SAfety VEhicles using adaptive Interface Technology (Task 9):

A Literature Review of

Safety Warning Countermeasures

> Prepared by

Matthew Smith, Ph.D. Harry Zhang, Ph.D.

Delphi Electronics & Safety Delphi Electronics & Safety

Phone: (765)-451-9816 Phone: (765)-451-7480

Email: <matt.smith@delphi.com> Email: <harry.zhang@delphi.com>

December 2004

Table Of Contents

[9.0 PROGRAM OVERVIEW 3](#program-overview)

[9.1 INTRODUCTION 11](#introduction)

[9.2 CRASH CLASSIFICATIONS AND THE ASSOCIATED SAFETY WARNING
COUNTERMEASURES
12](#crash-classifications-and-the-associated-safety-warning-countermeasures)

[9.2.1 Rear End Crashes 14](#rear-end-crashes)

[9.2.2 Road Departure Crashes 15](#road-departure-crashes)

[9.2.3 Intersection Crashes 16](#intersection-crashes)

[9.2.4 Lane Change/Merge Crashes 18](#lane-changemerge-crashes)

[9.2.5 Summary 19](#summary)

[9.3 FORWARD COLLISION WARNING (FCW) 21](#forward-collision-warning-fcw)

[9.3.1 Limitations on Human Perception
21](#limitations-on-human-perception)

[9.3.2 Algorithm Alternatives 22](#algorithm-alternatives)

[9.3.3 Braking Rates 29](#braking-rates)

[9.3.4 Driver's Brake Reaction Time 32](#drivers-brake-reaction-time)

[9.3.5 Cautionary Alerts 35](#cautionary-alerts)

[9.3.6 Driver Vehicle Interface 38](#driver-vehicle-interface)

[9.3.7 Nuisance Alerts 51](#nuisance-alerts)

[9.4 LANE DRIFT WARNING (LDW) 54](#lane-drift-warning-ldw)

[9.4.1 Algorithm Alternatives 54](#algorithm-alternatives-1)

[9.4.2 Driver Vehicle Interface 56](#driver-vehicle-interface-1)

[9.4.3 Nuisance Alerts 59](#nuisance-alerts-1)

[9.5 STOP SIGN VIOLATION WARNING (SSVW)
60](#stop-sign-violation-warning-ssvw)

[9.6 BLIND SPOT WARNING (BSW) 62](#blind-spot-warning-bsw)

[9.7 ADAPTIVE ENHANCEMENTS 65](#adaptive-enhancements)

[9.7.1 Forward Collision Warning (FCW)
67](#forward-collision-warning-fcw-1)

[9.7.2 Lane Drift Warning (LDW) 69](#lane-drift-warning-ldw-1)

[9.7.3 Stop Sign Violation Warning (SSVW)
70](#stop-sign-violation-warning-ssvw-1)

[9.7.4 Blind-spot Warning (BSW) 71](#blind-spot-warning-bsw-1)

REFERENCES.........................................................................................
72

# 9.0 Program Overview

Driver distraction is a major contributing factor to automobile crashes.
National Highway Traffic Safety Administration (NHTSA) has estimated
that approximately 25% of crashes are attributed to driver distraction
and inattention (Wang, Knipling, & Goodman, 1996). The issue of driver
distraction may become worse in the next few years because more
electronic devices (e.g., cell phones, navigation systems, wireless
Internet and email devices) are brought into vehicles that can
potentially create more distraction. In response to this situation, the
John A. Volpe National Transportation Systems Center (VNTSC), in support
of NHTSA\'s Office of Vehicle Safety Research, awarded a contract to
Delphi Electronics & Safety to develop, demonstrate, and evaluate the
potential safety benefits of adaptive interface technologies that manage
the information from various in-vehicle systems based on real-time
monitoring of the roadway conditions and the driver\'s capabilities. The
contract, known as SAfety VEhicle(s) using adaptive Interface Technology
(SAVE-IT), is designed to mitigate distraction with effective
countermeasures and enhance the effectiveness of safety warning systems.

