SAfety VEhicles using adaptive

Interface Technology

(Task 2b)

Visual Demand of Curves and Fog-Limited Sight Distance and Its
Relationship to Brake Response Time

> Prepared by

Brian Cullinane Paul Green

University of Michigan, UMTRI University of Michigan, UMTRI

Phone: (734) 763-3795 Phone: (734) 763-2485

Email: [bcullina@umich.edu](mailto:bcullina@umich.edu%09) Email:
<pagreen@umich.edu>

April (revised December), 2004

Revised, May 2006

#### TABLE OF CONTENTS

2.0 PROGRAM OVERVIEW [1](#program-overview)

2.1 INTRODUCTION [9](#introduction)

2.1.1 Why develop a workload manager?
[9](#why-develop-a-workload-manager)

2.1.2 Why focus on rear-end crashes?
[10](#why-focus-on-rear-end-crashes)

2.1.3 Why Examine Visual Demand? [11](#why-examine-visual-demand)

2.1.4 What Are the Research Issues? [13](#what-are-the-research-issues)

2.2 TEST PLAN [15](#test-plan)

2.2.1 Overview [15](#overview)

2.2.2 Roads [15](#roads)

2.2.3 Test Activities and Their Sequence
[19](#test-activities-and-their-sequence)

2.2.4 Driving Simulator [22](#driving-simulator)

2.2.5 Test Participants [23](#test-participants)

2.3 RESULTS [25](#results)

2.3.1 Visual Occlusion/Visual Demand Analysis
[25](#visual-occlusionvisual-demand-analysis)

*2.3.1.1 How Was Visual Demand Computed?*
[25](#how-was-visual-demand-computed)

*2.3.1.2 What Were Typical Visual Demand Curves?*
[26](#what-were-typical-visual-demand-curves)

2.3.2 ANOVA of Visual Demand [28](#anova-of-visual-demand)

2.3.3 Subjective Ratings of Visual Demand
[34](#subjective-ratings-of-visual-demand)

2.3.4 Response Times to Lead Vehicle Braking
[39](#response-times-to-lead-vehicle-braking)

2.3.5 Mean Response Time Profiles [40](#mean-response-time-profiles)

*2.3.5.1 ANOVA of Throttle Lift Off Response Time*
[41](#anova-of-throttle-lift-off-response-time)

*2.3.5.2 Prediction of Brake Response Time and Visual Demand*
[44](#prediction-of-brake-response-time-and-visual-demand)

2.4 CONCLUSIONS [49](#conclusions)

2.4.1. How does braking event response time vary as a function of road
geometry and subject differences?
[49](#how-does-braking-event-response-time-vary-as-a-function-of-road-geometry-and-subject-differences)

2.4.2. How does visual demand (measured using the visual occlusion
method) vary as a function of road geometry, presence of a lead vehicle,
subject differences, and how speed is controlled?
[50](#how-does-visual-demand-measured-using-the-visual-occlusion-method-vary-as-a-function-of-road-geometry-presence-of-a-lead-vehicle-subject-differences-and-how-speed-is-controlled)

2.4.3. How does rated visual demand vary as a function of road geometry,
subject differences, and how speed is controlled?
[51](#how-does-rated-visual-demand-vary-as-a-function-of-road-geometry-subject-differences-and-how-speed-is-controlled)

2.4.4. How closely does visual demand (measured by the visual occlusion
method) relate to rated visual demand in terms of road geometry, subject
differences, and how speed is controlled?
[51](#how-closely-does-visual-demand-measured-by-the-visual-occlusion-method-relate-to-rated-visual-demand-in-terms-of-road-geometry-subject-differences-and-how-speed-is-controlled)

2.4.5 Closing Thoughts [52](#closing-thoughts)

2.5 REFERENCES [53](#references)

2.6 APPENDIX A -- RATIONALE FOR CURVE-SIGHT DISTANCE COMBINATIONS
[57](#appendix-a-rationale-for-curve-sight-distance-combinations)

2.7 APPENDIX B -- ACTIVITY SEQUENCE RANDOMIZATION
[60](#appendix-b-activity-sequence-randomization)

2.7 APPENDIX C -- SIMULATOR INFORMATION
[60](#appendix-c-simulator-information)

2.7 APPENDIX D -- ADDITIONAL ANOVA ANALYSIS
[62](#appendix-d-additional-anova-analysis)

2.8 APPENDIX E - ADDITIONAL INFORMATION ABOUT REGRESSSION
[65](#appendix-e---additional-information-about-regresssion)

# 2.0 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.

To achieve the objective, Delphi Electronics & Safety has assembled a
comprehensive team including researchers and engineers from the
University of Iowa, University of Michigan Transportation Research
Institute (UMTRI), General Motors, Ford Motor Company, and Seeing
Machines, Inc. The SAVE-IT program is divided into two phases shown in
Figure i. Phase I spans one year (March 2003\--March 2004) and consists
of nine human factors tasks (Tasks 1-9) and one technology development
task (Task 10) for determination of diagnostic measures of driver
distraction and workload, architecture concept development, technology
development, and Phase II planning. Each of the Phase I tasks is further
divided into two sub-tasks. In the first sub-tasks (Tasks 1, 2A-10A),
the literature is reviewed, major findings are summarized, and research
needs are identified. In the second sub-tasks (Tasks 1, 2B-10B),
experiments will be performed and data will be analyzed to identify
diagnostic measures of distraction and workload and determine effective
and driver-friendly countermeasures. Phase II will span approximately
two years (October 2004\--October 2006) and consist of a continuation of
seven Phase I tasks (Tasks 2C\--8C) and five additional tasks (Tasks
11-15) for algorithm and guideline development, data fusion, integrated
countermeasure development, vehicle demonstration, and evaluation of
benefits.

![](media/image1.wmf){width="0.3138888888888889in"
height="1.9548611111111112in"}

It is worthwhile to note the SAVE-IT tasks in Figure i are
inter-related. They have been chosen to provide necessary human factors
data for a two-pronged approach to address the driver distraction and
adaptive safety warning countermeasure problems.

The first prong (Safety Warning Countermeasures sub-system) uses driver
distraction, intent, and driving task demand information to adaptively
adjust safety warning systems such as forward collision warning (FCW)
systems in order to enhance system effectiveness and user acceptance.
Task 1 is designed to determine which safety warning system(s) should be
deployed in the SAVE-IT system. Safety warning systems will require the
use of warnings about immediate traffic threats without an annoying rate
of false alarms and nuisance alerts. Both false alarms and nuisance
alerts will be reduced by system intelligence that integrates driver
state, intent, and driving task demand information that is obtained from
Tasks 2 (Driving Task Demand), 3 (Performance), 5 (Cognitive
Distraction), 7 (Visual Distraction), and 8 (Intent).

The safety warning system will adapt to the needs of the driver. When a
driver is cognitively and visually attending to the lead vehicle, for
example, the warning thresholds can be altered to delay the onset of the
FCW alarm or reduce the intrusiveness of the alerting stimuli. When a
driver intends to pass a slow-moving lead vehicle and the passing lane
is open, the auditory stimulus might be suppressed in order to reduce
the alert annoyance of a FCW system. Decreasing the number of false
positives may reduce the tendency for drivers to disregard safety system
warnings. Task 9 (Safety Warning Countermeasures) will investigate how
driver state and intent information can be used to adapt safety warning
systems to enhance their effectiveness and user acceptance. Tasks 10
(Technology Development), 11 (Data Fusion), 12 (Establish Guidelines and
Standards), 13 (System Integration), 14 (Evaluation), and 15 (Program
Summary and Benefit Evaluation) will incorporate the research results
gleaned from the other tasks to demonstrate the concept of adaptive
safety warning systems and evaluate and document the effectiveness, user
acceptance, driver understandability, and benefits and weaknesses of the
adaptive systems. It should be pointed out that the SAVE-IT system is a
relatively early step in bringing the driver into the loop and
therefore, system weaknesses will be evaluated, in addition to the
observed benefits.

The second prong of the SAVE-IT program (Distraction Mitigation
sub-system) will develop adaptive interface technologies to minimize
driver distraction to mitigate against a global increase in risk due to
inadequate attention allocation to the driving task. Two examples of the
distraction mitigation system include the delivery of a gentle warning
and the lockout of certain telematics functions when the driver is more
distracted than what the current driving environment allows. A major
focus of the SAVE-IT program is the comparison of various mitigation
methods in terms of their effectiveness, driver understandability, and
user acceptance. It is important that the mitigation system does not
introduce additional distraction or driver frustration. Because the
lockout method has been shown to be problematic in the aviation domain
and will likely cause similar problems for drivers, it should be
carefully studied before implementation. If this method is not shown to
be beneficial, it will not be implemented.

The distraction mitigation system will process the environmental demand
(Task 2: Driving Task Demand), the level of driver distraction \[Tasks 3
(Performance), 5 (Cognitive Distraction), 7 (Visual Distraction)\], the
intent of the driver (Task 8: Intent), and the telematics distraction
potential (Task 6: Telematics Demand) to determine which functions
should be advised against under a particular circumstance. Non-driving
task information and functions will be prioritized based on how crucial
the information is at a specific time relative to the level of driving
task demand. Task 4 will investigate distraction mitigation strategies
and methods that are very well accepted by the users (i.e., with a high
level of user acceptance) and understandable to the drivers. Tasks 10
(Technology Development), 11 (Data Fusion), 12 (Establish Guidelines and
Standards), 13 (System Integration), 14 (Evaluation), and 15 (Program
Summary and Benefit Evaluation) will incorporate the research results
gleaned from the other tasks to demonstrate the concept of using
adaptive interface technologies in distraction mitigation and evaluate
and document the effectiveness, driver understandability, user
acceptance, and benefits and potential weaknesses of these technologies.

In particular, driving task demand and driver state (including driver
distraction and impairment) form the major dimensions of a driver safety
system. It has been argued that crashes are frequently caused by drivers
paying insufficient attention when an unexpected event occurs, requiring
a novel (non-automatic) response. As displayed in Figure ii, attention
to the driving task may be depleted by driver impairment (due to
drowsiness, substance use, or a low level of arousal) leading to
diminished attentional resources, or allocation to non-driving
tasks[^1]. Because NHTSA is currently sponsoring other
impairment-related studies, the assessment of driver impairment is not
included in the SAVE-IT program at the present time. One assumption is
that safe driving requires that attention be commensurate with the
driving demand or unpredictability of the environment. Low demand
situations (e.g., straight country road with no traffic at daytime) may
require less attention because the driver can usually predict what will
happen in the next few seconds while the driver is attending elsewhere.
Conversely, high demand (e.g., multi-lane winding road with erratic
traffic) situations may require more attention because during any time
attention is diverted away, there is a high probability that a novel
response may be required. It is likely that most intuitively drivers
take the driving-task demand into account when deciding whether or not
to engage in a non-driving task. Although this assumption is likely to
be valid in a general sense, a counter argument is that problems may
also arise when the situation appears to be relatively benign and
drivers overestimate the predictability of the environment. Driving
environments that appear to be predictable may therefore leave drivers
less prepared to respond when an unexpected threat does arise.

A safety system that mitigates the use of in-vehicle information and
entertainment system (telematics) must balance both attention allocated
to the driving task that will be assessed in Tasks 3 (Performance), 5
(Cognitive Distraction), and 7 (Visual Distraction) and attention
demanded by the environment that will be assessed in Task 2 (Driving
Task Demand). The goal of the distraction mitigation system should be to
keep the level of attention allocated to the driving task above the
attentional requirements demanded by the current driving environment.
For example, as shown in Figure ii, "routine" driving may suffice during
low or moderate driving task demand, slightly distracted driving may be
adequate during low driving task demand, but high driving task demand
requires attentive driving.

![](media/image3.wmf){width="6.686111111111111in"
height="2.785416666666667in"}Figure ii. Attention allocation to driving
and non-driving tasks

It is important to note that the SAVE-IT system addresses both
high-demand and low-demand situations. With respect to the first prong
(Safety Warning Countermeasures sub-system), the safety warning systems
(e.g., the FCW system) will always be active, regardless of the demand.
Sensors will always be assessing the driving environment and driver
state. If traffic threats are detected, warnings will be issued that are
commensurate with the real time attentiveness of the driver, even under
low-demand situations. With respect to the second prong (Distraction
Mitigation sub-system), driver state including driver distraction and
intent will be continuously assessed under all circumstances. Warnings
may be issued and telematics functions may be screened out under both
high-demand and low-demand situations, although the threshold for
distraction mitigation may be different for these situations.

