. *Accident Analysis &
Prevention, 31*(181-198).

McKnight, A. J., & McKnight, A. S. (2003). Young novice drivers:
careless or clueless? *Accident Analysis and Prevention, 35*(6),
921-925.

McKnight, A. J., & Peck, R. C. (2003). Graduated driver licensing and
safer driving. *Journal of Safety Research, 34*(1), 85-89.

Mead, S. E., & Fisk, A. D. (1998). Measuring skill acquisition and
retention with an ATM simulator: the need for age-specific training.
*Human Factors, 40*(3), 516-523.

Merrill, D. C., Reiser, B. J., Ranney, M., & Trafton, J. G. (1992).
Effective tutoring techniques: A comparison of human tutors and
intelligent tutoring systems. *The Journal of Learning Sciences, 2*(3),
277-305.

Minderhoud, M. M., & Bovy, P. H. L. (2001). Extended time-to-collision
measures for road traffic safety assessment. *Accident Analysis &
Prevention, 33*, 89-97.

Monk, C. A., Boehm-Davis, D. A., & Trafton, J. G. (2004). Recovering
from interruptions: Implications for driver distraction research. *Human
Factors, 46*(4), 650-663.

Mourant, R. R., & Rockwell, T. H. (1972). Strategies of visual search by
novice and experienced drivers. *Human Factors, 14*(4), 325-335.

Munro, A., Fehling, M. R., & Towne, D. M. (1985). Instruction
intrusiveness in dynamic simulation training. *Journal of Computer-Based
Instruction, 12*, 50-53.

Nakayama, O., Futami, T., Nakamura, T., & Boer, E. R. (1999). SAE
Technical Paper Series: Development of a steering entropy method for
evaluating driver workload. *Human Factors in Audio Interior Systems,
Driving, and Vehicle Seating, SP-1426*.

Olsen, E. C. B., Lerner, N., Perel, M., & Simons-Morton, B. G. (2005).
In-car electronic device use amoung teen drivers. *Transportation
Research Board Annual Meeting*.

Parasuraman, R., & Hancock, P. A. (2001). (Adaptive control of mental
workload. In P. A. Hancock & P. A. Desmond (Eds.), *Stress, Workload,
and Fatigue* (pp. 305-320). Mahwah, New Jersey: Lawrence Erlbaum
Associates.

Parasuraman, R., Hancock, P. A., & Olofinboba, O. (1997). Alarm
effectiveness in driver-centred collision-warning systems. *Ergonomics,
40*(3), 390-399.

Parasuraman, R., & Riley, V. (1997). Humans and automation: Use, misuse,
disuse, abuse. *Human Factors, 39*(2), 230-253.

Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for
types and levels of human interaction with automation. *IEEE
Transactions on Systems Man and Cybernetics -Part A: Systems and Humans,
30*(3), 286-297.

Parker, D., Manstead, A. S. R., Stradling, S. G., & Reason, J. T.
(1992). Determinants of Intention to Commit Driving Violations.
*Accident Analysis and Prevention, 24*(2), 117-131.

Piechulla, W., Mayser, C., Gerkhe, H., & Konig, W. (2003). Reducing
drivers\' mental workload by means of an adaptive man-machine interface.
*Transportation Research Part F, 6*, 233-248.

Poysti, L., Rajalin, S., & Summala, H. (2005). Factors influencing the
use of cellular (mobile) phone during driving and hazards while using
it. *Accident Analysis and Prevention, 37*(1), 47-51.

Radeborg, K., Briem, V., & Hedman, L. R. (1999). The effect of
concurrent task difficulty on working memory during simulated driving,
*Ergonomics* (Vol. 42, pp. 767-777).

Rajalin, S., & Summala, H. (1997). What surviving drivers learn from a
fatal road accident. *Accident Analysis and Prevention, 29*(3), 277-283.

Ranney, T., Mazzae, E., Garrott, R., & Goodman, M. (2000). NHTSA Driver
Distraction Research: Past, Present, and Future.: NHTSA.

Reeves, B., & Nass, C. (1996). *The Media Equation: How people treat
computers, television, and new media like real people and places*. New
York: Cambridge University Press.

Rimmo, P. A. (2002). Aberrant driving behaviour: homogeneity of a
four-factor structure in samples differing in age and gender.
*Ergonomics, 45*(8), 569-582.

Rivers, K., Sarvela, P. D., Shannon, D. V., & Gast, J. (1996). Youth and
young adult perceptions of drinking and driving prevention programs: A
focus group study, *Journal of Alcohol and Drug Education* (Vol. 41, pp.
80-91).

Rogers, W. A., Meyer, B., Walker, N., & Fisk, A. D. (1998). Functional
limitations to daily living tasks in the aged: A focus group analysis,
*Human Factors* (Vol. 40, pp. 111-125).

Rouse, W. B. (1994). Twenty years of adaptive aiding: Origins of the
concepts and lessons learned. In M. Mouloula & R. Parasuraman (Eds.),
*Human performance in automated systems: Current research and trends*
(pp. 28-33). Hillsdale, NJ: Erlbaum.

Sanders, M. (2005). *The effect of immediate feedback and after-action
reviews (AARS) on learning, retention and transfer, Master\'s thesis*.
Orlando, Florida: University of Central Florida.

Schmidt, R. A., & Wulf, G. (1997). Continuous concurrent feedback
degrades skill learning: implications for training and simulation.
*Human Factors, 39*(4), 509-525.