The SAVE-IT program serves several important objectives. Perhaps the
most important objective is demonstrating a viable proof of concept that
is capable of reducing distraction-related crashes and enhancing the
effectiveness of safety warning systems. Program success is dependent on
integrated closed-loop principles that, not only include sophisticated
telematics, mobile office, entertainment and safety warning systems, but
also incorporate the state of the driver. This revolutionary closed-loop
vehicle environment will be achieved by measuring the driver's state,
assessing the situational threat, prioritizing information presentation,
providing adaptive countermeasures to minimize distraction, and
optimizing advanced collision warning.

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

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

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

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

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

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

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

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

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

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

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

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

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

Communication and Commonalities Among Tasks and Sites

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

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

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

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

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

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

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

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

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

The Content and Structure of the Report

The report submitted herein is a literature review report that documents
the research progress to date (March 1\--September 10, 2003) in Phase I.
During the period of March-September 2003, the effort has been focused
on the first Phase I sub-task: Literature Review. In this report,
previous experiments are discussed, research findings are reported, and
research needs are identified. This literature review report also serves
to establish the research strategies of each task.

## 9.1 INTRODUCTION

The countermeasures of the SAVE-IT program are divided into two major
categories. The first set of countermeasures represents the distraction
mitigation category. These systems mitigate excessive levels of
distraction by adapting the non-driving tasks to be commensurate with
the driving task demand. For example, if a driver is traveling along a
congested highway and is engaging in a difficult merging maneuver, an
incoming cellular phone call could be routed to voicemail. The other
major branch of the SAVE-IT program is the adaptive safety warning
countermeasures. These systems will adaptively modify safety-warning
countermeasures, such as forward collision warning (FCW) or blind-spot
warning (BSW) to the instantaneous attention allocation of the driver.
For example, if a driver is highly attentive to the forward-visual scene
and is not cognitively distracted, an FCW alert could either be delayed
or suppressed completely. Conversely, if a driver is highly distracted
and not attending to the forward-visual scene, an FCW alert could be
initiated much earlier or the driver could be notified if the lead
vehicle suddenly begins decelerating. Adaptive enhancements to safety
warning countermeasures will serve the dual goals of reducing nuisance
alerts and providing earlier warnings when the driver needs it most.
Early feedback from the ACAS FOT program appears to reveal that drivers
were relatively intolerant of warnings that occurred when they were
highly attentive.

The objective of this task is to improve safety-warning systems by
designing them to adaptively respond to workload, distraction, and task
demand information. During the early stages of this task a set of
countermeasures will be identified for further analysis in the SAVE-IT
program. The non-adaptive versions of these countermeasures will be
developed prior to an evaluation of how these countermeasures can be
enhanced using adaptive interface technology. The end product of this
task will be a set of adaptive and non-adaptive safety warning
countermeasures to be implemented in the second phase of the SAVE-IT
program. These countermeasures will be developed further in Task 11B
(Data Fusion: Safety Warning Countermeasures) before the System
Integration and final Evaluation.

This literature review report will be organized into eight subsections.
In Section 9.2 (Introduction) various countermeasure systems will be
described and discussed in relation to the relevant collision
statistics. Section 9.2 will conclude with a set of recommendations on
which countermeasure systems the SAVE-IT program should focus. The next
four subsections will describe these countermeasures systems in more
detail, including Forward Collision Warning (Section 9.3), Lane Drift
Warning (Section 9.4), Intersection Collision Warning (Section 9.5), and
Blind-spot Warning (Section 9.6). Section 9.7 will review the literature
on adaptive enhancements that have been made to collision-warning
systems and describe the potential enhancements that will be
investigated further in Task 9 (Safety Warning Countermeasures).

