**SAfety VEhicle(s) using adaptive Interface Technology (SAVE-IT)
Program**

**DTRS57-02-R-20003**

**Public Release**

**A Proposal Submitted to**

**U.S. Department of Transportation**

**RSPA/Volpe National Transportation Systems Center**

**Attn: Kathleen Regan, DTS-853**

**55 Broadway, Kendall Square**

**Cambridge, MA 02142**

**25 March, 2003**

Correspondence and questions about the proposal may be directed to:

Gerald J. Witt

Program Manager

Email: <Gerald.J.Witt@delphiauto.com>

Phone: 765-451-7048

Fax: 765-451-1340

Delphi Delco Electronics Systems

One Corporate Center

M/S E110

P.O. Box 9005

Kokomo, Indiana 46904-9005

USA

Table of Contents

Executive Summary [1](#executive-summary)

Section 1: Program Description [3](#section-1-program-description)

1.1 Introduction [3](#introduction)

1.2 Objectives and Goals [4](#objectives-and-goals)

1.3 Benefits [4](#benefits)

1.4 Program Plan Summary [5](#program-plan-summary)

Section 2: Technical Approach [6](#section-2-technical-approach)

2.1 System Development [6](#system-development)

**2.1.1 SAVE-IT System Concept** [6](#save-it-system-concept)

**2.1.2 Sensor Arrays** [9](#sensor-arrays)

**2.1.3 Workload/Distraction Management (Data Fusion)**
[11](#workloaddistraction-management-data-fusion)

**2.1.4 Adaptive Interface** [11](#adaptive-interface)

2.2 The Philosophy of Human Factors Research
[12](#the-philosophy-of-human-factors-research)

**2.2.1 The Arousal Approach** [12](#the-arousal-approach)

**2.2.2 The Demand Approach** [12](#the-demand-approach)

**2.2.3 The SAVE-IT Program Approach**
[12](#the-save-it-program-approach)

**2.2.4 SAVE-IT Task Structure and Methodology**
[13](#save-it-task-structure-and-methodology)

**2.3. Summary of Tasks** [19](#summary-of-tasks)

Section 3: Management Plan [20](#section-3-management-plan)

3.1 Program Team Structure [20](#program-team-structure)

Appendices [21](#appendices)

Appendix A: References [22](#appendix-a-references)

Appendix B: List of Figures [23](#appendix-b-list-of-figures)

Appendix C: List of Tables [24](#appendix-c-list-of-tables)

Appendix D: Acronyms Glossary [25](#appendix-d-acronyms-glossary)

#  {#section .unnumbered}

# Executive Summary

In order to support safe vehicular control, a substantial portion of the
driver's attention must be focused on driving-related tasks, such as
interacting with other vehicles, pedestrians, obstacles, and weather
conditions. Additionally, many tasks that are unrelated to operating the
vehicle may consume the driver's attention, for example, talking with a
passenger, thinking about work, or conversing on a cellular phone. All
of these activities require a portion of the driver's attention,
contributing to the total workload imposed on the driver. When the
driver allocates attention to non-driving tasks, attention is diverted
away from the driving tasks that ensure safety. If an unexpected event
occurs in the environment, a distracted driver may be ill-equipped to
react appropriately.

Near term solutions to this problem have focused on improving designs to
minimize driver head-down and hands-off-wheel time. In addition, human
factors principles have been applied to map vehicle interfaces to more
closely match user expectations, thereby reducing the cognitive load on
the driver. Safety enhancement systems such as Forward Collision Warning
(FCW) and Adaptive Cruise Control (ACC) assess direct and immediate
threats and can warn the driver to take appropriate action. As the
technologies continue to evolve, it is anticipated that an increasing
number of safety-enhancing technologies will be integrated into the
vehicle. Although substantial improvements continue to be made within
specific interface components, the system interface as a whole may
become increasingly complex as it integrates a wider range of
sub-components. To counteract this expansion in complexity, the
human-machine interface must evolve into one that provides synergistic
consideration of all safety aspects of the driving experience. In
essence, the driver must become a key component in a closed-loop vehicle
environment. The longer-term vision drives safety significantly further
by developing synergies between active and passive safety, mobile
multimedia, and the inclusion of a revolutionary driver state monitor.
The new system will not only measure the vehicle environment, but also
the biological element within the vehicle-environment system, supporting
an unprecedented level of human-system integration.

This program will serve several important objectives. Perhaps the most
important objective will be demonstrating a viable proof of concept that
is capable of reducing distraction-related crashes and enhancing the
effectiveness of collision avoidance systems. By working with such a
large portion of the automotive sector, this program will provide the
foundational building blocks for a system to be implemented in a uniform
manner across all adopters. This will provide a pathway for consistent
performance from vehicle to vehicle. Program success will be contingent
on integrated closed-loop principles that, not only include
sophisticated telematics, mobile office, entertainment and collision
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.

A comprehensive program team has been established to demonstrate and
evaluate integrated solutions for SAfety VEhicles using adaptive
Interface Technologies (SAVE-IT). The team is pleased to submit this
proposal in order to advance the science of automotive Human Machine
Interface (HMI) Technology. Based on a wealth of prior HMI and
system-integration experience, Delphi Delco Electronic Systems (DDE), a
world-class tier-one automotive supplier, will lead a team composed of
two of the most highly acclaimed universities in the area of automotive
human factors, the two largest automotive manufacturers in the world,
and the leading developer of stereo-vision eye-tracking systems. The
University of Iowa will provide a recognized level of expertise in the
areas of cognitive distraction, standards and guidelines development,
and driving simulator research. The University of Michigan
Transportation Research Institute (UMTRI) will contribute extensive
multidisciplinary automotive experience in the areas of driver
performance, driving task demand, crash-statistics analysis, vehicle
system evaluation, and field operational testing. These two renowned
automotive human factors institutions will draw upon a partnership that
will bring an unsurpassed level of human factors innovation to this
program. General Motors (GM) and Ford Motor Company will provide an
industry perspective to the evaluation of such a ground-breaking system.
Seeing Machines Inc. will continue their revolutionary development of a
robust non-invasive eye-tracking system that will incorporate
significant advancements beyond the version that is presently on the
market for human-factors research. Over the past decade, DDE has
developed advanced safety warning systems including the associated
sensor suites, detection, recognition, and threat assessment algorithms
and human-machine interfaces. The DDE Integrated Safety Systems (ISS)
development initiative was established to elevate safety systems from
those that we understand today, to holistic systems that include driver
state monitoring. Successful in-car demonstration of the ISS concept was
first performed in September of 2000 at the Paris Auto Show. The system
consisted of a safety warning system that provided 360 degrees of
environmental coverage and a driver workload manager which included
adaptive safety and distraction mitigation countermeasures. The next
generation of ISS concept vehicle was demonstrated in September 2001 at
the Frankfurt Auto Show. This is a road worthy vehicle, incorporating
the latest developments in the areas of driver state monitoring,
associated sensor array, and distraction mitigation technology in
addition to safety warning systems. An important feature of this concept
vehicle is a new technology for face and eye gaze tracking, pioneered by
Seeing Machines Inc.. Successful development and evaluation of the
SAVE-IT system will highly leverage the foundational work provided by
Delphi Delco Electronics Systems. Within a market driven business
environment, this team brings together a unique blend of expertise and
complementary capabilities to ensure superb technical solutions that are
grounded in sound research, development, and engineering practices.

This proposal presents a multifaceted three-year program, designed to
develop, demonstrate, and evaluate a system which embodies the SAVE-IT
requirements. The proposed program budget is approximately \$6 million.
The program is partially supported by the Government through a cost
sharing plan. The cost sharing will be an approximate 50/50 split ratio
with the Government providing \$3 million. In order to produce the
ground-breaking human factors research that is required to fulfill the
SAVE-IT objectives, the majority of Government funds will be allocated
to the academic institutions. Two development phases are proposed. Phase
I will consist of human factors research to determine diagnostic
measures of distraction and workload, architecture concept development,
technology development and evaluation, vehicle demonstration, and Phase
II planning. Phase II will focus on algorithm and guideline development,
data fusion, integrated countermeasure development, vehicle
demonstration, and evaluation of benefit.

A comprehensive SAVE-IT research program, as defined within this
proposal, has never been undertaken in the United States or
internationally. This program will break new ground in providing a
distraction and safety mitigation system solution that integrates driver
state. This research and development program will provide an ideal
opportunity for the Government, industry, and academic community to gain
a thorough understanding of the requirements, functions, and social
impact of such technology. Additionally, any potential adverse
operational and safety-related issues could be addressed while the
technology is in the early stages of development. Therefore, this
program will complement and extend the activities of other relevant
Government sponsored programs and provide the opportunity to make a
positive contribution to automotive safety.