Sharon, T., Selker, T., Wagner, L., & Frank, A. J. (2005). CarCoach: a
generalized layered architecture for educational car systems. In
*Proceedings of IEEE International Conference on Software - Science,
Technology and Engineering* (pp. 13-22).

Sheridan, T. B. (2002). *Humans and Automation*. New York: John Wiley.

Sheridan, T. B. (2004). Driver distraction from a control theoretic
perspective. *Human Factors, 46*(4), 587-599.

Shope, J. T., & Molnar, L. J. (2003). Graduated driver licensing in the
United States: evaluation results from the early programs. *Journal of
Safety Research, 34*(1), 63-69.

Sodhi, M., Reimer, B., & Llamazares, I. (2002). Glance analysis of
driver eye movements to evaluate distraction. *Behavior Research Methods
Instruments & Computers, 34*(4), 529-538.

Sorkin, R. D., Kantowitz, B. H., & Kantowitz, S. C. (1988). Likelihood
alarm displays. *Human Factors, 30*, 445-459.

Stearns, M., Najm, W., & Boyle, L. (2002, January 15). A methodology to
evaluate driver acceptance. *Transportation Research Board, 81st Annual
TRB Meeting*.

Stewart, D. W., & Shamdasani, P. N. (1990). Focus groups: Theory and
practice, *Applied social research methods series*. London: Sage.

Stutts, J., & Hunter, W. (2003). Driver inattention, driver distraction
and traffic crashes. *ITE Journal, 73*(7), 34-36, 43-45.

Suzuki, K., & Jansson, H. (2003). An analysis of driver\'s steering
behaviour during auditory or haptic warnings for the designing of lane
departure warning system. *JSAE Review, 24*, 65-70.

Tomer, T., & Lotan, T. (2006). In-vehicle data recorder for evaluation
of driving behavior and safety. *Transportation Research Record, 1953*,
112-119.

Tsimhoni, O., Smith, D., & Green, P. (2003). *On-the-road assessment of
driving workload and risk to support the development of an information
manager* (No. technical Report UMTRI-2003-08). Ann Arbor, MI: The
University of Michigan Transportation Institute.

Underwood, G., Chapman, P., Brocklehurst, N., Underwood, J., & Crundall,
D. (2003). Visual attention while driving: sequences of eye fixations
made by experienced and novice drivers. *Ergonomics, 46*(6), 629-646.

Vakil, S. S., & Hansman, R. J. (2002). Approaches to mitigating
complexity-driven issues in commercial autoflight systems. *Reliability
Engineering and System Safety, 75*, 133-145.

Van Der Laan, J., Heino, A., & De Waard, D. (1997). A simple procedure
for the assessment of acceptance of advanced transport telematics.
*Transportation Research Part C-Emerging Technologies, 5*(1), 1-10.

Verhaeghen, P., Steitz, D. W., Sliwinski, M. J., & Cerella, J. (2003).
Aging and dual-task performance: A meta-analysis. *Psychology and Aging,
18*(3), 443-460.

Verwey, W. B., Brookhuis, K. A., & Janssen., W. H. (1996.). *Safety
effects of in-vehicle information systems* (No. TM-96-C002).
Soesterberg, The Netherlands: TNO Human Factors Research Institute.

Vogel, K. (2003). A comparison of headway and time to collision as
safety indicators. *Accident Analysis & Prevention*(35), 427-433.

Wang, J., Knipling, R. R., & Goodman, M. J. (1996). The role of driver
inattention in crashes; new statistics from the 1995 crashworthiness
data system (CDS). *40th Annual Proceedings: Association for the
Advancement of Automotive Medicine*, 377-392.

Wickens, C. D., & Hollands, J. G. (1999). Engineering psychology and
human performance (Third ed.). New Jersey: Prentice Hall.

Wickens, C. D., Lee, J. D., Liu, Y., & Gordon, S. E. (2003). *An
Introduction to Human Factors Engineering.* (2 ed.). New York: Longman.

Wierwille, W. W. (1993). Visual and manual demands of in-car controls
and displays. In W. Karwoski (Ed.), *Automotive Ergonomics* (pp.
299-320). Bristol, PA: Taylor and Francis.

Wiese, E. E. (2003). Attention Grounding: A new approach to IVIS
implementation: Unpublished master\'s thesis, University of Iowa.

Wikman, A. S., Nieminen, T., & Summala, H. (1998). Driving experience
and time-sharing during in-car tasks on roads of different width.
*Ergonomics, 41*(3), 358-372.

Williams, A. F. (2003). Teenage drivers: patterns of risk. *Journal of
Safety Research, 34*(1), 5-15.

Yassuda, M. S., Wilson, J. J., & von Mering, O. (1997). Driving
cessation: The perspective of senior drivers, *Educational Gerontology*
(Vol. 23, pp. 525-538).

Zhang, H., & Smith., M. (2004). *A Final Report of SAfety Vehicles using
adaptive Interface Technology (Phase I: Task 7): Visual Distraction
Research*. Retrieved January, 2005

Zijlstra, F. R. H. (1993). *Efficiency in work behavior. A design
approach for modern tools.* Unpublished PhD, Delft University of
Technology, Delft, The Netherlands.

[^1]: ∆: Mean difference

[^2]: CI: 95% Confidence interval for the mean difference