## 9.2 CRASH CLASSIFICATIONS AND THE ASSOCIATED SAFETY WARNING COUNTERMEASURES

This Section will discuss the breakdown of the police-reported
collisions and examine the safety warning countermeasures that have been
designed to prevent them. Each subsection of this section will address
the four major types of crashes, including rear-end, road-departure,
intersection, and lane-change/merge crashes. Because of the limited
budget for the SAVE-IT program and due to the fact that some types of
crashes are more prevalent or more directly related to driver
inattention, recommendations will be made for which types of safety
warning countermeasure systems the SAVE-IT task will focus on. Figure
9.1 displays a breakdown of the most prevalent types of crashes based on
Najm, Sen, Smith, and Campbell's (2003) analysis of the 2000 GES
light-vehicle crashes.

***Figure 9.1.** The four most prevalent crash-types of Najm, Sen,
Smith, and Campbell's (2003) analysis of the 2000 GES light-vehicle
crashes.*

The four categories of rear-end, intersection, road departure, and lane
change/merge accounted for a combined 85 percent of all police reported
light-vehicle crashes. These four categories also accounted for 58
percent of the 36 thousand collision-related fatalities in 1994 in the
United States (based on a report by the U.S. Department of
Transportation, 1997). The contribution of each of the four categories
to the national fatalities is displayed in Figure 9.2. From this figure
it is apparent that road departure collisions produce a disproportionate
rate of fatalities compared with the proportion of accidents, indicating
that road departure accidents may be more life threatening than the
other categories of accidents.

Automotive engineers in conjunction with the U.S. Department of
Transportation (DOT) have developed and refined a set of in-vehicle
countermeasure systems to mitigate against these collisions. Forward
Collision Warning (FCW) systems have been developed to address the
problem of rear-end collisions. GM, Delphi, the University of Michigan
Transportation Research Institute (UMTRI), and National Highway
Transportation Safety Administration (NHTSA) are currently engaged in a
field operational test (FOT) to refine and evaluate an FCW and Adaptive
Cruise Control (ACC) system. This system uses a forward looking radar to
detect the range, range-rate, and azimuth of objects in front of the
host vehicle, and warns the driver when there is threat of a rear-end
collision. FCW systems are currently available on the market based on
either radar- or laser-based sensors.

***Figure 9.2.** The proportion of 1994 roadway fatalities caused by the
four most prevalent types of collisions based on a U.S. Department of
Transportation (1997) report.*

Lane Drift Warning (LDW) systems have been developed to address the
problem of road departure collisions. LDW systems usually process an
image from a forward-looking camera to determine the position of the
host vehicle with respect to the roadway. Different systems have been
developed to address the problems of lateral drifting on straight roads
and road departure during curve negotiation. Whereas the former system
warns the driver when the host vehicle begins to drift out of the lane,
the latter system warns the driver when the host vehicle has excessive
speed for an upcoming turn. Visteon, UMTRI, and NHTSA are currently
engaged in a field operational test (FOT) to refine and evaluate a LDW
system.

Blind-spot warning (BSW) systems have been developed to address the
problem of lane-change/merge collisions. Lane-change/merge collisions
are a smaller problem in terms of the number of collisions and
fatalities; however, due to their relative simplicity and lower cost,
BSW systems are beginning to penetrate the market. BSW systems utilize a
side-looking sensor (usually either an ultrasonic-, laser-, or
radar-based sensor) that detects the presence of an object in the blind
spot of the host vehicle. Although these systems usually only *warn* the
driver when the host vehicle is about to change lanes, these systems may
*inform* the driver when the blind spot is occupied.

Perhaps the most complex collision problem for designing countermeasures
is the problem of intersection collisions. Intersection collisions are
multifaceted, comprised of several types of collisions that are quite
distinct in terms of the countermeasures designed for their prevention.
Intersection collisions are usually classified in terms of the type of
intersection (unsignalized versus signalized), the paths of the
colliding vehicles, (e.g., straight crossing path \[SCP\], left turn
across path \[LTAP\], right turn in path \[RTIP\] etc.), and whether a
traffic violation occurred. Many of these distinctions are important for
the design of countermeasures and different systems may be required to
address the different sub-classes of accidents. As a result of the
greater complexity of intersection collisions, safety warning
countermeasure systems designed to address this problem are currently at
an earlier stage of development than the other countermeasure systems.