# Section 1: Program Description

## 1.1 Introduction

It is commonly believed that driver distraction is a substantial
attributing factor to automobile crashes. Estimates of the driver
distraction effect have varied widely across crash-statistics studies
and many researchers are skeptical about the accuracy of police reports
with respect to causation. Although driver distraction may not be the
direct cause of an accident, it may interact with many other factors,
such as traffic behavior and weather. Because of these complications, it
is difficult to estimate the size of the problem precisely. A
frequently-cited National Highway Transportation Safety Administration
(NHTSA) report estimated that approximately 20-30% of automotive crashes
are directly attributed to driver distraction and inattention (Wang,
Knipling, & Goodman, 1996). The estimated crash rate attributed to
driver distraction and inattention is higher for scenarios such as
rear-end collisions and intersection incursions. The recent adoption of
safety-impacting telematics devices such as wireless phones, internet
applications, information and entertainment systems, and navigation
systems in the automobiles may further increase the distraction
potential and thereby the crash risk. Safety-enhancing systems, such as
collision avoidance systems, could potentially reduce crashes, but may
also demand driver attention. The SAVE-IT program is proposed to
mitigate this situation and reduce distraction-related crashes.

This program will serve several important objectives. Perhaps the most
important objective will be demonstrating a proof of concept that is
practical, given the current level of technology, and capable of
reducing distraction-related crashes and enhancing the effectiveness of
collision avoidance systems. By working with such a large portion of the
automotive sector, this program will provide the foundational building
blocks for a system to be implemented in a uniform manner across all
adopters. This will provide a pathway for consistent performance from
vehicle to vehicle. Program success will be contingent on integrated
closed-loop principles that, not only include sophisticated telematics,
mobile office, entertainment and collision warning systems, but
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.

A comprehensive program team has formed to demonstrate and evaluate
integrated solutions for SAfety VEhicles using adaptive Interface
Technologies (SAVE-IT). The team is pleased to submit this proposal in
order to advance the science of automotive Human Machine Interface (HMI)
Technology. Based on a wealth of prior HMI and system-integration
experience, Delphi Delco Electronic Systems (DDE), a world-class
tier-one automotive supplier, will lead a team composed of two of the
most highly acclaimed universities in the area of automotive human
factors, the two largest automotive manufacturers in the world, and the
leading developer of stereo-vision eye-tracking systems. The University
of Iowa and University of Michigan Transportation Research Institute
(UMTRI) will provide a level of automotive human factors expertise that
cannot be matched. General Motors (GM) and Ford Motor Company will
provide an industry perspective for such a ground-breaking system.
Seeing Machines Inc. will continue their revolutionary development of a
robust non-invasive eye-tracking system that will incorporate
significant advancements beyond the version that is presently on the
market for human-factors research. Within a market driven business
environment, this team brings together a unique blend of expertise and
complimentary capabilities to ensure superb technical solutions that are
grounded in sound research, development, and engineering practices.

A multifaceted three year program is proposed to develop, demonstrate,
and evaluate a driver support system which embodies the SAVE-IT
requirements. Two development phases are proposed. Phase I will consist
of human factors research to determine diagnostic measures of
distraction and workload, architecture concept development, technology
development, vehicle demonstration and Phase II goal planning. Phase II
will focus on algorithm development and validation, data fusion,
integrated countermeasure development, vehicle demonstration and
evaluation of benefit.

The team views the SAVE-IT program as the first stage in adaptive
interface research. Due to the limitations of funding and time, the
SAVE-IT system that is proposed here will provide a scaleable framework
from which further research and development should be considered. This
program will not only identify technologies that will be appropriate for
immediate implementation in production programs, but will also identify
technologies that have potential for implementation in a longer time
frame. Consideration should be given to subsequent developmental stages
to identify the potential scalability of the system and the associated
partitioning that provides measured safety benefit over the range of
vehicle applications. Follow-on research should consider near-term
application of distraction mitigation that are available to most
vehicles without safety warning systems, up through the system evaluated
for the SAVE-IT program. Additional on-road testing in the form of a
Field Operational Test (FOT) should be considered to engage a larger
portion of the population, further expose the system to the driving
environment, and refine application guidelines, however, this is beyond
the scope of this program.

The interaction between the Government and program team in the
management of the activities are defined within the program management
section. DDE will lead the overall development effort, utilizing a
comprehensive team approach which will heavily leverage the respective
leadership successes in the areas of vehicle systems, integration,
sensor development, human factors research, and driver safety management
principles. Communication within the team is of utmost importance and
shall be facilitated by email, voice mail, conference calls, and regular
face-to-face team meetings.

## 1.2 Objectives and Goals

The mission of this program is to demonstrate the concept of a
comprehensive SAVE-IT system that provides benefit in reducing
distraction related crashes and enhances the effectiveness of collision
avoidance systems. The primary objectives of this program are:

(1) Advance the deployment of adaptive interface technology as a
    potential countermeasure for distraction related crashes

(2) Enhance collision warning system effectiveness by optimizing alarm
    onset algorithms tailored to the driver's level of workload and
    distraction.

(3) Conduct human factors research to help derive distraction and
    workload measures for use in algorithms for triggering interface
    adaptation.

(4) Develop and apply evaluation procedures for assessment of SAVE-IT
    safety benefits.

(5) Develop performance requirements for system operation and
    standards/guidelines for adaptive interface conventions.

(6) Provide the public with documentation of the human factors research
    and with information describing the algorithms for controlling the
    driver vehicle interface to the extent needed for specifying
    performance and standardization requirements.

(7) Identify potential scalable system concepts and sensing technologies
    for further stages of research and development as follow on to this
    SAVE-IT program.

The approach to achieving the above objectives will be described within
the statement of work (Section 2.3). In support of this mission, other
secondary objectives are outlined to provide focus to the development
process and to expedite the advancement of key technology science:

(8) Form a strong multi-disciplinary team that has demonstrated
    expertise in the areas of vehicle systems, integration, sensor
    development, human factors research, and adaptive safety management
    system principles.

(9) Leverage and exploit an existing critical technology portfolio in
    the areas of sensors, data fusion, human factors, and systems for
    the successful implementation of the proposed SAVE-IT Program. These
    activities will provide high value-added benefits of minimizing the
    learning curve, preventing duplication of effort, and streamlining
    the system design/integration process. This benefit will enable
    clear focus on meeting the research objectives.

(10) Utilize system engineering design procedures and processes to
     achieve a seamless comprehensive SAVE-IT system.

## 1.3 Benefits

***1.3.1 Safety Warning Systems***

Safety warning systems, such as forward collision warning (FCW), will
require the use of warnings about immediate traffic threats without an
annoying rate of false alarms and nuisance alerts. Both false alarm and
nuisance alerts conditions will be reduced by system intelligence that
integrates driver state and intent information. 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 alarm or
reduce the intrusiveness of the alerting stimuli. When a driver intends
to pass and is accelerating towards a lead vehicle, the audio stimulus
might be suppressed in order to reduce the alert annoyance. Decreasing
the number of false positives may reduce the tendency for drivers to
disregard safety system warnings. The SAVE-IT program will focus
directly on the major causes of vehicle crashes, as revealed by
crash-statistics analyses. This comprehensive program will provide a
framework for adaptive interface systems and expedite the development of
applied safety management solutions.

### 

***1.3.2 Distraction Mitigation***

Near term solutions have focused on improving designs to minimize driver
head down and hands-off-wheel time in addition to reducing the cognitive
load on the driver. Through the application of human factors principles,
vehicle systems designers have mapped vehicle interfaces to more closely
match user expectations. Other improvements emerge through innovative
applications of technology. Voice recognition and text to speech
capability offer significant advances. By linking voice control
capability with other technologies, a driver may use the cellular phone
or compose email, with higher efficiency than ever before. Additional
technology such as Head-Up Displays (HUD) and steering wheel controls
further reduce driver distraction. These interface technologies offer a
promising start to the distraction mitigation problem, but may not
represent the entire solution because they may not offer a substantial
reduction in cognitive distraction. Several studies have demonstrated
that cognitively taxing non-driving tasks can negatively impact driving
task performance, even when the driver has "eyes on road and hands on
wheel" (e.g., Recarte & Nunes, 2000; Lee, Caven, Haake, & Brown, 2000).

Although substantial improvements continue to be made within specific
interface components, the system interface as a whole may become
increasingly complex as it integrates a wider range of sub-components.
To counteract this expansion in complexity, the human-machine interface
must evolve into one that provides synergistic consideration of all
safety aspects of the driving experience. The future will require not
only those user interfaces that are available today, but advanced
integrated solutions that fuse driver and automobile. By incorporating
sensors and algorithms that measure the driver's visual and cognitive
involvement in the driving task, distraction mitigation systems will be
capable of countering the most significant sources of driver
distraction.

## 

## 1.4 Program Plan Summary

In order to achieve the SAVE-IT program objectives, the SAVE-IT system
may include the following subsystems: forward collision warning (FCW),
adaptive cruise control (ACC), mobile multi-media, and driver state
monitoring comprised of a HMI fusion processor (HMIP), an intent
monitor, an eye tracking system, and a heart rate and respiration
monitor.