It should be pointed out that drivers tend to adapt their driving,
including distraction behavior and maintenance of speed and headway,
based on driving (e.g., traffic and weather) and non-driving conditions
(e.g., availability of telematics services), either consciously or
unconsciously. For example, drivers may shed non-driving tasks (e.g.,
ending a cell phone conversation) when driving under unfavorable traffic
and weather conditions. It is critical to understand this \"driver
adaptation\" phenomenon. In principle, the \"system adaptation\" in the
SAVE-IT program (i.e., adaptive safety warning countermeasures and
adaptive distraction mitigation sub-systems) should be carefully

implemented to ensure a fit between the two types of adaptation:
\"system adaptation\" and \"driver adaptation\". One potential problem
in a system that is inappropriately implemented is that the system and
the driver may be reacting to each other in an unstable manner. If the
system adaptation is on a shorter time scale than the driver adaptation,
the driver may become confused and frustrated. Therefore, it is
important to take the time scale into account. System adaptation should
fit the driver\'s mental model in order to ensure driver
understandability and user acceptance. Because of individual difference,
it may also be important to tailor the system to individual drivers in
order to maximize driver understandability and user acceptance. Due to
resource constraints, however, a nominal driver model will be adopted in
the initial SAVE-IT system. Driver profiling, machine learning of driver
behavior, individual difference-based system tailoring may be
investigated in future research programs.

Communication and Commonalities Among Tasks and Sites

In the SAVE-IT program, a \"divide-and-conquer\" approach has been
taken. The program is first divided into different tasks so that a
particular research question can be studied in a particular task. The
research findings from the various tasks are then brought together to
enable us to develop and evaluate integrated systems. Therefore, a
sensible balance of commonality and diversity is crucial to the program
success. Diversity is reflected by the fact that every task is designed
to address a unique question to achieve a particular objective. As a
matter of fact, no tasks are redundant or unnecessary. Diversity is
clearly demonstrated in the respective task reports. Also documented in
the task reports is the creativity of different task owners in attacking
different research problems.

Task commonality is very important to the integration of the research
results from the various tasks into a coherent system and is reflected
in terms of the common methods across the various tasks. Because of the
large number of tasks (a total of 15 tasks depicted in Figure i) and the
participation of multiple sites (Delphi Electronics & Safety, University
of Iowa, UMTRI, Ford Motor Company, and General Motors), close
coordination and commonality among the tasks and sites are key to
program success. Coordination mechanisms, task and site commonalities
have been built into the program and are reinforced with the bi-weekly
teleconference meetings and regular email and telephone communications.
It should be pointed out that little time was wasted in meetings.
Indeed, some bi-weekly meetings were brief when decisions can be made
quickly, or canceled when issues can be resolved before the meetings.
The level of coordination and commonality among multiple sites and tasks
is un-precedented and has greatly contributed to program success. A
selection of commonalities is described below.

[Commonalities Among Driving Simulators and Eye Tracking Systems In
Phase I]{.underline} Although the Phase I tasks are performed at three
sites (Delphi Electronics & Safety, University of Iowa, and UMTRI), the
same driving simulator software, Drive Safety^TM^ (formerly called
GlobalSim^TM^) from Drive Safety Inc., and the same eye tracking system,
FaceLab^TM^ from Seeing Machines, Inc. are used in Phase I tasks at all
sites. The performance variables (e.g., steering angle, lane position,
headway) and eye gaze measures (e.g., gaze coordinate) are defined in
the same manner across tasks.

[Common Dependent Variables]{.underline} An important activity of the
driving task is tactical maneuvering such as speed and lane choice,
navigation, and hazard monitoring. A key component of tactical
maneuvering is responding to unpredictable and probabilistic events
(e.g., lead vehicle braking, vehicles cutting in front) in a timely
fashion. Timely responses are critical for collision avoidance. If a
driver is distracted, attention is diverted from tactical maneuvering
and vehicle control, and consequently, reaction time (RT) to
probabilistic events increases. Because of the tight coupling between
reaction time and attention allocation, RT is a useful metric for
operationally defining the concept of driver distraction. Furthermore,
brake RT can be readily measured in a driving simulator and is widely
used as input to algorithms, such as the forward collision warning
algorithm (Task 9: Safety Warning Countermeasures). In other words, RT
is directly related to driver safety. Because of these reasons, RT to
probabilistic events is chosen as a primary, "ground-truth" dependent
variable in Tasks 2 (Driving Task Demand), 5 (Cognitive Distraction), 6
(Telematics Demand), 7 (Visual Distraction), and 9 (Safety Warning
Countermeasures).

Because RT may not account for all of the variance in driver behavior,
other measures such as steering entropy (Boer, 2001), headway, lane
position and variance (e.g., standard deviation of lane position or
SDLP), lane departures, and eye glance behavior (e.g., glance duration
and frequency) are also be considered. Together these measures will
provide a comprehensive picture about driver distraction, demand, and
workload.

[Common Driving Scenarios]{.underline} For the tasks that measure the
brake RT, the \"lead vehicle following\" scenario is used. Because human
factors and psychological research has indicated that RT may be
influenced by many factors (e.g., headway), care has been taken to
ensure a certain level of uniformity across different tasks. For
instance, a common lead vehicle (a white passenger car) was used. The
lead vehicle may brake infrequently (no more than 1 braking per minute)
and at an unpredictable moment. The vehicle braking was non-imminent in
all experiments (e.g., a low value of deceleration), except in Task 9
(Safety Warning Countermeasures) that requires an imminent braking. In
addition, the lead vehicle speed and the time headway between the lead
vehicle and the host vehicle are commonized across tasks to a large
extent.

[Subject Demographics]{.underline} It has been shown in the past that
driver ages influence driving performance, user acceptance, and driver
understandability. Because the age effect is not the focus of the
SAVE-IT program, it is not possible to include all driver ages in every
task with the budgetary and resource constraints. Rather than using
different subject ages in different tasks, however, driver ages are
commonized across tasks. Three age groups are defined: younger group
(18-25 years old), middle group (35-55 years old), and older group
(65-75 years old). Because not all age groups can be used in all tasks,
one age group (the middle group) is chosen as the common age group that
is used in every task. One reason for this choice is that drivers of
35-55 years old are the likely initial buyers and users of vehicles with
advanced technologies such as the SAVE-IT systems. Although the age
effect is not the focus of the program, it is examined in some tasks. In
those tasks, multiple age groups were used.

The number of subjects per condition per task is based on the particular
experimental design and condition, the effect size shown in the
literature, and resource constraints. In order to ensure a reasonable
level of uniformity across tasks and confidence in the research results,
a minimum of eight subjects is used for each and every condition. The
typical number of subjects is considerably larger than the minimum,
frequently between 10-20.

[Other Commonalities]{.underline} In addition to the commonalities
across all tasks and all sites, there are additional common features
between two or three tasks. For example, the simulator roadway
environment and scripting events (e.g., the TCL scripts used in the
driving simulator for the headway control and braking event onset) may
be shared between experiments, the same distraction (non-driving) tasks
may be used in different experiments, and the same research methods and
models (e.g., Hidden Markov Model) may be deployed in various tasks.
These commonalities afford the consistency among the tasks that is
needed to develop and demonstrate a coherent SAVE-IT system.

# 

The Content and Structure of the Report

The report submitted herein is a final report for Task 2 that documents
the research progress to date (March 2003-March 2004) in Phase I. In
this report, the major results from the literature review are summarized
to determine the research needs for the present study, the experimental
methods and resultant data are described, diagnostic measures and
preliminary algorithms are identified, and human factors recommendations
are offered.

#  

# 2.1 INTRODUCTION

## 2.1.1 Why develop a workload manager?

As competition in the market place grows and product quality improves,
it is becoming increasingly difficult for automotive manufacturers to
distinguish their products from those of their competitors. This is
particularly true for the conventional aspects that people consider when
buying a motor vehicle such as price, fuel economy, reliability, and so
forth. Therefore, manufacturers are looking at providing features in
their products that distinguish their products from the competition.

Telematics, in-vehicle information and communications systems for
drivers, is one collection of features of interest. Common telematics
features include navigation, cell phones, collision avoidance systems,
parking assist systems, and so forth. Although telematics can greatly
enhance the comfort, convenience, and safety of driving, there is
concern that some tasks, when performed with some systems, can pose
unacceptable risks to drivers. For that reason, there has been
discussion of, for example, banning cell phone use while driving.

For other systems, such as navigation, there are limitations on tasks to
keep these tasks from interfering too much with driving. For example,
SAE Recommended Practice J2364 (the "15-Second Rule"), states that no
navigation task, when tested statically (parked), should take longer
than 15 seconds to complete (Society of Automotive Engineers, 2003a,b).
In fact, when driving, the task will take longer than 15 seconds, but
the static procedure has been chosen for ease of implementation.
Further, J2364 has an alternative procedure that involves visual
occlusion. See the Recommended Practice for details. A huge advantage of
J2364 is that compliance (that is, task times) can be estimated using
SAE Recommended Practice J2365 (Society of Automotive Engineers, 2002)

Complementing SAE J2364 are other procedures that have been developed by
the Alliance of Automobile Manufacturers and by the International
Standards Organization (ISO).

Another approach that might either complement or supplement such rules
is to employ a workload manager (Michon, 1993; Green, 2000; Hoedemaeker,
de Ridder, and Janssen, 2002; Remboski, Gardner, Wheatley, Hurwitz,
MacTavish, and Gardner, 2000). This device will continually measure the
workload of the driving task, and knowing the task demands and
potentially the driver's capabilities, decide which particular tasks
could be executed at what time. For example, in heavy traffic in a
rainstorm, the system might automatically direct all incoming cell phone
calls to an answering machine. At other times, the system might prevent
drivers from entering street addresses, but allow them to complete a
generally easier task, such as selecting points of interest.

## 2.1.2 Why focus on rear-end crashes?

This report is part of a major, federally-funded project to develop a
workload manager prototype to reduce distraction-related crashes. In
reviewing that literature (Wang, Knipling, and Goodman, 1996; Stutts,
Reinfurt, Staplin, and Rodgman, 2001; Eby and Kostyniuk, 2003), an
important point emerged that crashes in which distraction is a causal
factor are statistically different from other crashes. Table 2.1
presents data by crash type across such conditions as sleepy,
distracted, looked but did not see, unknown, and attentive.

Table 2.1. Crashes by Type

+------------+-------+----------+--------+---------+---------+------+
| > Crash    | > S   | > **Dist | >      | >       | > At    | > *  |
| > type     | leepy | racted** | Looked | Unknown | tentive | *Tot |
| >          |       |          | > but  |         |         | al** |
| > Row %    |       |          | > did  |         |         |      |
| >          |       |          | > not  |         |         |      |
| > Column % |       |          | > see  |         |         |      |
+------------+-------+----------+--------+---------+---------+------+
| > Single   | > 58  | > 18.1   | > 0.2  | > 3.8   | > 44.1  | > 1  |
| > vehicle  | >     | >        | >      | >       | >       | 00.0 |
|            | >     | >        | > 0.7  | > 20.6  | > *     | >    |
|            |  66.2 | **41.2** |        |         | *45.9** | >    |
|            |       |          |        |         |         | **30 |
|            |       |          |        |         |         | .0** |
+------------+-------+----------+--------+---------+---------+------+
| > Rea      | >     | > 21.3   | > 3.4@ | > 48.3  | > 14.3  | > 1  |
| r-end/Lead | 12.7@ | >        | >      | >       | >       | 00.0 |
| > vehicle  | >     | >        | > 2.0  | > 6.4   | >       | >    |
| > moving   | >     |  **9.6** |        |         | **2.9** | >    |
|            |  27.9 |          |        |         |         |  **5 |
|            |       |          |        |         |         | .9** |
+------------+-------+----------+--------+---------+---------+------+
| > R        | > \*  | > 23.9   | > 11.4 | > 52.6  | > 11\.  | > 1  |
| ear-end/LV |       | >        | >      | >       | >       | 00.0 |
| > stopped  |       | >        | > 13.8 | > 14.1  | >       | >    |
|            |       | **21.9** |        |         | **4.9** | >    |
|            |       |          |        |         |         | **12 |
|            |       |          |        |         |         | .1** |
+------------+-------+----------+--------+---------+---------+------+
| > Int      | > \*  | > 7.0    | > 17.9 | > 528   | > 22.3  | > 1  |
| ersection/ |       | >        | >      | >       | >       | 00.0 |
| > Cross    |       | > *18.1* | > 63.6 | > 39.8  | > 26.6  | >    |
| > path     |       |          |        |         |         | >    |
|            |       |          |        |         |         | **34 |
|            |       |          |        |         |         | .3** |
+------------+-------+----------+--------+---------+---------+------+
| > Lane     | > \*  | > 5.6    | > 7.2  | > 41.8  | > 35.3  | > 1  |
| > change/  |       | >        | >      | >       | >       | 00.0 |
| > Merge    |       | > 1.6    | > 6.7  | > 3.4   | > 4.6   | >    |
|            |       |          |        |         |         | >    |
|            |       |          |        |         |         |  **3 |
|            |       |          |        |         |         | .8** |
+------------+-------+----------+--------+---------+---------+------+
| > Head-on  | > 1.0 | > 7.0    | > 8.1  | > 46.4  | > 37.5  | > 1  |
|            | >     | >        | >      | >       | >       | 00.0 |
|            | > 1.7 | > 2.2    | > 3.5  | > 4.3   | > 5.0   | >    |
|            |       |          |        |         |         | >    |
|            |       |          |        |         |         |  **4 |
|            |       |          |        |         |         | .2** |
+------------+-------+----------+--------+---------+---------+------+
| > Other    | > \*  | > 7.3    | > 9.7  | > 535   | > 28.9  | > 1  |
|            |       | >        | >      | >       | >       | 00.0 |
|            |       | > 5.4    | > 9.7  | > 11.4  | > 9.7   | >    |
|            |       |          |        |         |         | >    |
|            |       |          |        |         |         |  **9 |
|            |       |          |        |         |         | .7** |
+------------+-------+----------+--------+---------+---------+------+
| > Total    | > 2.6 | > 13.2   | > 9.7  | > 45.6  | > 28.8  | > 1  |
| > Crashes  | >     | >        | >      | >       | >       | 00.0 |
|            | >     | > 100.0  | >      | > 100.0 | > 100.0 | >    |
|            | 100.0 |          |  100.0 |         |         | > *  |
|            |       |          |        |         |         | *100 |
|            |       |          |        |         |         | .0** |
+------------+-------+----------+--------+---------+---------+------+

@=5-9 cases

\*=too few for a stable estimate

Source: Wang, Knipling, and Goodman (1996)

Note: Within each cell, there are two values representing the row and
column percents respectively. For example, referring to the single
vehicle distracted cell, in 18.1% of all vehicle crashes, drivers were
distracted. Of all distracted crashes, 41.2% involve single vehicles.