This section will discuss the nature of the four most prevalent types of
crashes classes, including rear-end crashes (Section 9.2.1), road
departure crashes (Section 9.2.2), intersection crashes (Section 9.2.3),
and lane change/merge crashes (Section 9.2.4) and the safety warning
countermeasures designed to prevent them. This section will be concluded
with a summary of the countermeasure systems and a resultant set of
recommendations for the SAVE-IT program.

### 9.2.1 Rear End Crashes

Of the six million police-reported crashes that were reported in the
United States for the year 2000 involving at least one light
vehicle[^2], the single largest category of collisions was rear-end
accidents, accounting for 29.4 percent of the total (1.8 million
crashes) (Najm, Sen, Smith, & Campbell, 2003). By alerting drivers when
they approach a rear-end collision threat, forward collision warning
(FCW) systems have been developed in an attempt to reduce the number of
rear-end collisions.

Rear-end crashes are frequently divided into the two categories of lead
vehicle moving (RELVM) versus lead vehicle stationary (RELVS). RELVS
crashes are more common (59.1 percent), however in over half of these
cases the lead vehicle had recently decelerated to a stop (Najm et al.
2003). RELVM tend to be more severe than RELVS and are almost twice as
likely to involve a fatality (Knipling et al., 1992). In 26.5 percent of
cases, the lead vehicle is struck as it is decelerating, compared with
9.5 percent of rear-end collisions, where the struck lead vehicle is
traveling at a constant non-zero speed, and 1.1 percent of cases, where
the struck lead vehicle is accelerating (Najm et al. 2003). In 2 percent
of rear-end crashes the host vehicle is changing lanes when the
collision occurs and in 1.6 percent of rear-end crashes the lead vehicle
is changing lanes (Najm et al., 2003).

Knipling et al.'s (1992) statistical analysis suggested that over three
quarters of rear-end collisions are caused, at least in part, by an
inattentive driver. A more recent statistical analysis, based on the
2000 General Estimates System (GES) database, suggests that 65 percent
of rear-end collisions involve driver inattention, compared to 13
percent involving speeding, and 6 percent involving alcohol (Campbell,
Smith, & Najm, 2003). Although analyses of collision statistics are
limited by the ability of agencies to collect accurate information, they
appear to suggest that the majority of rear-end crashes occur because
the driver is not sufficiently attending to an unfolding event at an
inopportune time. When an unexpected event occurs, in front of an
inattentive driver, the driver may be too late in detecting the threat.
An FCW system may prevent a significant proportion of rear-end
collisions by providing vigilance when drivers are inattentive. Knipling
et al.'s analysis also reports that following too closely can contribute
to accidents (19 percent), suggesting that drivers may benefit from a
system that notifies them when they are driving beyond the constraints
of their own reaction time.

FCW systems function by predicting the future path of the host vehicle,
detecting the presence, location, and motion of objects in the forward
coverage zone, and alerting the driver when there is a threat of
rear-end collision. The module that is responsible for determining the
level of threat is referred to as threat assessment. There are several
different threat assessment algorithms that underlie the assessment of
threat and these will be discussed in Section 9.3.2. Because the
forward-looking sensor is usually unable to classify the type of object
that is responsible for the reflection of energy and because the
prediction of the future host vehicle path is prone to error, FCW
systems can frequently produce nuisance alerts (providing a warning when
there is little or no actual threat). In an effort to reduce the rate of
nuisance alerts, system designers are often faced with a difficult
problem of balancing the system coverage with the nuisance alert rate.
With the current state of technology, system designers are forced to
reduce the rate of nuisance alerts by tuning the algorithm to be less
sensitive in various situations. If the system designers are not
careful, the reduction of nuisance alerts may be achieved with too
greater cost of system effectiveness. However, adding the assessment of
driver state may help to alleviate this problem. When the driver is
attentive to the forward visual scene, alerts may be delayed or
suppressed. Delaying alerts when the driver is attentive is likely to
result in a reduced nuisance alert rate. However, unlike currently
available systems, this decrease in nuisance alerts will not necessarily
result in a reduction in system coverage. On the contrary, the system
could actually be designed to be more sensitive and provide earlier
warnings when the driver is not attentive.