Human factors research will determine the important variables in the
areas of driver state monitoring, situational threat assessment, and
adaptive countermeasures. Additionally, data fusion algorithms will be
developed, integrated, and validated as part of the system development
process. Integrated system development will evolve into a simulation
study and a road-worthy prototype vehicle for an evaluation of safety
benefits. Due to the complexity and breadth of this development effort,
the development process will rely heavily on well-established principles
of systems engineering. As such, the activities of the program are
separated into the following phases over the course of three years.

Phase 0: ISS development and frame-work (complete)

-   ISS second-generation concept vehicle platform

-   Preliminary distraction mitigation and adaptive safety warning
    countermeasures concepts

Phase I: Foundational research and concept development (1 year)

-   Human factors research to determine diagnostic measures in the areas
    of cognitive and visual distraction, intent, driving performance,
    telematics demand, and driving task demand

-   Identification of the crash scenarios against which the system will
    be designed to mitigate

-   Adaptive interface modeling and conceptual architecture development

-   Technology development, evaluation, and demonstration

-   Phase II planning and implementation plan

Upon completion of Phase I, resultant data from respective tasks will be
reviewed to assess whether adaptive interface technology is a viable
option for mitigating distraction-related crashes. Adaptive interface
technology will be demonstrated as viable if three criteria are met.
First, the proof of concept, employing the adaptive interface technology
for distraction mitigation and safety warning systems, is demonstrated
in terms of effectiveness and driver acceptance. Second, the deployment
of adaptive interface is technically feasible in terms of the
demonstrated capability and availability of sensors, actuators, and
workload/distraction manager. Third, diagnostic measures of driver
distraction and workload (e.g., visual and cognitive distraction) can be
identified. The deliverables from Phase I, including research reports
from respective tasks and a vehicle demonstration of selected adaptive
interface technology, will also serve to determine whether these
criteria have been met successfully and guide the development of the
Phase II implementation plan.

Phase II: Data fusion, integrated system development, and evaluation (2
years)

-   Diagnostic measures, algorithm development and validation

-   Data fusion modeling and situational assessment algorithm
    development

-   Development of integrated adaptive countermeasures based on
    situational threat assessment information to mitigate both
    distraction and collision avoidance

-   System integration and vehicle demonstration

-   Evaluation of safety benefits and user acceptance through driving
    simulator and on-road human factors studies

# Section 2: Technical Approach 

In order to achieve the stated objectives of the SAVE-IT program, the
team will leverage their respective internal activities and expertise in
a synergistic team approach. Although advanced technologies may be
employed to meet the program objectives, candidate technologies will be
evaluated and selected based on commercial viability and robustness.
This section describes the systematic technical plan that will be
executed to design, implement, and evaluate a fully integrated SAVE-IT
system.

## 2.1 System Development 

The system development effort will be based on advanced driver safety
management principles developed by DDE for support of Integrated Safety
Systems (ISS) initiatives and the ACAS FOT program. The activities
within these areas will expedite SAVE-IT technology assessment and
architecture development, allowing focus to remain on human factors
research and higher-level algorithm development. The system development
process will rely on well-established system design principles as a
framework to guide this highly focused design effort.

### ***2.1.1 SAVE-IT System Concept***

The SAVE-IT system is partitioned into three developmental areas: driver
state monitor, situational threat assessment and adaptive
countermeasures. Figure 1 presents the problem space for the SAVE-IT
program. The focus of the SAVE-IT program will encompass the Normal,
Warning, and Collision Avoidable states of the driving experience.

![](media/image1.wmf){width="6.866666666666666in"
height="2.8555555555555556in"}

Figure 1. The SAVE-IT system problem space

The SAVE-IT system will measure the visual and cognitive state of the
driver and external target and environmental data in order to determine
what level of non-driving task involvement is appropriate. The system
will integrate vehicle systems and driver, in an attempt to maintain
safe operating bounds. In the warning state, when a potential external
threat has been identified, the system shall notify the driver in a
manner that is quickly understood and encourages the driver to execute
an appropriate action. It is imperative that warnings be designed so not
to further distract, confuse, or annoy the driver. The SAVE-IT program
will assess the best methods of accomplishing successful safety
mitigation.

The SAVE-IT conceptual model (Figure 2) represents a preliminary
development framework, to be modified by the results of the human
factors research activity. Workload/distraction management and adaptive
interfaces are the result of integrating the driver state monitor (DSM),
situational threat assessment (STA) and adaptive countermeasures (ACM)
subsystems. Such a system has been successfully developed and
demonstrated as part of Delphi's Integrated Safety System (ISS) focus.
Central processing is performed within an HMI data fusion processor
(HMIP). The HMIP shall be based on the ACAS FOT platform, but modified
to accommodate the required I/O of the SAVE-IT system. Target assessment
will be performed via the external sensor suite that was utilized in the
ACAS FOT program. Although sensors are available, pedestrian and side
detection sensors are not targeted for this first SAVE-IT system, unless
warranted by the crash scenario analysis, because of budget and timing
constraints.

#### *[Driver State Monitor.]{.underline}* The driver state monitor (DSM) is based on the data fusion of driver physiological characteristics, driving performance and non-driving task activity. The DSM subsystem shall initially be comprised of the following sensing systems used to measure the physiological characteristics of the driver: stereo-vision head pose and eye-tracking system, heart rate monitor, and respiration monitor.

![](media/image2.wmf){width="6.7in" height="4.368055555555555in"}The
stereo-vision eye-tracking system will provide gaze point, gaze
variability, eye movement, blink, and closure, which are processed in
the HMI data fusion processor to determine real-time levels of
drowsiness (not included with current SAVE-IT scope), visual and
cognitive distraction. Threshold criteria will be based on human factors
research within the scope of the SAVE-IT program. Heart rate and
respiration monitoring will augment the ability to determine real-time
levels of driver workload, by providing driver arousal metrics. Human
factors research shall identify measures for each sensing system that
can diagnose important cognitive, visual, and intention states.

Figure 2. The SAVE-IT conceptual model

Although intent is primarily used by the situational threat assessment
subsystem, intent will be derived within the driver state monitor. The
variables that will identify driver intent include: gaze point,
throttle, brake, and steering angle. Future scalability considerations
could warrant the need for intent to be determined within the driver
state monitor.

Monitoring of the mobile multimedia system and cockpit interfaces will
also be performed to understand the non-driving task focus of the
driver. Performance measures will be assessed using algorithms derived
through human factors research. The candidate metrics for performance
assessment include derivations of throttle, brakes, steering angle, and
vehicle position, as a function of time. Performance measures will
provide a reference to assess the level of adaptation required of
distraction mitigation and safety warning system countermeasures.

The driver state monitor computation will occur within the HMI data
fusion processor. The situational threat assessment subsystem will
process visual and cognitive distraction levels to determine a global
attention allocation state and the resultant impact on driver reaction
time.

*[Distraction Mitigation.]{.underline}* In order to mitigate against a
global increase in risk due to inadequate attention allocation to the
driving task, several technologies will be developed to minimize driver
distraction (Table 1). For example, the distraction mitigation subsystem
will provide a warning or lock out functions when the driver is more
distracted than what the current driving environment allows. The system
will process the environmental demand (crash rates or reaction time
requirement/RT~REQ~), estimated reaction time (RT), and telematics
distraction potential to determine which functions should be advised
against or locked out. 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.

Table 1. Distraction and impairment countermeasure requirements

+--------------------+---------------+---------------+---------------+
| Countermeasure     | Responses     | Algorithms    | Requirements  |
| System             |               |               |               |
+--------------------+---------------+---------------+---------------+
| Distraction        | Function      | Cognitive     | RT~REQ~/Crash |
| Mitigation:        | Lockout       | distraction   | Rate          |
|                    |               | level         |               |
| Cognitive          | Call          |               | RT~C~         |
| Distraction        | Screening     | Visual        |               |
|                    |               | distraction   | RT~V~         |
| Visual Distraction | Visual        | level         |               |
|                    | Display       |               | Glance        |
|                    |               |               | duration &    |
|                    | Audio Display |               | frequency     |
|                    |               |               |               |
|                    | Haptic        |               | Telematics    |
|                    | Display       |               | Distraction   |
+--------------------+---------------+---------------+---------------+
| Drowsiness         | Visual        | Drowsiness    | RT~D~         |
| Mitigation         | Display       | Level         |               |
|                    |               |               | Gaze          |
|                    | Audio Display |               |               |
|                    |               |               |               |
|                    | Haptic        |               |               |
|                    | Display       |               |               |
+--------------------+---------------+---------------+---------------+
| Substance          | Visual        | Substance     | RT~S~         |
| Impairment         | Display       | impairment    |               |
| Mitigation         |               | level         | Gaze          |
|                    | Audio Display |               |               |
|                    |               |               |               |
|                    | Haptic        |               |               |
|                    | Display       |               |               |
+--------------------+---------------+---------------+---------------+

#### *[Driver Impairment.]{.underline}* Driver impairment is inherently provided by the DSM and the associated sensor suite. Sensor data would include eye tracking, PERCLOS measure, heart rate, and respiration. This data, when fused with performance metrics, could support an assessment of the driver's physiological state in terms of drowsiness and alcohol impairment. Driver impairment assessment could support appropriate countermeasures. The drowsiness mitigation subsystem would receive information regarding driver's drowsiness level (RT~D~) to determine what combination of visual, audio, and haptic warning should be provided to maintain driver attention. The substance impairment mitigation subsystem would receive information regarding driver's substance impairment (RT~S~) to determine when warnings should be applied to the driver that they should cease driving the vehicle. Due to timing and funding limitations of the SAVE-IT program, driver impairment is a candidate for follow-on study.