Notice that rear end collisions, both into moving and stopped vehicles
are relatively much more common when drivers are distracted than when
they are attentive. It was this and other data that led to this project
focusing on rear-end crash scenarios.

## 2.1.3 Why Examine Visual Demand?

Given this finding, what measure of performance should be assessed in
experiments to support workload manager development? Previous research
presents many possibilities (Gawron, 2000, Brookhuis and De Waard, 2001;
Kantowitz and Simsek, 2001). However, in this case, the accepted
proposal submitted by the prime contractor for this project, Delphi,
focused on brake response time. Indeed, the more complicated the driving
situation, longer it should take drivers to respond.

Although response time can be easy to measure, collecting enough
response times within a 2-hour test session in an experiment is a
problem and the project resources were insufficient to conduct numerous
experiments. A two-hour limit is about the maximum for a subject, and it
is highly desirable to examine differences within subjects while not
requiring more than one session. With multi-session experiments, there
are problems with subjects failing to return, with variable time periods
between sessions due to subject schedules, and with other factors that
make them generally undesirable.

In a typical 2-hour experiment, about 10-15 minutes are needed for the
introduction, 5-10 minutes for the initial practice drive to become
familiar with the simulator, and 5-10 minutes for debriefing, final
comments, and payment. Thus, only about 90 minutes are available for
experimentation. Of that time, another 10-20 minutes are needed for
baseline driving. In telematics studies, drivers are usually first
trained in using a device. Then baseline (parked) device use data is
collected, followed by dual task (while driving) use data as well as
data for just driving. Thus, depending on the number of conditions
examined (for example, several levels of driving workload), the time
available for testing in the most complex condition (for example, dual
task in high workload) is only 20 -- 40 minutes, depending on the number
of conditions explored.

For discussion, assume the mean of that range is 30 minutes. How many
braking response times can be collected in 30 minutes? To make the
braking events somewhat unpredictable (and indicative of real driving
situations), having no more than 1 event per minute seems reasonable,
with some variation of the interval between braking events. This
suggests a maximum of 30 events of interest per experiment.

Having some replication within subjects is highly desired. For
discussion, assume the minimum is two. This leads to 15 (30/2) unique
combinations per subject. For example, a 3 x 5 design might be used,
which places huge limitations on the number of factors and combinations
that might be explored. Thus, using response time measures directly, a
large number of experiments would be required to comprehensively examine
the factors of interest.

There is at least one other approach. Response time to a lead vehicle
braking should depend on the visual demand of the driving situation. The
more information in the scene a driver needs to consider, the more time
the driver requires to respond to a lead vehicle braking. One might
think of other scene information (vehicles other than the lead vehicle,
complex road geometry, signs, etc.) as distracting the driver from
attending to the lead vehicle. Furthermore, difficulty in extracting
scene information (for example, due to limited visibility) also
increases the demand of the driving situation and accordingly, response
time.

There are many ways visual demand can be measured, though the favored
measure here relies on visual occlusion. Though no one knows for sure
how much of the driving task is visual (Sivak, 1996), there is agreement
that driving is primarily a visual task and that measuring the visual
component of driving captures much of the workload.

The idea behind the visual occlusion method is that one must see in
order to drive. In its simplest form, subjects close their eyes whenever
they can and the fraction of time their eyes are open indicates visual
demand. See Wooldridge, Bauer, Green, and Fitzpatrick (2000) and van der
Horst (2001) for contemporary on-the-road studies using this method and
Senders, Kristofferson, Levison, Dietrich, and Ward (1966, 1967a, b) and
Senders and Ward (1968) for some of the original applications of this
method to driving. However, for the purpose of this project, obtaining
permission for such methods would have been extremely difficult, so this
experiment was conducted in a driving simulator. Giving subjects
complete control over both the glance interval and the time between
glances complicates analysis, because the tradeoff of these two
characteristics is unknown. There were inadequate resources to conduct a
tradeoff study within the context of this project.

Therefore, to avoid a complicating tradeoff, subjects pressed a button
in order to view the road (for 0.5 seconds), a procedure that has been
used before (Tsimhoni and Green, 1999). At all other times, the road
scene was a uniform gray field roughly matching the luminance of the
un-occluded screen. The percentage of time the road scene was visible is
an effective indicator of visual demand.

At 50% demand, a reasonable level for these experiments, there was about
1 button press per second, a stark contrast to the 1 per 60 seconds for
response time. Furthermore, although visual demand can be estimated by
the number of button presses over a series of events, a more accurate
estimate of demand can be obtained from the interval between key presses
(the interarrival time) as one would expect from queueing theory.

Thus, the approach in this project is to determine the relationship
between visual demand and response, and then use the more numerous
demand data to predict response time. Again, given the low number of
test conditions that can be explored in a braking response time
experiment, an unacceptably large number of experiments would be
required to collect the data necessary to build a workload manager.

There is some risk in only using occlusion data to assess visual demand.
Therefore, as a backup and to provide supporting evidence, subjective
workload ratings were collected after each road segment. This was easy
to do.

## 2.1.4 What Are the Research Issues?

Accordingly, 4 issues were examined in this experiment:

1\. How does braking event response time vary as a function of road
geometry and subject differences?

2\. How does visual demand (measured using the visual occlusion method)
vary as a function of road geometry, presence of a lead vehicle, subject
differences, and how speed is controlled?

3\. How does rated visual demand vary as a function of road geometry,
subject differences, and how speed is controlled?

4\. How closely does visual demand (measured by the visual occlusion
method) relate to rated visual demand in terms of road geometry, subject
differences, and how speed is controlled?

There was also interest in differences in driver performance between
conditions, but that topic was secondary and was not examined in this
report.

# 2.2 TEST PLAN

## 2.2.1 Overview

To determine the relationship between visual demand and brake response
time, subjects drove a two-lane rural road in the UMTRI simulator,
sometimes following a lead vehicle that braked. The road consisted of
straight and curved sections with varying sight distances (due to fog),
and therefore visual demand varied. Visual demand was measured using the
visual occlusion method and by ratings.

## 2.2.2 Roads

Two simulated worlds of two-lane rural roads were used in this
experiment: "square world" and "zig-zag world." Square world (Figure
2.1) was a loop of four left curves connected by straight road sections
used to familiarize subjects with simulator dynamics in a brief period
of time, and hence, was used only in practice sessions. To simplify the
driving task, no traffic or fog was present.

![](media/image4.pct){width="4.4319444444444445in"
height="4.054861111111111in"}

Figure 2.1. Practice Square World

The main test portions of this experiment were carried out in "zig-zag
world" (Figure 2.2). The test world was designed to allow for several
visual demand levels (straight plus two curve radii, three sight
distances limited by fog and, in some cases, traffic) and for the
sequence of those levels to be unpredictable / difficult to memorize.
Furthermore, each of the major variations of interest (curve radius, fog
sight distance) was experienced at least twice per drive, allowing for
some replication of test conditions within runs. To maximize test time,
the time spent loading worlds and saving data had to be minimized, so
the same basic world was used for all test runs. The complete rationale
for the design of the roads appears in Appendix A. Session time limits
did not allow for a factorial exploration of all combinations of
curvature, sight distance, and traffic.

Sight distance was manipulated by varying the density of the intervening
fog using the standard setfog command in the DriveSafety software. The
transmissivity (1‑opaqueness) of the road scene declined exponentially
with distance, dropping to almost zero at the site distance. Beyond the
set distance, no objects were visible, that is, the scene was opaque.
This decline is believed to reasonably mimic the atmospheric effects of
fog. Implementation details differ between versions 1.6.x and 1.9 of the
software. Some details of the 1.6.x version (used for this study) are
not available because the source code for the routines called by the
DriveSafety software is not available.

![](media/image5.pct){width="6.0in" height="7.888888888888889in"}

Figure 2.2. Curve Radii and Section Lengths for Zig-Zag (Test) World

Observations in prior research indicated that driving performance
declined in occlusion conditions, but that finding had been not examined
to the detail desired. Therefore, conditions were included in this
experiment in which subjects drove the same road occluded and
un-occluded. Furthermore, since subjects often compensate for increased
workload by slowing down (to reduce workload), it was important to have
uncontaminated conditions where speed is cruise controlled (and not able
to be reduced by the subject).

Just as with road geometry variations, the two-hour maximum session
duration (and 19 minutes required for each drive of the "zig-zag world")
limited the combinations of other major factors of interest (lead
vehicle, occlusion, speed control) that could be explored. (See Table
2.2.) The un-occluded cruise control pair (B & F in Table 2.2) were not
examined as performance in those conditions (namely lateral control)
should be very similar to the subject-controlled speed conditions.
Forcing the speed (cruise) suggests lateral variance would increase
slightly. However, reducing the number of tasks (accelerator use not
required when using the cruise control) should decrease workload.

Table 2.2. Test Condition Combinations (Selected Conditions in Bold)

+---------+--------------+-----------+-----------------+-------------+
|         | > Not        |           | > Occluded (w/  |             |
|         | > occluded   |           | > fog)          |             |
|         | > (w/o fog)  |           |                 |             |
+---------+--------------+-----------+-----------------+-------------+
|         | > Subjec     | > Cruise  | > Sub           | > Cruise    |
|         | t-controlled |           | ject-controlled |             |
+---------+--------------+-----------+-----------------+-------------+
| > No    | > **A.       | > B.      | > **C. baseline | > **D.      |
| > lead  | > "free"     | >         | > demand of     | > "pure"    |
| >       | > drive**    |  baseline | > road          | > measure   |
| vehicle | >            | > cruise  | >               | > of the    |
|         | > (only need | >         |  "contaminated" | > demand of |
|         | > short      | > (only   | > unsteady      | > the road  |
|         | > drive)     | > need    | > speed         | > (ratings  |
|         |              | > short   | > (ratings      | > c         |
|         |              | > drive)  | > collected)**  | ollected)** |
+---------+--------------+-----------+-----------------+-------------+
| > Lead  | > **E.       | > F. RT   | > **G. demand   | > H. "pure" |
| >       | > baseline   | > to lead | > with lead     | > demand    |
| vehicle | > RT (RT     | > with    | > vehicle       | > with lead |
|         | >            | > cruise  | >               | > vehicle   |
|         | collected)** | >         |  "contaminated" |             |
|         |              | > (not    | > by subject    |             |
|         |              | > needed, | > speed**       |             |
|         |              | > similar |                 |             |
|         |              | > to 5,   |                 |             |
|         |              | > maybe   |                 |             |
|         |              | > more    |                 |             |
|         |              | > stable) |                 |             |
+---------+--------------+-----------+-----------------+-------------+

Further, cell H, the "pure" demand option (response time to lead vehicle
braking with occlusion while under cruise control), was not examined
because there was thought to be little difference between the cruise and
subject-controlled speed conditions. The difference between cells C & D
could be used to examine this hypothesis. Also, the software to control
speed for cell H, in particular after a braking event, might have been a
development effort that would have challenged the project schedule.

## 2.2.3 Test Activities and Their Sequence

Table 2.3 shows the sequence of activities in this experiment, which was
partially counterbalanced (for activities 5--7 only where it was thought
to be crucial), with the easier conditions occurring first. The
extensive practice provided was thought to be sufficient to stabilize
driving performance. The alternative, counterbalancing all of the
conditions, might have overwhelmed subjects if some of the more
difficult combinations occurred earlier in the experiment. (Note: The
ordering of activities 5-7 by subject is shown in Appendix B.) To
minimize effects due to the order in which road features were
encountered, half of the subjects drove the world from each of the two
potential start points.

Table 2.3. Sequence of Activities (Note: The bold letter in the activity
column refers to the prior table.)