The collision statistics demonstrate a substantial connection between
driver distraction and rear-end collisions, and therefore the SAVE-IT
program is likely to make significant progress by examining the
possibilities of providing adaptive enhancement to FCW systems. The
details of FCW systems will be discussed in depth in Section 9.3.

### 9.2.2 Road Departure Crashes 

Of the six million police-reported crashes that were reported in the
United States for the year 2000 that involved at least one light
vehicle^2^, over one fifth of the total (approximately 1.3 million
crashes) were of the road-departure category (Najm et al., 2003). Najm
et al. defined road-departure crashes (2003) as crashes wherein the
first harmful event occurs off the roadway. Whereas road departure
crashes represent about one fifth of all police reported crashes
involving at least one light vehicle, they represent over one third of
all crash-related fatalities (U.S. Department of Transportation, 1997).
Road departure crashes are therefore quite threatening to the drivers
and passengers involved. Mironer and Hendricks (1994) estimated that 21
percent of single vehicle roadway departure (SVRD) crashes were caused
by an evasive maneuver and 20 percent were caused by excessive speed.

Mironer and Hendricks attributed 9 percent of SVRD crashes to
inattention to lane tracking and 25 percent to driver impairment
(including intoxication, sleep, and physical illness, such as seizures).
However, a more recent statistical analysis suggests that driver
inattention may be involved in 25 to 35 percent of SVRD accidents
(Campbell, Smith, and Najm, 2003), whereas drowsy driving may only
account for 8 to 10 percent. The collision statistics appear to be quite
inconsistent across analyses. If a large percentage of SVRD accidents
are attributable to drowsy driving, a drowsy-driver-alerting system that
does not necessarily measure the vehicle position on the roadway may
provide significant benefit to the SVRD problem. However, to address the
more general problem of road departure, two specific systems have been
conceived. Pomerleau, Jochem, Thorpe, Batavia, Pape, Hadden, McMillan,
Brown, and Everson (1999) referred to the first type of system as a Lane
Drift Warning System (LDW). This type of system determines the position
of the host vehicle relative to the road, the geometric properties of
the upcoming road segment, the vehicle dynamic state, and the driver's
intention and warns the driver if it is determined that road departure
is likely.

The other type of system, Pomerleau et al. referred to as a Curve Speed
Warning System (CSW). This type of system is designed to prevent the
SVRD crashes that are caused by drivers losing control of their vehicle
due to excessive speed on curved roadway segments. Campbell et al.
(2003) estimated that that speeding and loss of control are involved in
approximately 25 to 41 percent of SVRD accidents. The CSW system that
Pomerleau et al. (1999) developed measures the vehicle position and
orientation relative to the upcoming curve, the stability properties of
the vehicle, the geometric properties of the upcoming curve, the
pavement conditions of the upcoming road, and the driver intentions to
determine whether loss of control was likely.

The CSW system is quite complex and requires a relatively sophisticated
array of sensors to measure all of the relevant variables (e.g., grade,
friction, and banking of the road section). In addition, the crashes
that the LDW system are designed to prevent may be more closely related
to driver inattention than those that the CSW system is designed to
prevent. The application of driver state information is likely to
provide considerable benefit to LDW systems, suppressing nuisance alerts
when the driver is attentive and perhaps providing earlier warnings when
an inattentive driver begins to drift out of the lane.