*[Adaptive Safety Enhancement.]{.underline}* Crash scenario analysis
will determine the focus of the SAVE-IT system implementation. Forward
and side detection systems shall enable the situational threat
assessment algorithms to dynamically assess the evolving traffic
conditions around the host-vehicle. Technology will be available to
address the following potential applications:

-   Adaptive cruise control (ACC), using forward looking sensors, can
    enable maintenance of either constant cruising speed or constant
    headway between vehicles.

-   Forward collision warning (FCW) and blind-spot warning (BSW) sensors
    can measure the driving environment of the forward and side zones of
    the vehicle respectively.

-   Lane departure warning (LDW), employing vision-based sensing, can
    determine the position of the vehicle within the lane.

Table 2. Situation-specific countermeasure requirements (Safety warning
countermeasures)

+------------------+--------+-----------------------+-----------------+
| Countermeasure   | Res    | Algorithms            | Requirements    |
| System           | ponses |                       |                 |
+------------------+--------+-----------------------+-----------------+
| Adaptive Cruise  | H      | Headway Adjustment    | RT              |
| Control (ACC)    | eadway | Level                 |                 |
|                  | Adju   |                       | Coefficient of  |
|                  | stment |                       | Friction        |
+------------------+--------+-----------------------+-----------------+
| Forward          | Visual | Forward Collision     | RT              |
| Collision        | D      | Threat Level          |                 |
| Warning (FCW)    | isplay |                       | Coefficient of  |
|                  |        | Alert Intrusiveness   | Friction        |
|                  | Audio  |                       |                 |
|                  | D      |                       | Intent (change  |
|                  | isplay |                       | lanes, pass,    |
|                  |        |                       | brake)          |
|                  | Haptic |                       |                 |
|                  | D      |                       | RT~REQ~ /Crash  |
|                  | isplay |                       | Rate            |
|                  |        |                       |                 |
|                  |        |                       | Gaze            |
+------------------+--------+-----------------------+-----------------+
| Lane Departure   | Visual | Roadway Departure     | RT              |
| Warning (LDW)    | D      | Threat Level          |                 |
|                  | isplay |                       | Intent (avoid,  |
|                  |        | Alert Intrusiveness   | pass, turn)     |
|                  | Audio  |                       |                 |
|                  | D      |                       | Gaze/Eye        |
|                  | isplay |                       | Closure         |
|                  |        |                       |                 |
|                  | Haptic |                       |                 |
|                  | D      |                       |                 |
|                  | isplay |                       |                 |
+------------------+--------+-----------------------+-----------------+
| Blind Spot       | Visual | Side Collision Threat | RT              |
| Warning (BSW)    | D      | Level                 |                 |
|                  | isplay |                       | Intention       |
|                  |        | Alert Intrusiveness   | (change lanes)  |
|                  | Audio  |                       |                 |
|                  | D      |                       | Gaze            |
|                  | isplay |                       |                 |
|                  |        |                       |                 |
|                  | Haptic |                       |                 |
|                  | D      |                       |                 |
|                  | isplay |                       |                 |
+------------------+--------+-----------------------+-----------------+

#### 

Collision warning thresholds will be modified according to the
distraction level to provide timing that is contingent on the driver's
level of distraction or impairment. When situational threat assessment
determines that a high-probability crash event is unfolding, the
adaptive countermeasure system will provide an intuitive warning cue to
reorient the driver's attention. The driver state monitor system will
assess the driver's reaction time, thereby estimating how much time the
driver requires in order to safely address the developing situation. By
knowing where attention is allocated, the system can determine whether
the driver will be aware of developing threats. Information about driver
distraction and impairment will be fed into a central processor, which
will determine the urgency of various situational threats. Thus, an
adaptive cruise control subsystem (ACC), a forward collision warning
subsystem (FCW), a lane departure warning subsystem (LDW), a blind spot
warning subsystem (BSW) may be developed to fuse workload management
information (Table 2). The ACC subsystem processes information regarding
the driver's predicted reaction time to make headway adjustments. When
reaction time (RT) is impaired, the ACC subsystem will automatically
increase headway. The FCW subsystem processes information regarding
reaction time, driver's intention, gaze, and environmental demand (crash
rates or reaction time requirement/RT~REQ~) in addition to the
kinematics of the lead and host vehicles to calculate a threat level for
the lead vehicle. The LDW subsystem processes information regarding
reaction time and driver's intention as well as lane position to
calculate a threat level. Intention and gaze information could
potentially be used to mitigate the intrusiveness of the alert. The BSW
subsystem predicts whether the host vehicle is in danger of colliding
with a vehicle in the adjacent lane and can be enhanced by using
reaction time and intention information. If the subsystem can determine
that the driver intends to change lanes, but there is a vehicle in the
driver's blind spot, a warning can be issued accordingly. Given the
timing and budget constraints of this program, the SAVE-IT team will
focus on a subset of these safety warning countermeasures.

#### 

#### *[System Architecture.]{.underline}* The SAVE-IT system architecture will be based on the architectures provided by an ISS development vehicle and ACAS FOT development vehicle for Phases I and II respectively (see Figure 4). The architectures will be modified to accommodate the human factors research tasks. 

To enable the evaluation of distraction mitigation, a mobile multimedia
system will be integrated into the concept demonstration and test
vehicle. The system will include such features as cellular telephone,
navigation, web browser, email , memo, voice recognition, and text to
speech.

### ***2.1.2 Sensor Arrays***

#### *[Internal Sensor Array.]{.underline}* The internal sensor array will provide the driver state monitor with important driver state information. The array shall consist of a stereo-vision eye-tracking system and a heart-rate and respiration monitor. Eye tracking provides the critical data required for determining visual and cognitive distraction, driver intent and drowsy-driver detection. To date, oculometric sensing (eye tracking) has been reserved for the research community due to acquisition principles not compatible with the automotive environment, system complexity, and high cost. The system that is proposed here has been proven to yield accurate results within the vehicle environment via a robust method of data acquisition. This non-invasive and non-contact system captures driver's head orientation and eye gaze under a wide variety of ambient light conditions by using stereo-vision cameras integrated into the vehicle. The image processing unit extracts driver oculometric data such as driver's head pose, eye gaze, and blink characteristics for supporting higher level algorithms that assess driver distraction, impairment, alertness, and intent. The targeted eye-tracking system will be a result of a cooperative development effort between DDE and Seeing Machines Pty. Limited. The system will be applied across the board to provide consistency throughout testing.

Biosigns sensing is the term used to encompass all other physiological
sensing sans eye tracking. Specifically, the importance of heart rate
and respiration monitoring will be evaluated in Phase I of this SAVE-IT
program. Although non-invasive technologies exist for automotive
application, standard wrist and chest strap methods of measure will be
used for heart rate and respiration respectively. This will serve to
minimize cost and time, allowing a demonstration of automotive solutions
which compared with eye tracking, may be of questionable value for
determining driver state. It should be noted, however, that biosign
monitoring is likely to have a significant role in diagnosing post
collision event driver state. Biosigns will be monitored as dependent
variables within the Phase I human factors studies. Upon evaluation of
their importance the respective biosigns may or may not be incorporated
into the Phase II architecture.

*[External Sensor Array.]{.underline}* The exterior sensor suite will be
based on that established for the ACAS FOT and the ISS development
initiative. This will include the following: a forward radar sensor for
the detection of targets and target kinematic attributes, a global
positioning system (GPS) and map system to support the identification of
roadway curvature, and a forward vision system to support measurements
of lane curvature and host vehicle position within the lane. Other
sensors include: differential speed, speed, acceleration, steering
angle, and yaw rate. If crash scenario analysis warrants the integration
of blind spot and side collision warning, side detection sensing will
also be provided, consisting of short range radar sensors.

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### ***2.1.3 Workload/Distraction Management (Data Fusion)***

#### An important component within the system, in addition to the sensing technologies, is a workload/distraction manager that assesses the driver's attention allocation based on the relative demands of the outside and inside vehicle tasks. The algorithms for processing the sensor data should be robust enough to function in a variety of roadway and traffic scenarios across a diverse driving population. This task will be accomplished in situational threat assessment (STA) and the associated data fusion. STA will be based on the data fusion of driving task demand, telematics demand, driver state, and driver performance. Human factors research will extract reaction time and other diagnostic measures as input into the fusion algorithm. STA will be partitioned in such a way to focus on the mitigation zones of coverage. The countermeasure subsystems will align with the forward, lateral and rear zones of coverage in addition to distraction mitigation. Crash scenario identification will initially reduce the data fusion effort to accommodate the boundary conditions of the SAVE-IT program. The fusion computation will generate variables specific for global use as well as those specifically important to each countermeasure subsystem. An important variable is the driver's reaction time specific to the safety coverage zone. For example, the driver may be aware of the vehicle located in the adjacent lane, but due to this focus, be unaware of the forward threat that may be unfolding. Whereas reaction time relative to lateral safety management may be acceptable, reaction time to the forward condition may not. The data fusion involved with driving task demand assessment will determine the reaction time requirement, which will subsequently be compared to the composite reaction time. Thresholding of reaction time specific to each zone will be performed by the STA specific to each countermeasure subsystem. This will determine the level of countermeasure required to mitigate the given situation.