+------------+--------+---------+-------+-----+-----------------+-----+
| ####       | Block  | ##### O | #     | ##  | ##### Purpose   | T   |
| # Activity |        | cclude  | ####  | ### |                 | ime |
|            | type - | Vision  | Speed |  Le |                 | (m  |
|            | world  |         |       | ad  |                 | in) |
|            |        |         | co    | car |                 |     |
|            |        |         | ntrol |     |                 |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 1\. Intro  | \-     | \-      | \-    | \-  | Obtain          | 12  |
|            |        |         |       |     | description of  |     |
| & consent  |        |         |       |     | subject &       |     |
| form       |        |         |       |     | permission      |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 2\.        | Pr     | No      | Su    | No  | Learn how to    | 7   |
| Practice   | actice |         | bject |     | drive simulator |     |
| drive      | s      |         |       |     |                 |     |
|            | quare, |         |       |     |                 |     |
|            |        |         |       |     |                 |     |
|            | 1 loop |         |       |     |                 |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 3\.        | Test - | No      | Su    | No  | Obtain baseline | 9   |
| Baseline   | zi     |         | bject |     | data on how     |     |
| drive -    | g-zag, |         |       |     | people drive    |     |
| just drive | second |         |       |     | when they       |     |
| (**A**)    | half   |         |       |     | control their   |     |
|            |        |         |       |     | speed           |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 4\.        | Pr     | Yes     | Su    | No  | Learn to use    | 7   |
| Occlusion  | actice |         | bject |     | the occlusion   |     |
| practice   | -s     |         |       |     | method          |     |
| (**B**)    | quare, |         |       |     |                 |     |
|            | 1 loop |         |       |     |                 |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 5\. Demand | Test - | Yes     | C     | No  | Determine       | 19  |
| of road    | z      |         | ruise |     | demand when     |     |
| when       | ig-zag |         |       |     | cruise control  |     |
| cruise     |        |         |       |     | is used         |     |
| control is |        |         |       |     |                 |     |
| used       |        |         |       |     |                 |     |
| (**D**)    |        |         |       |     |                 |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 6\.        | Test - | No      | Su    | Yes | Obtain          | 19  |
| Baseline   | z      |         | bject |     | "               |     |
| RT (**E**) | ig-zag |         |       |     | uncontaminated" |     |
|            |        |         |       |     | RT data         |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 7\. Demand | Test - | Yes     | Su    | No  | Determine       | 19  |
| of road    | z      |         | bject |     | demand when     |     |
| when subj. | ig-zag |         |       |     | subject         |     |
| controls   |        |         |       |     | controls the    |     |
| speed      |        |         |       |     | speed           |     |
| (**C**)    |        |         |       |     |                 |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 8\. Demand | Test - | Yes     | Su    | Yes | Total demand    | 19  |
| of road +  | z      |         | bject |     |                 |     |
| lead       | ig-zag |         |       |     |                 |     |
| vehicle    |        |         |       |     |                 |     |
| (**G**)    |        |         |       |     |                 |     |
+------------+--------+---------+-------+-----+-----------------+-----+
| 9\. Final  | \-     | \-      | \-    | \-  | Obtain          | 5   |
| comments   |        |         |       |     | explanations    |     |
|            |        |         |       |     | for responses   |     |
+------------+--------+---------+-------+-----+-----------------+-----+
|            |        | ######  | ##    | #   | ###### Total    | 116 |
|            |        |         | ####  | ### |                 |     |
|            |        |         |       | ##  |                 |     |
+------------+--------+---------+-------+-----+-----------------+-----+

The behavior of the lead vehicle followed a script that had been used in
other experiments within this project. Braking events were to occur
approximately once per minute when the time headway was 1.8 s, with the
lead vehicle braking at .2 g for 4-5 s (specified later), causing the
lead vehicle's brake lights to illuminate and the lead vehicle to pitch
forward slightly. The subject was unable to lag the lead vehicle by more
the 1.8 s, pass the vehicle, or go through it. To make braking events
somewhat unexpected, braking events only occurred on straight sections.

After completing each curve or straight section for all sections,
subjects rated the workload of driving that section using a 1 to 10
rating scale (Table 2.4). To avoid overloading subjects, ratings were
only taken for the two conditions (activities 5 and 7 in Test Plan) in
which occlusion was present, a lead vehicle was not, and the subjects'
velocity was either self- or cruise-controlled. Before beginning the
runs in which ratings were collected, each subject was required to read
all 10 of the possible options and rate one of the curves they had just
experienced in an initial test run. The rating scale was discussed at
length before the next run was presented. Instruction continued until
subjects fully understood the rating scale. The subjects were instructed
to look at the ratings listed on a card only if necessary (located
slightly down and to the right of straight ahead) to choose a risk
rating. When subjects looked, glances were brief, and hence, the added
load of the rating task was minimal.

Table 2.4. Subjective Risk Ratings

  -------- ---------------------------------------------------------------
  Risk     As Risky As:
  Rating   

  10       Driving with my eyes closed. A crash is bound to occur every
           time I do this.

  9        Passing a school bus that has its red lights flashing and the
           stop arm in full view

  8        Driving just under the legal alcohol limit with observed
           weaving in the lane

  7         

  6        Driving 20 miles an hour faster than traffic on an expressway

  5         

  4        Driving 10 miles an hour faster than traffic on an expressway

  3         

  2        Driving on an average road under average conditions

  1        Driving on an easy road with no traffic, pedestrians, or
           animals while perfectly alert
  -------- ---------------------------------------------------------------

As subjects drove, run time, subject velocity, lane position, lane
number, steering wheel value, accelerator position, brake position,
subject heading, subject lateral position and distance traveled, headway
time and distance (if applicable), time to collision (TTC), and lead
vehicle velocity (if applicable) were collected at 20 Hz, as well as the
current state of occlusion pertaining to each button press. In addition,
(1) the road scene ahead, (2) the road scene behind, (3) the driver's
face, and (4) the speedometer/tachometer cluster were recorded by a VCR
as a quad-split image.

To un-occlude the screen, subjects pressed a finger switch (attached to
their finger via a Velcro ring) against the steering wheel. Subjects
were told that when the button was pressed, the screen would become
visible for 0.5 seconds and then turn gray again. Subjects were told "As
much as you need to, you can press the button in order to see the
screen. Please do not simply hold the button down unless feel you must
in order to drive effectively. Just act and drive as you would normally
through fog."

## 2.2.4 Driving Simulator

Each of the three experiments took place in the third generation UMTRI
Driving Simulator (www.umich.edu/\~driving/simulator.html). The
simulator consisted of a full size cab, computers, video projectors,
cameras, audio equipment, and other items (Figure 2.3). The simulator
has a forward field of view of 120 degrees (3 channels) and a rear field
of view of 40 degrees (1 channel). The forward screen was approximately
16-17 feet (4.9-5.2 m) from the driver's eyes, close to the 20-foot (6
m) distance often approximating optical infinity in accommodation
studies. To reduce the likelihood of motion sickness, only the center
front and rear channels were used.

![](media/image6.pct){width="4.152777777777778in"
height="3.111111111111111in"}

Figure 2.3. Simulator Screen, Cab, and Control Room

The vehicle mockup consisted of the A-to-B pillar section of a 1985
Chrysler Laser with a custom-made hood and back end. Mounted in the
mockup was a torque motor connected to the steering wheel (to provide
steering feedback), an LCD projector under the hood (to show the
speedometer/tachometer cluster), a touch screen monitor in the center
console (for in-vehicle tasks), a 10-speaker sound system (for auditory
warnings), a sub-bass sound system (to provide vertical vibration), and
a 5-speaker surround system (to provide simulated background road
noise). The 10-speaker sound system was from a 2002 Nissan Altima and
was installed in the A-pillars and lower door panel, and behind each of
the two front seats. The stock amplifier (from the 2002 Nissan Altima)
drove the speakers. The main simulator hardware and software was a
DriveSafety Vection simulator running version 1.6.1 of the software. The
display cards, GeForce3's, did not support anti-aliasing.

The simulator was controlled from a "room" on the driver's side of the
vehicle and behind it. The room contained a large table with multiple
quad-split video monitors to show the output of every camera and
computer, a keyboard and LCD for the driving simulator computers, and a
second keyboard and LCD to control the instrument panel and touch screen
software. Also in the control room was a 19-inch rack containing all of
the audio and video equipment (audio mixers, video patch panel and
switchers, distribution amplifiers, VCR, quad splitter, etc.) and two
separate racks for the instrument panel and touch screen computers, the
simulator host computers, and the four simulator image generators. The
instrument panel and center console computers ran under the Mac OS. The
user interface to the simulator ran under Windows and the simulators ran
under Linux.

Additional information on the simulator (e.g. a plan view of the
facility with dimensions and the manufacturer and model numbers of key
components) appears in Appendix C.

## 2.2.5 Test Participants

A total of 16 licensed drivers, eight ages 35-55 and eight ages 65-75,
served as subjects. Within each age group there were an equal number of
men and women. Most of the subjects were recruited from a list of people
who had previously participated in driver interface studies. None of the
subjects had previously participated in occlusion studies.

Most subjects listed their primary vehicle as a mid-sized car, while two
listed a mini-van or SUV. The mean average mileage driven by the
subjects was approximately 11,250 miles, with a range of 2,000 to 25,000
miles per year. Three subjects had special driver's licenses: a cycle,
chauffeur, and commercial drivers license (CDL).

In this experiment, 12 subjects wore glasses while driving, one subject
wore contacts, and three did not require any vision correction. The mean
far acuity was 20/24, with a range of 20/17 to 20/30. The mean near
acuity was 20/30, with a range of 20/17 to 20/50 (the maximum allowed).

As a group, participants were not aggressive drivers. When asked where
they would drive on a 3-lane highway (3=left, 2=middle, or 1=right
lane), the mean was 1.9. Six of the 16 participants claimed having an
accident within the last 5 years, but none of the subjects had more than
one accident during that time period. Five subjects reported a moving
violation within the last year (3 subjects with 1 violation, 1 with 2
violations, and 1 with 3).

# 2.3 RESULTS

## 2.3.1 Visual Occlusion/Visual Demand Analysis

How does visual demand (measured using the visual occlusion method) vary
as a function of road geometry, subject differences and how speed is
controlled?

### *2.3.1.1 How Was Visual Demand Computed?*

Visual demand was defined as the percentage of time the screen was
un-occluded (i.e., the road scene was visible) for a segment of a set
distance. For this analysis, a distance of 20 meters was chosen as the
segment length to provide one data point per second; that is,
second-by-second analysis. (Note: Subjects drove at approximately 45
miles per hour, which is about 66 feet per second or 22 meters per
second.)

There are at least three methods to calculate visual demand: simulator
sampling state, interkeypress interval, and keypress counting method. In
the simulator sampling state method, each time the simulator samples the
driving performance variables (speed, lateral position, steering wheel
angle, etc.), it also records if the road scene was occluded or
un-occluded in one field of the sample record. Computation of demand
began by setting a distance counter to 0. The counter was incremented
one record at a time (one line at a time) until the distance traveled
was equal to or greater than 20 meters. Within that set of lines, the
percentage flagged as un-occluded determined demand for that segment.

For subsequent segments, the distance counter was set back to 0 and the
process continued in 20-meter increments until the subject reached the
end of the section, either the beginning of a curve (the point of
curvature, PC) or the end of a curve (the point of tangency, PT). If the
section did not end exactly at the end of a 20-meter segment, the
remaining segment (\<20 m) was viewed as insignificant and discarded.

For the interkeypress interval (arrival method), visual demand is
proportional to the inverse of the time between keypresses. Because
keypresses are often recorded with millisecond accuracy, this method has
the promise of high accuracy. However, how to analyze keypress pairs
that span segment boundaries, a nontrivial computational problem, has
yet to be decided.

The simplest method to compute demand is to count the number of
keypresses within a fixed time interval. The demand is equal to the
number of keypresses in an interval times the viewing duration (usually
0.5 s), divided by the sampling interval. So, if the duration is 1 s,
there can be none, one, or two keypresses in that time period, resulting
in demand values of 0, 50, or 100 %. This method leads to estimates that
are crude.

For this report, the simulator sampling method was used to compute
visual demand. The sampling frequency of 20 Hz was a compromise of
providing desired accuracy but avoiding data management problems. For
other channels, such as steering wheel position and throttle position,
most of the signal is at a maximum of 5 Hz so, according to the Nyquist
folding principle, sampling at above 10 Hz was not needed. Sampling
could have been as high as 60 Hz, which would have improved the accuracy
of the visual demand estimates but tripled the amount of data saved. For
this initial examination of demand on this simulator, 20 Hz was chosen
as a compromise, leading to demand estimates to the nearest 5% for each
second. Because of time constraints, each subject drove each road only
once (versus 6 times in Tsimhoni and Green, 1999). However, each subject
drove all curvature-sight distance combinations twice and demand could
be averaged over successive segments, leading to improvements in
accuracy. Had there been more repetitions of roads, fewer variables
could have been explored.