### 9.2.3 Intersection Crashes

Collisions at intersections account for approximately one quarter of all
police reported light vehicle crashes, with about 1.6 M intersection
crashes in the United States annually (Najm et al., 2003). Intersection
crashes also represent a significant problem in terms of fatalities,
contributing about one fifth of all crash-related fatalities in the
United States (U.S. Department of Transportation, 1997). Pierwowicz et
al. (2000) organized intersection crashes using the categories of
left-turn across path (LTAP) and straight crossing path (SCP). SCP
crashes could occur due to either a traffic violation or an inadequate
gap. The crashes were further organized according to the type of
intersection control (phased signal, stop sign/flashing red, or
yield/other). The percentages associated with the different categories
of intersection crashes are organized according to this scheme in Table
9.1.

Table 9.1. Pierwowicz et al.'s (2000) Classification Scheme of
Intersection Crashes

  --------------------- -------------- ---------------------- -------------
                        Phased Signal  Stop Sign/Flashing Red Yield/Other

  LTAP                  20.7           0                      3.1

  SCP: Inadequate Gap   0              29.1                   1.0

  SCP: Violation        23.3           18.2                   2.5

  Other                 2.1            0                      0
  --------------------- -------------- ---------------------- -------------

Note---LTAP represents Left turn across path and SCP represents Straight
crossing path.

According to Pierwowicz et al. (2000), 87 percent of the LTAP crashes
occurred in the green phase of the traffic signal, where the host
vehicle was not required to stop. To design countermeasures to prevent
this kind of accident, Pierwowicz developed a system with a wide-azimuth
long-range radar system mounted on top of the host vehicle. The threat
assessment module of this system calculated whether there was sufficient
gap to allow the host vehicle to complete the left turn. This warning
system was also developed to provide countermeasures against the SCP
inadequate gap accidents at stop signs. Although this sophisticated
system may in the future be capable of preventing a large percentage of
intersection collisions, it is still relatively early in the development
cycle. The prototype system of Pierwowicz et al. would currently be too
expensive to be practical.

Pierwowicz et al. also examined the system requirements for preventing
SCP crashes involving violations at signalized intersections. They
concluded that this type of warning system would require some type of
communication between the signal and the host vehicle so that the
warning system could discriminate between the different phases of the
traffic light. Consequently they eliminated that class of countermeasure
from further consideration. Unless a system could be developed to
discriminate between the different phases of the light based on the
wavelength of the emitted electromagnetic radiation or the location of
the source, such a system would require large-scale changes to the
roadway infrastructure. Infrastructure-based countermeasures, although
promising, are beyond the scope of the SAVE-IT program.

The final countermeasure system that Pierwowicz et al. developed
addressed the SCP crashes involving stop-sign violations. This was a
relatively simple system that warns the driver when it does not appear
that the host vehicle is decelerating in response to an upcoming stop
sign. The engineering requirements for this system are far less
demanding than those previously mentioned. A stop-sign violation warning
(SSVW) system need only detect the location of the vehicle with
reference to an electronic map and the speed and acceleration of the
host vehicle. Pierwowicz et al. estimated that over one quarter of the
SCP crashes involving violations were caused by driver inattention, and
the rate was much higher (69.9 percent) when the driver was intending to
turn. Under 10 percent of all SCP violation crashes involved deliberate
stop-sign violations. Because of the apparent link between stop-sign
violation crashes and driver inattention, and because the countermeasure
system designed to address these crashes is simple from an engineering
standpoint, SSVW systems appear to be the most feasible choice of
countermeasure against intersection crashes for the SAVE-IT program.

### 9.2.4 Lane Change/Merge Crashes 

Najm et al. (2003) reported that there are approximately half a million
police-reported crashes caused by lane change or merging maneuvers,
which represent just over 9 percent of all light-vehicle crashes in the
United States. Lane-change/merge accidents appear to contribute to
approximately 1 percent of all roadway fatalities in the United States
(U.S. Department of Transportation, 1997). The estimates of the extent
to which these crashes are related to driver distraction and inattention
vary greatly across references. Wang, Knipling, and Goodman (1996) for
example, estimated that only 5.6 percent of lane change/merge crashes
are caused by driver distraction and 17.2 percent were attributed to the
Looked-but-did-not-see (LBDNS) category. A more recent statistical
analysis, however, suggests that 33 to 50 percent of lane change
accidents involved driver inattention (Campbell et al., 2003).