The research findings from respective tasks will be reviewed,
integrated, and fused to determine the overall workload and distraction
level. Threshold values will be determined to distinguish between
acceptable and unacceptable distraction levels. It is likely that
various models and alternative algorithms will be produced. For example,
the black-box approach (the product model) focuses on the mapping from
input to output and disregards the internal structure, whereas the
glass-box approach (the process model) explicitly considers the internal
structure and intermediate steps involved in mapping input to output.
Various data fusion approaches for implementation of the STA block are
available, including analytic, rule-based expert system, and neural
nets. In this particular case, data fusion will be based on the format
of the input data (values, ranges, categories), varying data
reliability, and expected complexity and nonlinearity of the relation
between the STA inputs and outputs. These models and algorithms will be
evaluated in human factors experiments that involve simulators, test
tracks, and on-road testing.

### ***2.1.4 Adaptive Interface***

Based on the results of human factors research and crash scenario
identification, the system will be designed to provide adaptive
integrated visual, auditory, and haptic warnings to direct the driver's
attention to the threat/hazard at hand. In addition to the control set
of the instrument panel and mobile multimedia (MMM) system, the adaptive
countermeasure human machine interface will include a color head-up
display (HUD), 3-D audio cueing, and haptic seat. The HUD, audio, and
haptic systems will be based on those used for the ACAS FOT program.
Others that are identified via human factors research will be
incorporated.

The color reconfigurable HUD may provide warning icons indicating
associated threat levels respective of the forward road, blind spots,
and the rear of the vehicle. The choice of the icons shall be based on
human factors research and crash scenario identification. 3-D auditory
warnings provide directional cues about the location of the impending
threat in a 360-degree coverage area (front, rear, left, or right). Tone
characteristics are based on human factors research and with respect to
the crash scenarios identified. The haptic seat will provide left,
right, and center haptic cueing ability. Should lateral safety
management crash scenarios be addressed within the scope of this SAVE-IT
program, the side mirror icons and directional haptic seat vibration
will discriminate between the left or right side of the car for blind
spot warnings.

Adaptive forward collision warning sensitivity and ACC headway
adjustment will be provided based on real-time situational threat
assessment. The control platform for ACC headway adjustment will be
based on the ACAS FOT for the test vehicle in Phase II. If an excessive
level of distraction is detected, or the driving task demand is
sufficiently high to warrant the initialization of distraction
mitigation, telematics features may be suppressed and visual (HUD) and
auditory warnings may be displayed to remind the driver to re-focus
attention on the driving task. For example, when approaching a busy
intersection, incoming cellular phone calls may be re-routed to an
answering service. When drivers are visually distracted for an extended
period of time, an auditory stimulus may be presented to remind drivers
to reorient visual attention toward the forward road.

Adaptive interfaces represent a qualitative shift in in-vehicle
technology. As systems become increasingly adaptive drivers may have an
increasingly difficult time predicting and anticipating the behavior of
the system. In aviation, surprises related to adaptive systems have
caused substantial safety concerns. Avoiding these kinds of problems in
the driving domain will require careful consideration of how to develop
veridical mental models of the adaptive system. The SAVE-IT team will
address this issue by assessing driver expectations regarding system
behavior and tracking the incidence of surprises associated with
adaptive changes. The general approach to creating mental models will be
to identify an appropriate metaphor that drivers can use to infer
general behavior and then develop detailed mental models. An important
means of supporting accurate mental models is to provide detailed
feedback on system behavior and state changes. By embedding feedback in
a coherent metaphor, drivers are likely to develop accurate mental
models that avoid surprises associated with adaptive interface behavior.

## 2.2 The Philosophy of Human Factors Research

At present, researchers tend to adopt one of two approaches to the
workload management problem: the arousal approach and the demand
approach. The arousal approach measures the energy level of the
autonomic nervous system in attempt to determine the amount of driver
workload. The level of demand approach measures external environmental
information or uses the method of task analysis to determine the demand
placed on the driver. This section will describe these two approaches in
more detail and then explain how the SAVE-IT program approach differs
from them.

### ***2.2.1 The Arousal Approach***

The "Yerkes-Dodson law" has frequently been applied to the workload
management problem (De Waard, 1996). This law states that human
performance is optimal at an intermediate level of arousal and may
suffer if arousal is too low or too high. Arousal is measured from
physiological metrics such as heart rate, respiration rate, and pupil
diameter. The arousal approach proposes that a workload management
system should increase arousal level when it is low and decrease the
arousal level when it is high so that the arousal level is maintained
within the optimal range. One major problem with this approach is that a
high level of arousal could result from a wide range of factors, such as
physical or mental workload, emotion, anxiety, or stress. Arousal and
distraction, for example, may be not be correlated. Therefore, the
assumption that driver distraction, workload, substance impairment, and
drowsiness fall onto a uni-dimensional continuum (i.e., arousal) may be
overly simplistic. Because of this lack of specificity, arousal is not
diagnostic of driver distraction, workload, or driver safety. Even if a
high level of arousal is detected, it is not clear what action should be
taken to reduce the arousal level. If a driver is emotional or stressed,
technology could offer little assistance in calming the driver. Changes
in many of the nervous system measures may not be related to arousal,
for example, pupil diameter, varies with ambient illumination, and
therefore it is of limited application in the automotive industry.
Despite the shortcomings of this approach, the concept of arousal is
useful in the study of driver impairment because impairment is
accompanied with a low level of arousal and has clear and immediate
consequences for driver safety.

### ***2.2.2 The Demand Approach***

Some researchers have proposed a demand-based, "driver-out-of-the-loop"
workload management system that considers driving and non-driving task
demands to the exclusion of driver state assessment (e.g., the GIDS
approach, Michon, 1993). It is assumed that environmental factors such
as road, weather, and traffic conditions can determine driving demand,
so that more non-driving tasks (distraction) are permitted under low
driving demand situations and fewer distraction tasks are permitted
under high driving demand situations. Inspired by the domain of aviation
human factors, many researchers have approached the workload management
problem through task analysis, drawing parallels between pilots using a
declutter button during high workload events and the automotive
environment (Mykityshyn & Hansman, 1993). This approach identifies
information prioritization and scheduling as an important focus of
workload management. This is a reasonable approach because it focuses on
task demands that have implications for safety, but it has several
limitations.

One limitation is that the aviation human factors research and
methodologies may not be directly applicable to the automotive domain.
Aviation missions tend to be more predictable at a macroscopic level,
with take-off at the beginning, attaining desired altitude, navigation
through waypoints, weapons deployment (in military missions), return to
final approach, final approach, and landing. The irregularity of the
driving task at the macroscopic level makes it less amenable to task
analysis. In addition, military missions tend to encompass much higher
rates of information flow than the automotive environment. Military
pilots are bombarded by multiple screens of information detailing
navigation, aircraft state, and threat information, and an analogy has
been made between controlling the flight stick and playing a piccolo.
The degree of training and personnel selection for pilots compared with
drivers is an additional factor that limits the generalizeability of
aviation research to the automotive domain. The aviation industry has
been researching workload management and adaptive interfaces for several
decades, creating a vast amount of data to draw from. The SAVE-IT team
will review this literature thoroughly to guide further research,
however, we will be careful before generalizing these data to automotive
applications. The literature reviews of the relevant tasks will
scrutinize information from the aviation domain, and determine which
theories and measures are suitable for the SAVE-IT applications.

Another limitation of a "driver-out-of-the-loop" system is that it
disregards driver's individual differences. When given the same weather,
road, traffic, and telematics system, different drivers may exhibit
different levels of visual and cognitive distraction. Some individual
differences may be attributed to driver age and experience. Furthermore,
a demand-only approach ignores the fact that when given similar driving
and non-driving tasks, the same driver may behave inconsistently across
time. Given the same external and internal demands, a driver could be
allocating adequate attention to the driving task, or allocating
attention to unrelated tasks, such as daydreaming or focusing on an item
in the external world that is unrelated to the driving task. Approaches
which do not measure the driver would be incapable of making these kinds
of distinctions. In short, distraction is inherently an individual- and
time-dependent driver state and therefore the driver should be included
in the loop of distraction assessment. Although understanding the
demands placed upon the driver is crucial for approaching the workload
management problem, in isolation, the demand approach is incomplete.