### *2.3.1.2 What Were Typical Visual Demand Curves?*

Before describing the results with regard to the specific experimental
data, it is useful to provide a more general impression of the data.
Figure 2.4 shows visual demand results for a representative subject
(Subject 9, 68-year-old male) for each of his three runs involving
occlusion: (1) subject-controlled speed, (2) cruise-controlled speed,
and (3) subject-controlled speed with a lead vehicle. As a reminder, all
runs occurred in the "zig-zag world." For the subject-controlled
condition, the subject controlled his/her velocity and there was no lead
vehicle or fog present (cell C of Table 3). For cruise controlled, the
test conditions were the same except that the speed driven was
externally controlled and fixed (cell D of Table 3). The "subject
controlled with a lead vehicle" condition corresponds to cell G of Table
3. Fog was present for all three conditions.

![](media/image7.pct){width="5.847222222222222in"
height="2.0833333333333335in"}

Figure 2.4. Visual Demand Observations for Representative Subject

SC=subject-controlled speed, CC=cruise-controlled speed,
SC+LV=subject-controlled speed with lead vehicle

As can be seen in Figure 2.4, the three trials show a similar pattern of
visual demand: an initial period of increasing demand, then a more
stable level of demand for the rest of the experiment. The hills and
valleys of demand correspond to differences in road geometry (sight
distance, curvature) as described later. The initial increase may
represent some adjustment of the subjects' behavior in this experiment
(the first segment was a long straight section of low demand). The
occurrence of this increase is particularly surprising given subjects
had completed a practice occlusion block on the square world before the
test block. It may be that the practice condition was too easy with no
fog present, and time was required to adjust to fog. However, that does
not explain why there were initial ramp-ups in demand in the subsequent
subject-controlled and subject-controlled, with-lead-vehicle runs that
occurred after an additional 19 and 38 minutes of driving.

Using the data from this subject as a guide for future studies, the
authors recommend discarding the data for the first 2,500 feet if that
distance includes a mixture of low and high demand situations. If high
demand is not present, then at least the first high demand segment
should be discarded. Furthermore, there may be value in more carefully
reviewing the data from all subjects to assure they all have adequate
time to adapt to the occlusion conditions.

As shown here, the largest difference between sections was about 20%,
but most differences were about 5%. Keep in mind, though, that this is
the data only for 1 subject, and that each segment represented the means
of several data points.

As an example, Figure 2.5 shows the visual demand profile for a
representative subject (Subject 5, 35-year-old female) entering a
400-meter radius right hand curve. In a manner consistent with Tsimhoni
and Green (1999), the visual demand increases sharply as the subject
reaches the point of curvature (approximately 150 m before the curve),
reaches a peak near the beginning of the curve, declines to a more
moderate level, and then returns to a lower level near the curve exit.
Again, there is considerable variability in the curve because of the
small number of data points in the estimate and the fact that estimates
for a single run were rounded to the nearest 5%.

![](media/image7.pct){width="5.5in" height="2.8472222222222223in"}

Figure 2.5. Visual Demand Profile for a Sample Subject (400 m Right
Curve)

> Note: PC = Point of Curvature, where the curve begins
>
> PT = Point of Tangency, where the curve ends

## 2.3.2 ANOVA of Visual Demand

The analysis was based on the mean demands from three runs/subject
(subject controlled, cruise controlled, cruise controlled with lead
vehicle) \* 16 subjects \* 29 segments = 1392 data points. Demand values
ranged from 16.2 to 94.3 with a mean of 61.6 and a standard deviation of
17.7. Figure 2.6 shows the distribution of demand values.

![](media/image8.pct){width="4.069444444444445in" height="3.125in"}

Figure 2.6. Distribution of Visual Demand Values

To provide a sense of the relative impact of all of the experimental
factors as guide further analysis, a series of 1-way ANOVAs was carried
out (Table 2.5). The outcome of this analysis was used to guide the
other ANOVAs computed as other dependent variables had common
independent variables.

For simplicity, all values were treated as nominal. In order of
importance, the factors that had an effect on the demand values were
subject, road geometry, and others in that order.

Table 2.5. Summary of Initial ANOVAs of Visual Demand

+-------------------+------+------+-------+--------------------------+
| Factor            | \#   | P    | La    | Means                    |
|                   | Le   |      | rgest |                          |
|                   | vels |      | Delta |                          |
+-------------------+------+------+-------+--------------------------+
| Section of Road   | 29   | \<.  | 16.8  | 51.6 -- 68.4             |
|                   |      | 0001 |       |                          |
+-------------------+------+------+-------+--------------------------+
| > Radius &        | 5    | \<.  | 7.5   | 200L=64.0,200R=66.7,     |
| > Direction       |      | 0001 |       | 400L=63.3,400R=63.4,     |
| >                 |      |      |       | S=59.2                   |
| > (confounded     |      |      |       |                          |
| > with fog)       |      |      |       |                          |
+-------------------+------+------+-------+--------------------------+
| > Inverse Radius  | 3    | \<.  | 5.9   | Straight (0.0) = 59.2,   |
|                   |      | 0001 |       | .003 (1/400) = 63.3,     |
|                   |      |      |       | .005 (1/400) = 65.2      |
+-------------------+------+------+-------+--------------------------+
| > Straight        | 5    | .02  | 7.6   | 700=60.5, 800=59.8,      |
| > Section Length  |      |      |       | 900=59.4, 1000=60.7,     |
| > (confounded     |      |      |       | 1400=53.1                |
| > with fog),      |      |      |       |                          |
| > straight        |      |      |       |                          |
| > sections only   |      |      |       |                          |
+-------------------+------+------+-------+--------------------------+
| > For or Log      | 3    | \<.  | 6.2   | 2.0 (100) = 65.3, 2.35   |
| > (sight          |      | 0001 |       | (225)=60.9, 2.70 (500) = |
| > distance)       |      |      |       | 59.1                     |
+-------------------+------+------+-------+--------------------------+
| Subject           | 16   | \<.  | 52.8  | 36.3 -- 89.1             |
|                   |      | 0001 |       |                          |
+-------------------+------+------+-------+--------------------------+
| > Sex             | 2    | .02  | 2.3   | Female=62.8, male=60.5   |
+-------------------+------+------+-------+--------------------------+
| > Age             | 2    | \<.  | 7.7   | Middle=57.8, older=65.5  |
|                   |      | 0001 |       |                          |
+-------------------+------+------+-------+--------------------------+
| Cruise & Lead     | 3    | .    | 4.9   | Subject-controlled=59.3, |
| Vehicle           |      | 0001 |       | cruise controlled =61.4, |
|                   |      |      |       | subject controlled +     |
|                   |      |      |       | lead = 64.2              |
|                   |      |      |       |                          |
|                   |      |      |       | Control effect = 2.1     |
|                   |      |      |       |                          |
|                   |      |      |       | Lead vehicle=2.8         |
+-------------------+------+------+-------+--------------------------+
| Braking Trials    | 2    | .09  | 2.4   | No brake=62.7,           |
| (for subject      |      |      |       | brake=65.1               |
| controlled + lead |      |      |       |                          |
| vehicle only)     |      |      |       |                          |
+-------------------+------+------+-------+--------------------------+
| Block             | 3    | .    | 4.0   | 1=60.3, 2=60.5, 3=64.2   |
| (confounded)      |      | 0007 |       |                          |
+-------------------+------+------+-------+--------------------------+

To examine the interactions that might be significant, an ANOVA was
computed with Inverse curve radius, Log (sight distance), Age, Sex,
Subject nested with Age and Sex, and Speed Control-Lead Vehicle (three
levels: subject controlled, cruise controlled, subject controlled with
lead vehicle) as the independent variables. For simplicity, interactions
involving all pairs of factors were included in the model except
subject. Age, Sex, and Speed Control-Lead Vehicle were all discrete
variables. All others were continuous. In that ANOVA, all of the main
effects were highly statistically significant as were the Age \* Sex,
Age \* Speed Control-Lead Vehicle and the Sex \* Speed Control-Lead
Vehicle interactions (all p\<.0001). Also significant was the Age \*
Inverse Radius combination (p=.03). The ANOVA table appears in Appendix
D.

Each of these factors were examined, usually in combination with others,
to provide a greater understanding of the data. Figure 2.7 shows the
mean occlusion ratings for all 16 subjects. The figure clearly indicates
an interaction between age and sex, though the pattern is the opposite
of what is typical, with demand being lowest for middle-aged women and
greatest for older women. There was no age effect on mean occlusion for
male subjects.

![](media/image7.pct){width="3.7083333333333335in"
height="2.3333333333333335in"}

Figure 2.7. Age and Sex vs. Visual Demand

f,F = female subjects; m,M = male subjects

Figure 2.8 shows the relationship between demand and curve
direction/radius. Notice that demand decreases as curvature decreases,
and that in three of the four cases, the demand for left curves was
slightly less than right curves, though the difference was small. It is
uncertain why. In subsequent analyses, differences between left and
right curves were ignored.

![](media/image7.pct){width="3.6805555555555554in"
height="2.6527777777777777in"}

Figure 2.8. Road/Curve Characteristics vs. Visual Demand

L = Left; R = Right; S = Straight; 200, 400 = radii (m)

Figure 2.9 shows the relationship between the log of sight distance and
inverse curve radius. Demand increases as a function of both. However,
the two effects are not completely additive; that is, there is a
suggestion of an interaction (though it was not statistically
significant), with curvature having less of an impact for short sight
distances (100, 225 feet). Apparently for shorter sight distances,
curvature is relatively less important because sight distance is limited
by fog.

![](media/image7.pct){width="3.6666666666666665in"
height="3.0277777777777777in"}

Figure 2.9. Log (Sight Distance) & Inverse Curve Radius vs. Visual
Demand

For straight section length, the means suggest an odd pattern for long
sight distances, with the demand for 1000 feet and 1400 feet being
different from the other distances. However, as shown in Figure 2.10,
not all sight distance \* fog combinations were expressed during the
experiment, so looking at the mean demand average across fog levels can
be misleading.

![](media/image7.pct){width="3.6944444444444446in"
height="2.5694444444444446in"}

Figure 2.10. Sight Distance & Segment Length vs. Visual Demand

As shown in Figure 2.11, the demand when driving using cruise control
was consistently greater than when the subject controlled speed even
though the subject had more to do. It may be that because the subjects
were often driving in fog, they were not able to reduce their speed,
which elevated demand. Also, note that for all trials, providing a lead
vehicle (when the subject controlled the speed) added to workload by
about the same amount as providing the cruise control, and this
increment occurred consistently for all sight distances.

![](media/image7.pct){width="3.6527777777777777in" height="2.375in"}

Figure 2.11. Control Type & Sight Distance vs. Visual Demand

SC=subject-controlled speed, CC=cruise-controlled speed,
SC+LV=subject-controlled speed with lead vehicle

Figure 2.12 shows the effect of the presence of a braking event on
visual demand. For this figure analysis, only trials where a lead
vehicle was present were used, whether a braking event occurred or not.
No occlusion data was included if a lead vehicle was not present. It is
uncertain why the effect was slightly less for the 225 m sight distance
condition.

![](media/image7.pct){width="3.6666666666666665in"
height="2.361111111111111in"}

Figure 2.12. Presence of a Braking Event & Sight Distance vs. Visual
Demand

Given these results and previous research (e.g., Hulse, et al., 1998),
it made sense to include log (sight distance), inverse curve radius,
subject age, subject sex, cruise control on/off, and lead vehicle
present in the model. The lead-vehicle braking factor was not included
in the model. In a real world situation, if a lead vehicle was braking,
the driver should not be performing an in-vehicle task anyway. Block was
not included because it was a confounding factor and would not be
present in a real system.

In that model, the r-squared was fairly low (.152), in part because the
fit was to data from individual trials, not means for conditions across
subjects. The best-fit equation was:

Demand (0 to 1) = 73.65 --9.42 (log (sight distance))

> \+ 1325.90 (1/curve radius)

\+ 15.35 (Age code, 0=middle, 1=old)),

> \+ 5.40 (Sex code, 0=female, 1=male))
>
> -- 15.23 \* Age code \* Sex code
>
> \+ 3.83 (Lead Vehicle code, 0=no lead, 1=lead vehicle).

The effect of the cruise control did not enter the model. Forcing that
term into the model led to the following equation that increases the
r-squared value to only .154.

Demand (0 to 1) = 72.59 -- 9.42 (log (sight distance))

\+ 1325.90 (1/radius)

\+ 15.35 (Age Code)

\+ 5.40 (Sex Code)

-15.23 (Age code) \* (Sex Code)

\+ 4.90 Lead Vehicle Code

\+ 2.13 Cruise Code (1=cruise control)

## 2.3.3 Subjective Ratings of Visual Demand

How does rated visual demand vary as a function road geometry, subject
differences, and how speed is controlled?

Because of time constraints, each of the 29 road segments was rated once
by each of the 16 subjects for the two runs when no lead vehicle was
present, one subject controlled and one cruise controlled, for a total
of 928 ratings. There was no missing data. Those ratings were examined
using ANOVA with main effects of Age (middle and older), Sex (male,
female), Speed Control (subject, cruise), Sight Distance due to fog
(100, 225, or 500 m), Block (1 or 2), and Road Segment
Type/Curve-Direction-Radius. The segment type variable had five levels
(left curve-200 m, left curve-500 m, right curve-200 m, right curve-500
m, straight). For simplicity, only interactions including two factors
were included in the model except for subject effects, where, for
simplicity, interactions were not examined. Sight Distance and Block
were treated as continuous variables. All others were nominal.