### ***2.2.3 The SAVE-IT Program Approach***

The previous approaches are limited because they do not directly measure
the allocation of attention. The SAVE-IT system will be comprehensive
and consider driver state in addition to driving and non-driving task
demands. For driver state, the SAVE-IT approach focuses on the
perceptual and cognitive allocation rather than the affective state and
personality. To the extent that a low level of arousal is associated
with impairment, arousal is studied in that context. Sensor development
is paramount to the assessment of driver state. DDE and Seeing Machines,
Inc. have co-developed an automotive-grade, non-obtrusive, stereo
eye-tracking system (ETS) that tracks driver's gaze point and will
enable the measurement of attention allocation. The nature of the
countermeasure subsystems portrayed in Tables 1 and 2 will dictate the
types of information that the human factors research investigates,
because for this information to be useful, the driver state and
environmental assessment must be tailored to the potential
countermeasure systems.

![](media/image3.wmf)*[Attention Allocation in Driving.]{.underline}*
Crashes are frequently caused by drivers paying insufficient attention
when an unexpected event occurs, requiring a novel (non-automatic)
response. As displayed in Figure 3, attention to the driving task may be
depleted from either allocation to non-driving tasks, or from impairment
(drowsy or substance) leading to diminished attentional resources. 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) 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 require
more attention because during any time attention is diverted, there is a
high probability that a novel response may be required.

Figure 3. Attention allocation to driving and non-driving tasks

A safety system that mitigates the use of in-vehicle information and
entertainment system (telematics) must balance both attention allocated
to the driving task and attention demanded by the environment. In low
driving demand scenarios, allocation of attention to non-driving tasks
may not adversely impact safety. In high driving demand scenarios, the
same non-driving tasks could divert much-needed attention away from the
driving task. 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 evident in Figure 1, "routine" driving may suffice
during low or moderate driving task demand, a distracted driving may
suffice during low driving task demand, but high driving task demand
requires attentive driving.

Drivers routinely perform three classes of activities: (1) vehicle
control such as speed and lane control, (2) tactical maneuvering such as
speed and lane choice, navigation, and hazard monitoring, and (3)
non-driving tasks. Attentional allocation to these activities is dynamic
and may vary with individuals. Both vehicle control and tactical
maneuvering are subcomponents of the driving task. Under most
situations, vehicle control is well-learned and does not require a high
level of attention; however, tactical maneuvering requires a high level
of attention because it can require a novel response.

An important 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 and
is widely used as input to algorithms, such as the forward collision
warning algorithm. Therefore, RT to probabilistic events is chosen as
the primary, "ground-truth" dependent variable for this research
program. RT may not account for all of the variance in driver behavior,
and other measures such as headway, action selection and eye glance
behavior may be considered separately.

### ***2.2.4 SAVE-IT Task Structure and Methodology***

*[Task Structure.]{.underline}* Given that the SAVE-IT program is a
3-year program of limited budget, we must be selective and scale the
proposed research to a level that fits the program objectives.
Consequently, impairment- and drowsiness-related research is beyond the
purview of the SAVE-IT program. Distraction is usually divided into
cognitive distraction, visual distraction, manual distraction, and
auditory distraction.[^1] Cognitive distraction is distinct from visual
distraction because drivers may keep their eyes on the road but take
their mind off the driving task. To achieve the seven major objectives
of the SAVE-IT program (Section 1.2), we adopt a "divide and conquer"
approach, dividing the program into fifteen manageable tasks. The
mapping from tasks to objectives is discussed below and will be
displayed in Table 5. The tasks will be discussed in detail in the
statement of work (Section 2.3), and are listed as follows:

**(1) Scenario identification**

-   Identify which driving scenarios the SAVE-IT technologies are likely
    to offer the most benefit in reducing crashes.

-   By targeting the distraction-related problem scenarios for the
    design of mitigation solutions, this task will contribute to the
    objectives of advancing the deployment of adaptive interface
    technology as a countermeasure for distraction-related crashes
    (Objective 1) and enhancing the effectiveness of collision warning
    systems by optimizing alarm onset algorithms tailored to the
    driver's level of workload and distraction (Objective 2). The
    identification of scenarios will also contribute to designing
    experiments to evaluate the SAVE-IT systems (Objective 4), by
    providing prototypical scenarios to mitigate against.

**(2) Driving task demand**

-   Develop algorithms that measure the level of attention that is
    required by the driving environment as a function of environmental
    parameters.

-   Based on the philosophy of maintaining an allocation of attention
    that is commensurate with the level of driving task demand, this
    program must develop a reliable and valid means of measuring driving
    task demand. This will serve the objectives of advancing the
    deployment of adaptive interface technology (Objective 1) and
    conducting human factors research to help derive distraction and
    workload measures (Objective 3). In a vehicle that does not employ
    an eye-tracking system, driving task demand measures may be of
    elevated important for determining which telematics functions are
    appropriate at a given time, therefore this task contributes to the
    objective of identifying potential scalable system concepts
    (Objective 7).

**(3) Performance**

-   Develop algorithms that reliably measure driving performance.

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-   Because poor driving performance may be indicative of inadequate
    attention allocation, an algorithm that measures driving performance
    will contribute to the objectives of advancing the deployment of
    adaptive interface technology (Objective 1) and conducting human
    factors research to help derive distraction and workload measures
    (Objective 3). In a vehicle that does not employ an eye-tracking
    system, performance measures may be of elevated important for
    determining which telematics functions are appropriate at a given
    time and therefore this task will also contribute to the objective
    of identifying potential scalable system concepts (Objective 7).

**(4) Distraction mitigation**

-   Develop countermeasures that mitigate against inappropriate levels
    of distraction, while maintaining high levels of driver acceptance
    (e.g., screening phone calls).

-   This task will contribute directly to the objectives of advancing
    the deployment of adaptive interface technology (Objective 1), and
    developing performance requirements and standards/guidelines for
    adaptive interface conventions (Objective 5).

**(5) Cognitive distraction**

-   Develop algorithms that can reliably measure the level of cognitive
    distraction.

-   This task will contribute directly to the objective of conducting
    human factors research to help derive distraction and workload
    measures for use in algorithms for triggering interface adaptation
    (Objective 3). Because distraction measures provide input to
    countermeasure systems, this task will also contribute to advancing
    the deployment of adaptive interface technology (Objective 1),
    enhancing collision warning systems (Objective 2), and developing
    performance requirements and standards/guidelines (Objective 5).

**(6) Telematics demand**

-   Identify the distraction potential and priorities of telematics
    functions to support distraction countermeasure systems.

-   By providing information that will dictate which telematics
    functions should be blocked or advised against, this task will
    contribute to the objectives of advancing the deployment of adaptive
    interface technology (Objective 1). This research will also serve
    the objectives of conducting human factors research to derive
    distraction and workload measures (Objective 3), and developing
    performance requirements and standards/guidelines (Objective 5).

**(7) Visual distraction**

-   Develop algorithms that can reliably measure the level of visual
    distraction.

-   This task will contribute directly to the objective of conducting
    human factors research to derive distraction and workload measures
    (Objective 3). Because distraction measures provide input to
    countermeasure systems, this task will also contribute to advancing
    the deployment of adaptive interface technology (Objective 1),
    enhancing collision warning systems (Objective 2), and developing
    performance requirements and standards/guidelines (Objective 5).

**(8) Intent**

-   Develop algorithms that can reliably measure the immediate intention
    of the driver (e.g., intent to pass)

-   Providing driver intent information to the safety warning
    countermeasures will reduce the number of false alarms, therefore,
    this task contributes to enhancing the effectiveness of collision
    warning systems (Objective 2).

**(9) Safety warning countermeasures**

-   Improve existing safety warning countermeasures so they can
    adaptively warn the driver about immediate threats in the
    environment as a function of driver state information (e.g., forward
    collision warning).

-   This task contributes directly to the objectives of enhancing the
    effectiveness of collision warning systems by optimizing alarm onset
    algorithms tailored to the driver's level of workload and
    distraction (Objective 2), and developing performance requirements
    and standards/guidelines (Objective 5).

**(10) Technology development**

-   Identify and develop technologies for supporting different stages of
    SAVE-IT system development (e.g., eye tracking). Develop a concept
    vehicle that can serve as a platform for the system algorithms.

-   This task will either directly or indirectly serve all seven
    objectives of this program by providing the necessary technology to
    support the human factors research and build a viable system. One of
    the most important objectives that this task serves is identifying
    scaleable system concepts and sensing technologies for further
    stages of research and development (Objective 7).

**(11) Data fusion**

-   Develop algorithms that coherently fuse all data from the
    sub-systems into information that can drive the countermeasure
    systems.

-   Because this task involves developing the final algorithms that will
    assess the driver state and govern the behavior of the
    countermeasures, this task is a requirement for fulfilling the
    objectives of advancing the deployment of adaptive interface
    technology (Objective 1), enhancing the effectiveness of collision
    warning systems (Objective 2), conducting human factors research to
    help derive distraction and workload measures (Objective 3), and
    developing performance requirements and standards/guidelines
    (Objective 5).

**(12) Establish Guidelines and Standards**

-   Develop performance requirements for system operation and
    standards/guidelines for adaptive interface conventions.

-   This task will contribute directly to the objective of developing
    performance requirements for system operation and
    standards/guidelines for adaptive interface conventions (Objective
    5). Because these standards/guidelines will be implemented in this
    program, this task will also contribute to the objectives of
    advancing the deployment of adaptive interface technology (Objective
    1), and enhancing the effectiveness of collision warning systems
    (Objective 2). These standards/guidelines will also provide an
    important document to be disseminated to the public (Objective 6).

**(13) System integration**

-   Integrate the sensors, countermeasures, and algorithms into a fully
    functional prototype vehicle.

-   By providing a prototype vehicle that is composed of the SAVE-IT
    systems, this task will contribute to the objectives of advancing
    the deployment of adaptive interface technology (Objective 1), and
    enhancing the effectiveness of collision warning systems (Objective
    2). Because the prototype vehicle will be used in the evaluation,
    this task will also contribute to the objective of developing and
    applying evaluation procedures for assessment of SAVE-IT safety
    benefits (Objective 4).

**(14) Evaluation**

-   Evaluate the safety benefits and driver acceptance of the SAVE-IT
    systems.

-   This task will directly contribute to the objective of developing
    and applying evaluation procedures for assessment of SAVE-IT safety
    benefits (Objective 4). Because this evaluation will reveal which
    aspects of the SAVE-IT system are effective, this task will
    contribute to all seven objectives.

**(15) Program summary and benefit evaluation**

-   Develop a program summary that describes all of the preceding tasks
    in detail, evaluates the benefit of the SAVE-IT systems, and
    disseminates the resultant data of this program to the public.

-   This task will directly contribute to the objective of providing the
    public with documentation of the human factors research and with
    information describing the algorithms for controlling the driver
    vehicle interface to the extent needed for specifying performance
    and standardization requirements (Objective 6). Increasing public
    and industry knowledge about the SAVE-IT systems will contribute to
    advancing the deployment of both distraction mitigation
    (Objective 1) and safety warning (Objective 2) countermeasures. An
    important component of this task will be identifying which concepts
    and technologies are appropriate for further stages of research and
    development (Objective 7).

The program is divided into two phases. Phase I includes tasks that can
be studied in parallel. Phase I supports the objectives of conducting
human factors research to derive distraction and workload measures, and
initiates the development of adaptive interface technologies and
enhanced collision warning systems. The objectives of Phase I tasks are
to identify crash scenarios that SAVE-IT should be designed to prevent,
evaluate available technologies and sensors, and conduct initial human
factors research, including literature review and the identification of
diagnostic measures for respective dimensions, to guide the development
of more detailed implementation plan in Phase II. At the end of Phase I,
a concept vehicle will be demonstrated and more details will be provided
about the plan to be implemented in Phase II. If initial experiments
demonstrate the need of adding, removing, or modifying tasks, the task
structure can be revised accordingly. The initial human factors research
in Phase I will determine which measures are diagnostic for each
dimension, however, but stop short of determining and validating
algorithms and countermeasures. In Phase II, additional human factors
research will be conducted to develop and validate the algorithms and
countermeasures.

In addition to the technology demonstration, the SAVE-IT program will
collect, summarize, and disseminate data that describe driver behavior.
The simulator, on-road, and test track experiments will collect driver
behavior data that will be summarized in relational databases for
scalability, organization, and documentation. The data will be delivered
in the most convenient form for the sponsor, perhaps recordable DVDs,
portable hard disks, or CDROM. These data provide an important resource
that will enable other researchers to examine algorithm performance and
explore alternate interpretations and hypotheses. The SAVE-IT team will
provide a small set of representative conditions. These data will
include all conditions for several drivers. Depending on the demand for
these data, NHTSA has the option to expand this effort to provide a more
comprehensive archive. Although UMTRI now employs Microsoft's SQL Server
2000, scripts will be provided to unpack the data into the sponsor's
particular database application.

Beyond the dissemination of empirical data, the program will publish
experiment results through professional conferences and peer-review
journals. The SAVE-IT team has a strong history of publication. Each
task will also contribute guidelines that will be synthesized in Task
12. These guidelines will be organized in a two page format that has
been developed and used successfully in previous projects. These
guidelines will capture common findings and principles that should
govern adaptive interface technology for cars. The SAVE-IT team will
also work to incorporate these findings into SAE and other national and
international standards. Several team members are currently contributing
to SAE standards on in-vehicle system evaluation, speech system
conventions, and collision warning design.

*[Variables.]{.underline}* An important objective of this research
program is to identify signature measures and variables for dimensions
such as distraction, impairment, and intent. Previous research has
revealed that gaze variability (Recartes & Nunes, 2000) and steering
entropy (Boer, 2001) are indicative of cognitive distraction, but more
research is required to determine combinations of measures that are
diagnostic of the given dimensions. Decades of human factors research
have identified several signature measures such as glance duration and
frequency for visual distraction. Research is required to determine
performance and physiological measures that are diagnostic of driver
intent. Driving task demand can be determined as a function of road,
traffic, and weather variables. Telematics demand can be determined for
various in-vehicle devices and features.

*[Methods and Resources.]{.underline}* There is a tradeoff between
control and realism in the different research environments. At the most
realistic extreme is the on-road environment, which affords no control
over environmental parameters such as traffic and road geometry. At the
other extreme is the driving simulator, which offers less realism but
almost total control over environmental parameters. Between the two
extremes of realism and control exists the test track which offers
moderate control and realism. Human factors researchers possess
differing perspectives on the ecological validity of behavioral research
conducted in driving simulators. If drivers do not consider the
simulation environment to be "realistic", drivers may not behave
naturally. It is feared that some drivers may treat the task like a
video game and test the system to see how it behaves under extreme
conditions. Many researchers believe that the test track offers a viable
alternative to the driving simulator. The test track, however, also
presents departures from reality. Drivers are still confronted with the
unusual task of "trying to drive normally" in an unfamiliar vehicle, and
may imagine experimenters scrutinizing their every move. Drivers on a
test track are also stripped of natural motivating factors such as the
need to travel from one location to another in an acceptable time, and
may become burdened with unnatural motivating factors such as not
damaging the unfamiliar property and not appearing to behave in an
abnormal manner. If an experimenter is seated in the passenger seat, the
driver may become overwhelmed with feelings of self-consciousness and
may drive in a manner that is far more cautious than "normal driving".

The driving simulator, test track, and on-road environments offer
different advantages and disadvantages and consequently no one
environment is appropriate for all research questions. In some
situations, the level of risk may rule out experimentation with real
vehicles. In other situations, where vehicle dynamics such as braking
and turning are an integral aspect of the problem focus, the driving
simulator may not be appropriate. Skillful researchers can select the
most appropriate environment and minimize the disadvantages contained
therein. For example, researchers can allow drivers to become accustomed
with the new environment so that the novelty wears off, and create a
surrogate set of motivational constraints that effectively mimic the
natural constraints of the real environment. If a study is not overly
dependent on the dynamics and feel of the vehicle, and the driver is
able to achieve temporary suspension of disbelief about the environment,
valid data may be obtained in a driving simulator.

Given that the scope of the SAVE-IT program requires research to be
conducted in simulator, test track, and on-road environments, the
SAVE-IT team have secured driving simulators at DDE, the University of
Iowa (including National Advanced Driving Simulator, or NADS), UMTRI,
and Ford Motor Company (including Virtual Test Track
Experiment/VIRTTEX), and test tracks at Transportation Research Center
(TRC) and Dana Corporation. Approximately 20 subjects will be used for
each experimental condition, sampled from Kokomo (IN), Iowa City (IA),
Ann Arbor (MI), Detroit (MI), and Columbus (OH). A ruse and reward
system will be used to surrogate the natural motivational constraints of
driving, such as productivity, arriving at the location in a reasonable
time, and safety.

The first task of this program will be identifying scenarios that are
strongly affected by driver distraction. These tasks will be used in
experimental designs of other tasks. Vehicle following scenarios have
been used in several studies, including the driver-vehicle interface
studies of the ACAS FOT program and Lee et al.'s email studies (NHTSA
DTNH22-99-H-07019, 2001; Lee et al., 2000), and crash data suggest that
driver distraction is a common cause of rear-end collisions (Wang,
Knipling, & Goodman, 1996). If the scenario identification task reveals
that vehicle following tasks should be used, headway could be
constrained to vary within a small range (e.g., 1-2 seconds). The lead
vehicle may apply non-imminent braking at approximately 0.2 g
erratically several times within a condition, and brake reaction time
(BRT) could be analyzed as the primary dependent variable (see Lee et
al., 2000). Erratic, non-imminent situations frequently occur in
real-world driving and therefore this scenario will have high face
validity. An additional reason for using non-imminent braking events is
that they are more suitable than imminent events for a within-subjects
design. Within-subjects comparisons are crucial for the removal of
individual differences among conditions. To control order effects,
conditions will be counterbalanced with Latin squares and sufficient
practice will be provided before the experimental conditions begin.
Results will be analyzed using appropriate statistical techniques (e.g.,
regression, analysis of variance, and analysis of covariance, and time
series analysis or entropy analyses) to develop driver models and
algorithms.