All of the main effects were highly statistically significant (p\<.0001)
except for Block and Speed Control. Of the interactions, only the Age \*
Sex and Block \* Speed control were highly significant (p\<.0001),
though Sex \* Curve-Direction-Radius was also significant (p=.02). The
ANOVA table appears in Appendix D.

As shown in Figure 2.13, older subjects had higher ratings than younger
subjects (visual demand was greater). Inter-subject deviation from the
mean rating value for the younger male subjects was much larger than
seen with the younger females. All younger female subjects produced mean
rating values that were closely located to the mean rating value. The
same large deviation seen with the younger male subjects exists for both
the older male and female subject groups.

![](media/image7.pct){width="3.5694444444444446in"
height="2.3472222222222223in"}

Figure 2.13. Age/Sex vs. Mean Rating

In terms of experimental factors, subjects rated the cruise-controlled
condition (4.4) as being slightly less demanding than the
subject-controlled condition (4.6), which makes sense since in the
cruise-controlled condition there was less for subjects to control. As
shown in Figure 2.14, there was a statistically significant interaction
between speed control and block number, with the demand being lower when
the cruise-controlled condition occurred first.

![](media/image7.pct){width="3.7555555555555555in"
height="2.5708333333333333in"}

Figure 2.14. Block Effect on Mean Rating

Given there is less to do when driving with cruise control than when
under driver control, the lower rating for the cruise-controlled
condition in block 1 makes sense. However, there are no explanations for
the reversed relationship seen for block 2, other than some effect
associated with familiarity with the route or task.

Figure 2.15 shows both the overall effects of curvature and the effects
of fog (sight distance), clearly indicating the lack of an interaction.
The inverse relationship between the mean rating values and sight
distance can also be seen in the highest mean ratings with the 100 m
distance for all characteristic types of road section. Similarly, the
500 m sight distance resulted in the lowest mean rating value for all
road section characteristics.

![](media/image7.pct){width="3.6527777777777777in"
height="2.638888888888889in"}

Figure 2.15, Ratings as a Function of Road Section Type and Fog Sight
Distance

L = Left; R = Right; S = Straight; 200, 400 = radii (m)

It was postulated from previous research (Tsimhoni and Green, 1999) that
ratings of demand should be proportional to inverse curve radius and
also proportional to the log of sight distance (Hulse, et al., 1989). As
shown in Figure 2.16, the demand from sight distance and curvature are
additive (there is no interaction) and the decrease was almost linear
for log sight distance. However, the actual rating was slightly greater
than would be predicted by a strictly log model. (The mean ratings were
5.6, 4.3, and 3.7 for 100, 225, and 500 m respectively.) This could
represent a floor effect in the use of ratings.

![](media/image7.pct){width="3.6805555555555554in"
height="3.0972222222222223in"}

Figure 2.16. Relationship between Ratings of Demand, Log Sight Distance,

and Radius of Curvature

The effect of inverse curve radius is shown in Figure 2.17. Notice that
the 1/r relationship holds, but that the relationship departs from
linearity. This may be because the straight sections were presented many
more times than the other two radius conditions, so they became easier
to drive as subjects experienced them more.

![](media/image7.pct){width="3.7222222222222223in"
height="3.0694444444444446in"}

Figure 2.17. Relationship between Curve Radius and Ratings of Demand

After this initial analysis was completed, a second model was created to
consider the effects of road segment length for straight sections (700,
800, 900, 1000, 1400 m) in lieu of a post hoc test, where section length
was treated as a continuous factor. (See Appendix D for the ANOVA
table.) The effect of section length was not significant (p=.75), as
shown in Figure 2.18. Furthermore, there seemed to be no explainable
pattern to the results. Section length did not interact with any of the
other factors examined.

![](media/image7.pct){width="3.7083333333333335in" height="2.625in"}

Figure 2.18. Effect of Section Length on Ratings of Demand

As shown in Figure 2.19, unbalanced sampling of different section
length-sight distance combinations resulted in the apparent differences
due to section length.

![](media/image7.pct){width="3.9583333333333335in"
height="2.6944444444444446in"}

Figure 2.19. Segment Length & Sight Distance vs. Mean Rating

In a stepwise regression analysis of the data, the following equation
resulted:

Rating = 9.078

\+ 204.002 \* Inverse Radius (m)

\- 2.820 \* Log (Sight Distance (m))

+2.293 \* Age Code (0=middle, 1=old)

+1.987 \* Sex Code (0=female, 1=male)

-1.724 \* Age\*Sex Code

For that model, the r-squared value is .361.

## 

## 2.3.4 Response Times to Lead Vehicle Braking

How does braking event response time vary as a function of road geometry
and subject differences?

Figure 2.20 illustrates the average response to a braking event for a
representative subject. Before the initiation of the braking event at
three seconds, the subject is holding the accelerator in a fixed
position, a common occurrence directly before a braking event for most
of the braking trials. After the initiation of the braking event, there
is a delay before the subject recognizes the braking event and responds.
In most cases, the subject quickly lifts their foot, decreasing the
throttle value to a minimal or zero value. After braking, the lead
vehicle again accelerates, leading to the subject again pressing the
accelerator pedal to follow the lead vehicle.

![](media/image7.pct){width="3.6527777777777777in"
height="2.2777777777777777in"}

Figure 2.20. Acceleration Behavior for a Typical Braking Event

Response time measurement began when the lead vehicle's tail lights
illuminated and it simultaneously began to decelerate. Response time
measurement ended when the subject lifted their foot off of the throttle
(accelerator lift off time). Situations where the throttle was released
for other reasons (coasting) were rare.

The throttle signal had a range of 0.0 (not depressed) to 1.0 (fully
depressed), and as with all other vehicle performance channels, was
sampled at 20 Hz. Inspection of the data from multiple subjects revealed
that at points around which braking might have occurred, throttle
position changes in excess of .01 between samples were invariably
associated with overt braking and not normal adjustments in speed;
hence, any change of that magnitude when followed by additional
decreases in throttle position was used to signal throttle release
braking. It should be noted that for \<2.3% of all potential braking
events, there was no throttle change exceeding .01 for a 10-second
response window. In subsequent analyses, these data were treated as
missing responses. Any subsequent increase in the throttle position
beyond 0.00 (as the subject returned to the suggested speed of 45 mi/hr)
started the search for a new braking event.

Another alternative would have been to select when the brake was pressed
(brake response time, when the brake value changed from 0 to not 0).
Unfortunately, for 35% of the trials, subjects responded to the lead
vehicle braking by removing their foot from the throttle but did not
press the brake petal, a situation that lead to excessive missing data.

In addition to ease of measurement, implications of the measures were
also considered. Brake RT is more directly related to crash performance
because it represents when the vehicle velocity changes substantially in
response to driver input. However, there is some slowing when the
throttle is released, and furthermore, because it is not "contaminated"
by the variability of movement from the throttle to the brake, throttle
RT could be more highly correlated with factors influencing driver
response than brake RT. Overall the differences in correlations between
these two measures and other measures of performance should be slight.

## 2.3.5 Mean Response Time Profiles

To provide a sense of the response time data, mean throttle values for a
representative subject for an entire run (trial block) appear in Figure
2.21. There was a fair amount of variability between individual sections
(due to fog, etc.) and there was about a 10% increase in response time
across all trials.

![](media/image7.pct){width="3.388888888888889in"
height="2.5416666666666665in"}

Figure 2.21. Average Accelerator Response Time Over Entire RT Test Run
(Activity 6)

### *2.3.5.1 ANOVA of Throttle Lift Off Response Time*

Only values of response time calculated from accelerator lift of were
examined. There were a total of 576 potential responses (16 subjects \*
18 response/subject/condition \* 2 conditions (fog with occlusion, no
fog with no occlusion)), for which 13 were missing because subjects did
not respond or had already released their foot from the accelerator at
braking event initiation. Of those missing responses, five were from
middle-aged subjects and eight from older subjects. The largest number
of missed responses per subject was five (out of 36) for an older
subject. That subject did not have the longest mean response time.

The factors in the ANOVA of throttle lift off response time were: (1)
Sight Condition (no fog, 500 m, 225 m, 100m), (2) Curve/Direction/Radius
(left 200 m, left 400 m, right 200 m, right 400 m, straight), (3) Age
(middle, old), Sex (female, male), Subject nested within Age and Sex,
and all first order interactions except those with Subjects. Only
Subject was a random effect. The length of straight sections was
addressed separately. Also, Block effects were not examined as order and
condition type (no fog-no occlusion, fog-occlusion) were confounded.

In that analysis, only Sight Condition (basically fog) was highly
significant (p\<.0001). Also significant were Sex (p=.009), Subject
(p=.002), and Age \* Sex (p=.02). The full ANOVA table is in Appendix D.

Distribution of response time values can be seen below in Figure 2.22.

![](media/image9.pct){width="3.861111111111111in"
height="3.0833333333333335in"}

Figure 2.22. Distribution of RT Values

Figure 2.23 shows the subject differences. Consistent with typical
findings, middle-aged men had the shortest response times and older men
had the longest response times.

![](media/image7.pct){width="3.6527777777777777in"
height="2.3194444444444446in"}

Figure 2.23. Response Times by Subject Age and Sex

Figure 2.24 shows the sight condition results. Notice that response
times were clearly elevated for the 100 m sight distance (2.29 s) and
that as sight distance increased, response time decreased, though the
difference between 225 and 500 m was somewhat small (1.58 and 1.37 s
respectively). Interestingly, the response time for the no occlusion-no
fog condition was between those 2 values, at 1.41 s. Given it was easier
(longer sight distance, no occlusion), the time should have been less.
The lack of a difference may reflect statistical error in the
measurements or confounding of factors.

![](media/image7.pct){width="3.8472222222222223in"
height="2.8472222222222223in"}

Figure 2.24. Effect of Sight Condition on Response Time

It was thought that when visual demand increased (as when curve radius
decreased), that response time would increase. As shown in Figure 2.25,
demand due to curvature had no effect on response time, nor did curve
direction when grouping the subjects by age.

![](media/image7.pct){width="3.736111111111111in" height="2.625in"}

Figure 2.25. Response Time as a Function of Curve Direction and Radius

When splitting the data by sight distance, demand due to curvature also
had no effect on response time, as seen in Figure 2.26 below. Curve
direction also did not produce any effect on response time.

![](media/image7.pct){width="3.75in" height="2.638888888888889in"}

Figure 2.26. Response Time as a Function of Road/Curve Characteristics

and Sight Distance

Expected results of the effect of fog on response time were found as
response time was always lower when there was no fog present in the
subjects' view. Figure 2.27 shows that as for all tested curve radii as
well as curve directions, the presence of fog led to an increase in
response time.

![](media/image7.pct){width="3.736111111111111in"
height="2.638888888888889in"}

Figure 2.27. Effect of Fog on Response Time Split by Road/Curve
Characteristics

As with the previous dependent measures, the effect of straight section
length on response time was examined. As shown in Figure 2.28, section
length had no effect. Section length effect on response time was not
intended to be analyzed when this experiment was designed, hence the
number of actual data points for Figure 2.27 are limited. While no major
conclusions could be drawn from this figure, the effect of section
length on mean response time seems to be relatively small.

![](media/image7.pct){width="4.138888888888889in"
height="2.6666666666666665in"}

Figure 2.28. Response Time as a Function of Straight Section Length

###  *2.3.5.2 Prediction of Brake Response Time and Visual Demand*

A goal of this experiment was to examine whether brake response time
could be predicted from other measurements because so few response times
can be collected in a typical experimental session. In order to examine
if this prediction was possible, a correlation table was created between
the independent and dependent measures throughout the experiment. Table
2.6 shows the correlation values between the primary measures of
interest pooled across subjects. Inverse curve radius and log (sight
distance) were used because the research of Hulse had shown they were
better predictors than curve radius and sight distances, findings that
were verified for this data. Given that there were 3 curve radii (none,
200 m, 400 m) and 3 sight distances (100, 225, 500 m), 9 data points
were used in the correlation analysis. Bolded values shown were
significantly correlated (p\<.05). Most interesting is the absence of a
statistically significant correlation with inverse radius with any of
the dependent measures. (The correlation with visual demand was almost
significant (p=.0501).) However, all of the dependent measures were
highly correlated with each other.

Table 2.6. Correlation of Independent and Dependent Measures

  ------------------- ---------- ------------ ----------- ----------- --------
                      1/Radius   Log (Sight   Brake RT    Occlusion   Rating
                                 Distance)                            

  1/Radius            1                                               

  Log (Sight          1.50E-15   1                                    
  Distance)                                                           

  Brake RT            0.027      **-0.913**   1                       

  Occlusion           0.664      **-0.684**   0.703       1           

  Rating              0.424      **-0.881**   **0.862**   **0.928**   1
  ------------------- ---------- ------------ ----------- ----------- --------

Note: Statistically significant differences (p\<.05) are shown in bold.