## 

## ![](media/image4.wmf){width="6.177083333333333in" height="9.875in"}

## 

## 

## 

![](media/image5.wmf){width="0.5354166666666667in"
height="6.740277777777778in"}

### ***2.3. Summary of Tasks***

Table 3 summarizes the task leads, facilities, and deliverables for
respective tasks. It also maps these tasks to the seven major objectives
outlined in Section 1.2 to demonstrate that the tasks have been
carefully planned to achieve the stated objectives of the SAVE-IT
program.

# 

# 

# 

Table 3. Summary of tasks, task leads, deliverables, and objectives

# ![](media/image6.wmf){width="6.6in" height="6.7375in"}  {#section-13 .unnumbered}

#  Section 3: Management Plan 

## 

## 3.1 Program Team Structure

The SAVE-IT program team will be comprised of diverse and geographically
dispersed team members. Consequently, In order to assure an effective
and efficient coordination of program activities, the management process
will include four key features: (i.) assurance of mutual clear
understanding of the program mission, tasks, schedule and budget via
written documentation; (ii.) measurement of performance and frequent
status reporting through common program management instruments; (iii.)
early identification and resolution of problems to assure conformance
with program objectives and still maintain high quality technical
performance within cost and schedule restrictions; and (iv.)
communication throughout the team of changes in requirements, concepts
and schedule. To accomplish the mission of the SAVE-IT Program, the team
organization will be structured as shown in Figure 8 (team structure
chart).

Delphi Delco Electronics Systems will coordinate the SAVE-IT team. As
such the Program Manager will be an employee of DDE. The program manager
will manage the day-to-day activities of the SAVE-IT program including
the deliverables, schedule timing, and budget. The Program Manager will
provide an overall technical lead of the program in addition to serving
as the primary liaison between the team members and the appointed
Government representative. Due to the significant effort required in
both human factors research and systems technology development a team
leader will be assigned to provide management focus in their respective
task areas (see Figure 5). Each task will have a task lead who will be
responsible for accomplishing the task objectives and completing the
deliverables on schedule. The task leads will be assisted by team
members and other technical assistants (e.g., graduate student research
assistants).

![](media/image7.wmf){width="7.4in" height="4.075in"}

Figure 5. Team structure chart

### 

# 

# 

# 

# 

# Appendices 

## 

## Appendix A: References

Boer, E. R. (2001). Behavioral entropy as a measure of driving
performance. [Proceedings of the first international driving symposium
on human factors in driver assessment, training and vehicle
designs.]{.underline} Aspen, CO. pp. 225-229.

Brown, T., Lee, J. D., & McGehee, D. V. (in press). An attention-based
model of driver performance in rear-end collision situations.
[Transportation Research Record.]{.underline}

De Waard, D. (1996). [The measurement of drivers' mental
workload.]{.underline} Unpublished doctoral dissertation.

Green, P. (1995). [Measures and methods used to assess the safety and
usability of driver information systems.]{.underline} FHWA-RD-94-088.

Green, P. (2000). Crashes induced by driver information systems and what
can be done to reduce them. [Proceedings of the 2000 international
congress on transportation electronics.]{.underline} Warrendale, PA. pp.
27-36.

Harwood, D. W., Council, F. M., Hauer, E., Hughes, W. E., & Vogt, A.
(2000). [Prediction of the expected safety performance of rural two-lane
highways.]{.underline} FHWA-RD-99-207.

Kantowitz, B. H., Levison, W. H., Hughes, W., Taori, S., Palmer, J.,
Dingus, T. A., Hanowski, R., Lee, J. D., Mears, B., & Williges, R.
(1997). [Development of prototype driver models for highway design: Task
A final project I work plan.]{.underline} Seattle, WA: Battelle Human
Factors Transportation Center.

Lee, J. D., Caven, B., Haake, S., & Brown, T. (2000). Are conversations
with your car distracting? Understanding the promises and pitfalls of
speech-based interfaces. [Proceedings of the 2000 international congress
on transportation electronics.]{.underline} Warrendale, PA. pp. 51-58.

Michon, J. A. (Ed). (1993). [Generic Intelligent Driver
Support.]{.underline} Washington, DC: Taylor & Francis.

Mykityshyn, M. & Hansman, R. J. (1993). [Electronic instrument approach
plates: The effect of selective decluttering on flight crew
performance.]{.underline} Paper presented at the Seventh International
Symposium on Aviation Psychology.

NHTSA DTNH22-99-H-07019 (2001). [Automotive collision avoidance system
field operational test.]{.underline}

Recartes, M. A., & Nunes, L. M. (2000). Effects of verbal and
spatial-imagery tasks on eye fixations while driving. [Journal of
Experimental Psychology: Applied, 6,]{.underline} 31-43.

Senders, J. W., Krisofferson, A. B., Levison, W. H., Dietrich, C. W., &
Ward, J. L. (1967). The attentional demand of automobile driving.
[Highway Research Record #195,]{.underline} Washington, DC: Highway
Research Board, 15-32.

Tsimhoni, O. & Green, P. A. (1999). Visual demand of driving curves as
determined by visual occlusion. [Vision in vehicles.]{.underline}

Wang, J. S., Knipling, R. R., & Goodman, M. J. (1996). [The role of
driver inattention in crashes: New statistics from the 1995
crashworthiness data system.]{.underline} The 40^th^ Annual Proceedings
of the Association for the Advancement of Automotive Medicine, October
7-9, 1996, Vancouver, British Columbia.

## 

## Appendix B: List of Figures

  ------------ ----------------------------------------------------- ----------
  **Figure**   **Caption**                                           **Page**

  1            The SAVE-IT system problem space.                     6

  2            SAVE-IT Conceptual model                              7

  3            Attention allocation to driving and non-driving tasks 13

  4            SAVE-IT proposal task chart                           18

  5            Team structure chart                                  20
  ------------ ----------------------------------------------------- ----------

## 

## Appendix C: List of Tables

  ----------- ----------------------------------------------------------- ----------
  **Table**   **Caption**                                                 **Page**

  1           Distraction and impairment countermeasure requirements      8

  2           Situation-specific countermeasure requirements (Safety      8
              warning countermeasures)                                    

  3           Summary of tasks, task leads, deliverables, and objectives  19
  ----------- ----------------------------------------------------------- ----------

#  

## 

## Appendix D: Acronyms Glossary

ACAS Automotive Collision Avoidance System

ACC Adaptive Cruise Control

ACM Adaptive CounterMeasures

AHS Autonomous Highway System

ANCOVA Analysis Of COVAriance

ANOVA Analysis Of VAriance

BAA Broad Agency Announcement

BRT Brake Reaction Time

BSW Blind Spot Warning

CD Compact Disk

DDE Delphi Delco Electronic Systems

DSM Driver State Monitor

DVD Digital Versatile Disk

ETS Eye Tracking System

FCW Forward Collision Warning

FOT Field Operational Test

GM General Motors

GPS Global Positioning System

HMI Human Machine Interface

HMIP HMI fusion Processor

HUD Head-Up Display

IESIM Industrial Engineering Hyperion Simulator at the University of
Iowa

ISS Integrated Safety Systems

IVI Intelligent Vehicles Initiative

LDW Lane Departure Warning

NADS National Advanced Driving Simulator

NHTSA National Highway Transportation Safety Administration

OEM Original Equipment Manufacturer

RDCWS Road Departure Crash Warning System

RSA Rollover Stability Advisory

RT Reaction Time

RT~C~ Reaction Time due to Cognitive distraction

RT~D~ Reaction Time due to Drowsiness impairment

RT~REQ~ Reaction Time REQuirement

RT~S~ Reaction Time due to Substance impairment

RT~T~ Reaction Time due to Telematics demand

RT~V~ Reaction Time due to Visual distraction

SAE Society for Automotive Engineers

SAVE-IT SAfety VEhicles using adaptive Interface Technologies

SDLP Standard Deviation of Lane Position

SDM Simulator Development Module at Iowa's NADS facility

SIREN Simulator for Interdisciplinary Research in Ergonomics and
Neuroscience at the University of Iowa

STA Situational Threat Assessment

TLC Time-to-Lane Crossing

TRC Transportation Research Center

TTC Time To Collision

UMTRI University of Michigan Transportation Research Institute

VIRTTEX VIRtual Test Track EXperiment

## 

[^1]: The most important types of driver distraction are cognitive and
    visual distractions. It is well known that two hands can perform two
    independent tasks and usually only one hand on the steering wheel is
    required for lane control. It is likely that the most detrimental
    effect of manual interaction with an interface is the glance at
    buttons and the cognitive load of carrying out the function. These
    aspects will already be captured by the visual and cognitive
    distraction algorithms. Similarly, the most distracting component of
    auditory messages is the cognitive load of processing the auditory
    material. Therefore, no further experiments are required or planned
    for manual distraction and auditory distraction.