Figures 2.29, 2.30, and 2.31 show the relationships among the dependent
measures of interest (visual demand as measured by the percentage of
time the forward scene was visible, the brake response time (in s), and
the rating of visual demand on a1 to 10 scale). Notice that for the
relationships with brake RT, there is one rating data point and one
occlusion data point that seem to depart from the correlation. This
departure is due to the unique interaction that occurs at straight
sections with the 100 m sight distance characteristic. The three data
points located on the upper right side of both Figures 2.29 and 2.30 are
representative of the 100 m sight distance characteristic at the 200 m
curve radii, 400 m curve radii, and straight sections, but the outlier
from the correlation line is the point for straight sections. As
subjects experienced the 100 m sight distance for all three radii
sections, they adapted their ratings and occlusions to be higher in
curved sections as compared to straight sections. Therefore, the
straight section data point will be shifted more left on both figures.
At the same time though, as less attention is being paid (i.e. lower
occlusion values) to the world, response time increases, leading to the
longest response time values for any data point in either figure.

![](media/image10.pct){width="4.013194444444444in" height="3.25in"}

Figure 2.29. Relationship between Ratings of Demand and Brake Response
Time

![](media/image11.pct){width="4.263888888888889in"
height="3.1944444444444446in"}

Figure 2.30. Relationship between Visual Demand and Brake Response Time

![](media/image12.pct){width="4.180555555555555in" height="3.25in"}

Figure 2.31. Relationship between Visual Demand Measured Using Occlusion
and Ratings of Demand

Stepwise regression modeling was used to predict the dependent measures,
the most important of which was brake response time. Two sets of
equations were developed to suit the various ways in which the findings
could be applied. In the first approach (Table 2.7, equations 1-3), it
was assumed that the workload manager would have data from the
navigation system and other sensors on curve radius and sight distance.
In addition, it was assumed that some limited information was available
concerning the driver, in particular their age category and sex. A total
of 36 data points were used in the analysis (age (2) \* sex (2) \* sight
distance (3) \* curve radius (3)). As was shown in earlier ANOVAs, age
and sex have significant effects on brake response time and visual
demand.

Table 2.7. Regression Equations Coefficients

  ----- ----- ----------- ----------- ---------- ---------- ------- ------- --------
  \#    R2    Dependent   Intercept   1/Radius   Log (sight Age     Sex     Age Code
              Measure                 (m)        distance   Code    Code    \* Sex
                                                 (m))                       Code

  1     .87   Response    4.58        0          -1.33      0.58    -0.28   0.60
              Time (s)                                                      

  2     .93   Visual      74.99       1248.93    -9.20      14.71   5.47    -15.39
              Demand (%)                                                    

  3     .84   Rating      8.65        174.10     -2.54      2.06    1.87    -1.38

  4     .87   Response    4.83        0          -1.31                      
              Time (s)                                                      

  5     .91   Visual      81.34       +1249.00   -9.20                      
              Demand (%)                                                    

  6     .96   Rating      10.89       191.47     -2.84                      
  ----- ----- ----------- ----------- ---------- ---------- ------- ------- --------

Age code (0=middle, 1=old)

Sex code (0=female, 1=male)

All of the predictions make sense, with all dependent measures
decreasing as radius is increasing, as well as decreasing with greater
sight distance (hence negative log values) and increasing with age. Sex
and Age \* Sex values vary to fit the particular experimental data.

The resulting predictions of brake response time were quite good, with
the three independent variable model (4.58 -- 1.33 Log(Sight Distance) +
0.58 Age Code --0.28 Sex Code + 0.60 Age Code \* Sex Code) accounting
for 87% of the variance. Notice that inverse curve radius did not enter
into the model. Forcing 1/r into the equation (RT = 4.82 --1.31
(Log(Sight Distance (m))) + 5.40(1/r (m)) increased r2 by .001, a small
amount. For visual demand, the 4-factor model accounted for 93% of the
variance, and for ratings of demand, a 4-factor model accounted for 84%
of the variance. These r-squared values are all quite high.

If the Age and Sex data are not included, then equations 4, 5, and 6
result. The r-squared values remain high because the data set consisted
of only nine points (3 curve radii \* 3 sight distances). In both cases,
the r-squared values would be much lower if the raw data or subject
means for various conditions were used. For those interested in
computing other regression models using other combinations of
independent measures, the 27 data point means are in Appendix E.

# 2.4 CONCLUSIONS

In the experiment, 16 subjects (8 middle-aged, 8 older) drove 2-lane
rural roads in a driving simulator, sometimes following a lead vehicle
that braked for which response time was measured. The road, consisting
of straight and curved sections (of varying radii), was at times
obscured by varying levels of fog. The visual demand of the driving task
for each road segment was measured using the visual occlusion method and
by ratings.

The overall goal of this experiment described in this report was to
relate response time to a lead vehicle braking to the visual demand of
the driving situation under which that data was collected. More
specifically, four questions were addressed, each of which is covered in
separate sections that follow.

## 2.4.1. How does braking event response time vary as a function of road geometry and subject differences?

Response time to a braking event was longer when the stimulus occurred
on a curve as opposed to a straight section when sight distance was
unlimited (meaning there was no fog). Average response times were 1.51 s
on curves and 1.24 s on straight sections. When sight distance was
limited (averaged over all tested distances), the difference between
curved and straight sections was less than 0.05 s (1.76 s for curved;
1.73 s for straight). Curve radius had very little effect on response
time to a lead vehicle braking with the mean response times for the 200
m and 400 m curve both being 1.63 s.

As expected, the direction of the curvature did not affect response time
to a braking event, with the difference in response time between the
left and the right curves being 0.02 s (1.62 s for left; 1.64 s for
right).

Response time to a lead vehicle braking was inversely proportional to
sight distance and was reasonably well correlated with the log of sight
distance. Response time at a limited sight distance of 100 m was found
to be approximately 2.29 s with 225 m and 500 m sight distances,
resulting in response times of 1.58 and 1.37 s, respectively.

As expected, older subjects had longer response times to braking events
than younger subjects, regardless of sex. When sight distance was not
limited, older subjects' response time was 0.55 s longer than that of
the middle-aged subjects (1.70 s to 1.55 s), but when sight distance was
limited by varying levels of fog, the difference increased to 0.88 s
(2.19 s to 1.31 s). Older men and women had very similar response times
(difference \<=0.02) when sight distance was not limited, but a
difference of 0.31 s when fog was present (1.71 s to 1.69 s and 2.34 s
to 2.03 s, males over females respectively). Middle-aged males had a
lower response time than the middle-aged females in both situations,
with no limited sight distance (1.04 s to 1.26 s) and limited sight
distance (1.17 s to 1.44 s).

Based on these analyses, the best prediction of response time is:

Response time (s) = 4.58 --1.33 log (sight distance (m)

+.58\*Age Code --0.28 Sex Code +.60 \* Age Code \* Sex Code

where the Age Code is 0 for middle aged subjects and 1 for older
subjects, and the Sex Code is 0 for women and 1 for men. Notice that
curvature was not part of the model.

## 2.4.2. How does visual demand (measured using the visual occlusion method) vary as a function of road geometry, presence of a lead vehicle, subject differences, and how speed is controlled?

Visual demand, as measured using the visual occlusion method, was
roughly proportional to inverse curve radius as indicated by the
literature; that is, it was more demanding to drive tighter curves. The
demand on straight sections (infinite curvature) was approximately 59%.
The demands for 200 m and 400 m were found to have demand levels of 65%
and 63%, respectively.

There was also a noticeable difference in the visual demand as a
function of the direction of curvature. When experiencing a left curve,
the average visual demand was 64%, but the demand increased 1% for right
curves.

As expected, visual demand increased as the sight distance decreased.
When the sight distance was 100 m, the visual demand was approximately
65%. However, demand increased for the 225 m and 500 m distances at 61%
and 59%, respectively. The rate of change of this relationship at the
500 m sight distance approaches zero, suggesting that 500 m is
approximately equivalent to the absence of fog or unlimited sight
distance. Further tests with greater sight distances should be conducted
to verify this conclusion.

Visual demand increased almost 3% when a lead vehicle was present,
rising from 60% to 63 %. This project did not separate the demand due to
the mere presence of a vehicle from the demand associated with
responding to a braking event.

Visual demand was higher when vehicle velocity was cruise controlled
than when subject controlled, which was an unexpected finding. It was
hypothesized that when subjects were in charge of their velocity, as
well as occlusion and steering, the level of visual demand would be
higher than with cruise control. Subject-controlled velocity lead to a
visual demand level of 59%, but cruise-controlled velocity produced a
visual demand of 61%.

Measured visual demand depended on the age and sex of the driver to a
significant degree. For women, the visual demand increased substantially
with age (55% for middle aged women, 70% for older women). For the men,
the age difference was .1%, with both values between 60 and 61%. Thus,
there was a substantial age by sex interaction. The size of these
differences was generally larger than some of the effect differences,
suggesting that a workload manager may need to be tailored to particular
age-sex groups.

Based on these analyses, the best prediction of visual demand is:

Visual demand (%) = 74.99 + 1248.93\*(1/Radius(m)) -- 9.20\*Log(Sight
distance(m))

+14.71\*Age Code +5.47 Sex Code --15.39\*Age Code\*Sex Code.

## 2.4.3. How does rated visual demand vary as a function of road geometry, subject differences, and how speed is controlled?

Visual demand, as measured in ratings, increased as the radius of
curvature decreased, again appearing to be related to inverse curve
radius. For straight sections (infinite radius of curvature), the mean
rating was 4.1, but it increased to 4.9 for 400 m curves and 5.0 for 200
m curves. As a reminder, the rating "4" was presented as "driving 10
miles an hour faster than traffic on an expressway," while a rating of
"6" was "driving 20 miles an hour faster than traffic on an expressway".

There was also a noticeable difference in the visual demand as a
function of the direction of curvature. For both left and right curves,
the mean visual demand rating was approximately 4.9. Straight sections
had a rating of only 4.1.

Visual demand as assessed by subjective ratings decreased as the sight
distance increased. Ratings were 5.6 for 100 m, 4.3 for 225 m, and 3.68
for 500 m.

Age and sex had an interesting effect on visual demand as determined by
subjective ratings. For the middle-aged subjects, men had higher
subjective ratings than the women: 4.8 to 2.8. The increase of 2.0
points is quite surprising. As expected, the mean ratings for the older
subjects were higher than that of the middle-aged subjects.
Interestingly, the older male and female subjects had similar mean
ratings (5.3 and 5.1, respectively).

Ratings for cruise-controlled velocity were lower than that of
subject-controlled velocity, as expected. Having velocity under cruise
control produced an average rating of 4.4 with subject control equaling
4.6, a 4 % increase.

Based on these analyses, the best prediction of subjective ratings is:

Rating (1 to 10) = 8.65 + 174.10\*(1/Radius(m)) --2.54\*Log(Sight
distance(m))

+2.06\*Age Code +1.87\*Sex Code --1.38\*Age Code\*Sex Code.

## 2.4.4. How closely does visual demand (measured by the visual occlusion method) relate to rated visual demand in terms of road geometry, subject differences, and how speed is controlled?

Overall, the correlation of the two means (average across subjects) was
0.928, quite high. More specifically, the percentage of visual demand
increasing from 400 m to 200 m radius of curvature for the rating system
closely resembles the relationship seen in the visual occlusion method:
an increase of 2.0% for the ratings versus 2.9% for the visual occlusion
method. When examining the difference in terms of direction of
curvature, the ratings increased by 1.2% for right curves over left
curves. The visual occlusion method indicated a 1.4% increase for right
over left curves. Differences in the effect of sight distance were noted
between the two methods. The visual occlusion method had a 6.6% decrease
from 100 m to 225 m sight distances and a 3.0% decrease from 225 m to
500 m, while the decreases for the subjective ratings were much greater
at 22.8% and 14.8%, respectively. The age/sex interaction for both
methods did not produce similar results, except for the average upward
trend when looking from middle-aged to older subjects. Results
pertaining to means of control were opposite across the two visual
demand methods. Use of cruise control led to a greater visual demand
than when the subject controlled speed, as assessed by the visual
occlusion method, but the opposite held true for the subjective ratings.

## 2.4.5 Closing Thoughts

The overall goal of establishing a quantitative relationship between
response time to a lead vehicle braking and visual demand measured using
the visual occlusion method was achieved. Visual demand was well
correlated with sight distance and response time, but surprising, curve
radius was not (as was expected from the literature).

Nonetheless, the use of visual occlusion as a predictor for brake
response time deserves further investigation for other factors that
affect driving demand (such as traffic) as it continues to be a more
efficient experimental method than collecting brake response time
directly.

# 2.5 REFERENCES

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# 2.6 APPENDIX A -- RATIONALE FOR CURVE-SIGHT DISTANCE COMBINATIONS

**Experiment Design**

Table 2.8 shows the curve radius -- direction combinations explored for
the road driven in the "forward" direction. Curves on which braking
events occur are shown in bold, with only two curves having no events in
order to make event occurrence less predictable. This design allows the
effect of visual demand (curvature) on braking response time (RT) to be
explored efficiently.

Table 2.8. Curve Radius -- Direction Combinations Explored

(Curves with braking events in bold)

+------------+-------------------+-------------------+----------------+
|            | 200 m curve       | 400 m curve       | Total          |
+------------+-------------------+-------------------+----------------+
| Left       | **100,**          | **100**           | 7 curves       |
|            |                   |                   |                |
|            | **225,**          | **225,** 225      |                |
|            |                   |                   |                |
|            | **500,** 500      | **500**           |                |
+------------+-------------------+-------------------+----------------+
| Right      | **100,**          | **100,**          | 7 curves       |
|            |                   |                   |                |
|            | **225,**          | **225,**          |                |
|            |                   |                   |                |
|            | **500**           | **500**           |                |
+------------+-------------------+-------------------+----------------+
| Total      | 7 curves          | 7 curves          |                |
+------------+-------------------+-------------------+----------------+

Table 2.9 shows the sight distances (due to fog) used for various
straight section lengths. Examination of the interaction of curvature
and sight distance was beyond the scope of this experiment. Notice that
braking events only occurred on longer straight sections. At the
beginning of each straight section, subjects did not know its length, so
having braking events on only long straights was not a confounding
factor. All braking events during straight sections occurred at the
middle of the section length.

Table 2.9. Straight Section Length -- Sight Distance Combinations
Explored

(Cell entry is the number of sections, braking events are in bold)

+--------------+-------------+-------------+------------+--------------+
| Sight        | Section     |             |            |              |
| Distance     | Length      |             |            |              |
|              |             |             |            |              |
| \(m\)        |             |             |            |              |
+--------------+-------------+-------------+------------+--------------+
|              | (no lead    |             | (lead      |              |
|              | vehicle     |             | vehicle    |              |
|              | braking)    |             | braking)   |              |
+--------------+-------------+-------------+------------+--------------+
|              | 700         | 800         | 900        | 1000         |
+--------------+-------------+-------------+------------+--------------+
| 100          | 2           | 1           | **1**      | **1**        |
+--------------+-------------+-------------+------------+--------------+
| 225          | 3           |             | **1**      | **1**        |
+--------------+-------------+-------------+------------+--------------+
| 500          | 1           |             | **1**      | **1**        |
+--------------+-------------+-------------+------------+--------------+

There were 3 sight distance limits examined in this experiment. The
longest distance, 500 m, represents the longest distance for which some
fog was present but which may not affect visual demand (Hulse, Dingus,
Fisher, Wierwille, 1989). The shortest distance is based on the shortest
stopping distance required for a vehicle when the deceleration value is
at its maximum. For 45 mi/hr, 1% of the drivers would achieve 0.21 g
(and 99% would be lower) (Kiefer, LeBlanc, Palmer, Salinger, Deering,
and Shulman, 1999). leading to a stopping distance of 100 m. Since the
relationship between visual demand and sight distance is logarithmic,
225 m is a reasonable middle value.

For curves with braking events, the events occurred either approximately
90 m from the beginning or end of the curve. Braking event occurrence
was balanced across curve direction and radius.

# 2.7 APPENDIX B -- ACTIVITY SEQUENCE RANDOMIZATION 

# 

Table 2.10. Order Sequence for Activities 5-7

+----------+-------------------+------------------+-------------------+
| *        | ######## Order    |                  |                   |
| *Subject |                   |                  |                   |
| \#**     |                   |                  |                   |
+----------+-------------------+------------------+-------------------+
|          | **1^st^ (Activity | **2^nd^          | **3^rd^ (Activity |
|          | #)**              | (Activity #)**   | #)**              |
+----------+-------------------+------------------+-------------------+
| 1        | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 2        | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 3        | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+
| 4        | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+
| 5        | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 6        | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 7        | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+
| 8        | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+
| 9        | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 10       | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 11       | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+
| 12       | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+
| 13       | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 14       | Subject           | Response Time    | Cruise Controlled |
|          | Controlled (7)    | (6)              | (5)               |
+----------+-------------------+------------------+-------------------+
| 15       | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+
| 16       | Cruise Controlled | Response Time    | Subject           |
|          | (5)               | (6)              | Controlled (7)    |
+----------+-------------------+------------------+-------------------+

# 2.7 APPENDIX C -- SIMULATOR INFORMATION

# 

![](media/image13.pct){width="6.5in" height="8.196527777777778in"}

Figure 2.32. Buck Layout and Equipment

![](media/image14.pct){width="5.8069444444444445in" height="5.69375in"}

Figure 2.33. Overall Simulator Room Layout

# 2.7 APPENDIX D -- ADDITIONAL ANOVA ANALYSIS

Table 2.11. Visual Demand ANOVA (from Occlusion Data)

  --------------------------------------------------------------------------
  **Source**                    **DF**   **SS**       **F Ratio**  **P**
  ----------------------------- -------- ------------ ------------ ---------
  Inverse radius                1        10222.660    53.0389      \<.0001

  Log (sight distance)          1        9826.758     50.9848      \<.0001

  Sex                           1        13399.579    69.5219      \<.0001

  Age                           1        54380.932    282.1480     \<.0001

  SpeedControl-LV               2        5592.707     14.5085      \<.0001

  Subject\[Sex,Age\]            4        99747.039    129.3810     \<.0001

  Inverse radius\*Sex           1        0.330        0.0017       0.9670

  Inverse radius\*Age           1        940.855      4.8815       0.0273

  Inverse                       2        574.597      1.4906       0.2256
  radius\*SpeedControl-LV                                          

  Inverse radius\*log (sight    1        141.414      0.7337       0.3918
  distance)                                                        

  Log (sight distance)\*Sex     1        0.095        0.0005       0.9823

  Log (sight distance)\*Age     1        488.841      2.5363       0.1115

  Log (sight distance)\*        2        33.128       0.0859       0.9177
  SpeedControl-LV                                                  

  Sex\*Age                      1        4334.549     22.4892      \<.0001

  Sex\* SpeedControl-LV         2        3632.451     9.4232       \<.0001

  Age\* SpeedControl-LV         2        283.846      0.7363       0.4790

  Error                         1367     263474.26                 
  --------------------------------------------------------------------------

Table 2.12. Ratings of Demand ANOVA

# 

  ---------------------------------------------------------------------------
  **Source**                **DF**   **Sum of          **F Ratio** **P**
                                     Squares**                     
  ------------------------- -------- ----------------- ----------- ----------
  CurveDirexRadius          4        168.30201         16.3709     \<.0001

  Log Sight Distance        1        407.60224         158.5913    \<.0001

  Sex                       1        232.87833         90.6091     \<.0001

  Age                       1        463.39719         180.3002    \<.0001

  Block                     1        1.32993           0.5175      0.4721

  SpeedControl              1        7.71722           3.0026      0.0835

  Subject\[Sex,Age\]        4        525.26724         51.0932     \<.0001

  CurveDirexRadius\*Sex     4        30.40392          2.9574      0.0192

  CurveDirexRadius\*Age     4        2.97708           0.2896      0.8848

  CurveDirexRadius\*Block   4        7.07207           0.6879      0.6004

  CurveDirexRadius\*        4        2.59042           0.2520      0.9085
  SpeedControl                                                     

  CurveDirexRadius\*Log     4        6.79052           0.6605      0.6196
  Sight Distance                                                   

  Log Sight Distance\*Sex   1        0.76867           0.2991      0.5846

  Log Sight Distance\*Age   1        5.05806           1.9680      0.1610

  Log Sight Distance\*Block 1        0.20376           0.0793      0.7783

  Log Sight Distance\*      1        0.48391           0.1883      0.6645
  SpeedControl                                                     

  Sex\*Age                  1        42.87156          16.6806     \<.0001

  Sex\*Block                1        0.72845           0.2834      0.5946

  Sex\* SpeedControl        1        3.37931           1.3148      0.2518

  Age\*Block                1        3.37931           1.3148      0.2518

  Age\* SpeedControl        1        9.12069           3.5487      0.0599

  Block\* SpeedControl      1        172.24914         67.0193     \<.0001

  Error                     884      2272.0064                     
  ---------------------------------------------------------------------------

Table 2.13. Response Time ANOVA

  ---------------------------------------------------------------------------
  **Source**                    **DF**   **SS**        **F Ratio**  **P**
  ----------------------------- -------- ------------- ------------ ---------
  Sight Condition               3        44.697125     22.9427      \<.0001

  CurveDirexRadius              4        1.394686      0.5369       0.7087

  Sex                           1        4.428647      6.8196       0.0093

  Age                           1        2.019417      3.1097       0.0784

  Subject\[Sex,Age\]            4        11.068152     4.2609       0.0021

  Sight Distance\*Sex           3        0.674149      0.3460       0.7920

  Sight Distance\*Age           3        4.437385      2.2777       0.0787

  Sight Distance                12       12.453102     1.5980       0.0882
  \*CurveDirexRadius                                                

  CurveDirexRadius\*Sex         4        1.083963      0.4173       0.7962

  CurveDirexRadius\*Age         4        1.014777      0.3907       0.8154

  Sex\*Age                      1        3.717758      5.7249       0.0171

  Error                         522      338.98750                  
  ---------------------------------------------------------------------------

# 

# 2.8 APPENDIX E - ADDITIONAL INFORMATION ABOUT REGRESSSION

![](media/image15.pct){width="3.9583333333333335in"
height="3.0833333333333335in"}

Figure 2.34. Visual Demand (%) versus Brake Response Time (s)

![](media/image16.pct){width="3.9027777777777777in"
height="3.0833333333333335in"}

Figure 2.35. Ratings of Demand versus Brake Response Time (s)

Table 2.13. Means Used for Regression Analysis

+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | Sight  | Radius  | Brake RT  | Occlusion | Rating    |
|  Age |  Sex | Di     |         |           |           |           |
|      |      | stance |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 100    | 200     | 1.921     | 65.426    | 4.250     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 100    | 400     | 2.043     | 62.602    | 4.250     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 225    | 200     | 1.250     | 59.975    | 2.333     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 225    | 400     | 1.356     | 54.506    | 2.833     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 500    | 200     | 1.169     | 56.282    | 2.333     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 500    | 400     | 1.081     | 53.023    | 4.406     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 100    | s       | 2.069     | 56.185    | 3.425     |
| ddle | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 225    | s       | 1.238     | 53.154    | 2.300     |
| ddle | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | > fe | 500    | s       | 0.969     | 47.252    | 1.825     |
| ddle | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 100    | 200     | 1.825     | 69.218    | 6.250     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 100    | 400     | 1.588     | 66.868    | 5.875     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 225    | 200     | 0.906     | 63.073    | 5.375     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 225    | 400     | 1.006     | 62.440    | 4.958     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 500    | 200     | 0.994     | 63.234    | 4.750     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 500    | 400     | 1.044     | 61.802    | 4.562     |
| ddle | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 100    | s       | 1.894     | 61.254    | 5.475     |
| ddle | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 225    | s       | 0.713     | 56.770    | 4.050     |
| ddle | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| > mi | >    | 500    | s       | 0.594     | 52.987    | 3.500     |
| ddle | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 100    | 200     | 2.231     | 75.593    | 6.375     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 100    | 400     | 2.407     | 74.063    | 6.219     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 225    | 200     | 2.094     | 72.628    | 4.875     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 225    | 400     | 1.694     | 70.236    | 5.292     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 500    | 200     | 1.775     | 71.860    | 4.542     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 500    | 400     | 1.600     | 70.277    | 4.406     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 100    | s       | 2.863     | 71.111    | 6.175     |
|  old | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 225    | s       | 1.988     | 68.366    | 4.500     |
|  old | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | > fe | 500    | s       | 1.631     | 66.669    | 4.125     |
|  old | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 100    | 200     | 2.675     | 66.046    | 7.750     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 100    | 400     | 2.893     | 67.080    | 7.469     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 225    | 200     | 2.031     | 58.889    | 5.875     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 225    | 400     | 2.744     | 60.662    | 5.917     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 500    | 200     | 2.244     | 63.222    | 5.125     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 500    | 400     | 1.613     | 58.912    | 4.750     |
|  old | male |        |         |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 100    | s       | 3.207     | 62.613    | 5.812     |
|  old | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 225    | s       | 1.981     | 58.680    | 4.500     |
|  old | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+
| >    | >    | 500    | s       | 1.763     | 55.469    | 3.700     |
|  old | male |        | traight |           |           |           |
+------+------+--------+---------+-----------+-----------+-----------+

[^1]: The distinction between driving and non-driving tasks may become
    blurred sometimes. For example, reading street signs and numbers is
    necessary for determining the correct course of driving, but may
    momentarily divert visual attention away from the forward road and
    degrade a driver\'s responses to unpredictable danger evolving in
    the driving path. In the SAVE-IT program, any off-road glances,
    including those for reading street signs, will be assessed in terms
    of visual distraction and the information about distraction will be
    fed into adaptive safety warning countermeasures and distraction
    mitigation sub-systems.