The category of lane change crashes can include several different types
of lane change, such as merges, exits, passing, and weaving (Chovan,
Tijerina, Alexander, & Hendricks, 1994). Whereas 16 percent of lane
change crashes appear to be caused by an inattentive or impaired driver
unintentionally drifting into another lane (Young, Eberhard, & Moffa,
1995), the majority of lane change crashes appear to be caused by the
driver not observing the presence of another vehicle in the lane during
an intentional lane change. Chovan et al. (1994) estimated that in 64
percent of crashes, the driver responsible for the incident did not see
the principal other vehicle (POV). Many lane change accidents appear to
involve an inability to perceive the POV due to the blind spot. Systems
designed to protect drivers against this type of accident have already
appeared on the market. Most blind-spot warning (BSW) systems use a
short-range sensor such as ultra-sonic or radar sensors in order to
detect the presence of a vehicle in the adjacent lane. Because the
majority of lane-change crashes involve low relative speeds rather than
fast approaching POVs in the other lane, blind-spot sensors need only
observe a short range (e.g., one or two car lengths) in the adjacent
lane (Young et al., 1995).

The most challenging aspect of lane change warning systems appears to be
avoiding driver annoyance. The presence of a vehicle in the
coverage-zone of a blind-spot sensor is not an infrequent occurrence. In
high traffic situations, where the host vehicle is traveling either
faster or slower than the average speed of traffic, vehicles may pass in
and out of the blind-spot sensor zone with great regularity. If the
driver is notified every time this event occurs, it is likely that the
driver will become annoyed with the system. To prevent this from
occurring, systems should be designed to warn the driver only when there
is a threat of collision. A threat of collision only exists when a
vehicle is present in the host vehicle blind spot coverage area and the
host vehicle is currently or will soon be moving in the direction of the
blind spot. This can occur either when the driver intends to move into
the other lane or when the driver is unintentionally drifting into the
other lane perhaps due to inattention or impairment.

Whereas most current systems monitor the turn signal to determine the
driver intent to change lanes, it appears that most lane changes are not
preceded by turn signal activation. Lee, Olsen, and Wierwille (2004)
observed that drivers used the turn signal in only 44 percent of the
lane changes in a naturalistic lane change study. Driver state
information could potentially enhance BSW systems by providing more
sophisticated detection of driver intention to change lanes. The
detection of an intention to change lanes could be used as a criterion
to enable blind-spot warnings, providing earlier warnings when
appropriate and suppressing inappropriate alerts when a driver does not
intend to change lanes and is attentive.

### 9.2.5 Summary 

According to Wang et al. (1996) "NHTSA has recognized that available
statistics on driver inattention, including drowsiness, are not
definitive" (p. 3). Police reports are currently not designed to gather
information on the driver's level of attentiveness prior to the crash.
Even if these reports were designed for this specific purpose, it would
still be difficult to precisely assess attention-related variables.
Drivers are discouraged to admit guilt at the scene of an accident by
insurance companies and the psychological principle of self-serving bias
suggests that individuals typically do not blame themselves for
unpleasant consequences (Lerner & Miller, 1978). This bias toward
attributing negative consequences to external causation may result in an
under-reporting of driver inattentiveness in the collision statistics.
Although the collision statistics may underestimate the problem of
driver inattention, if one makes the assumption that this bias is
relatively constant across the different types of accidents, the
collision statistics provide relevant data to guide policy and research.
This section has briefly reviewed the collision statistics and safety
warning countermeasure literature to help support a decision on which
types of safety warning countermeasures the SAVE-IT program should
focus.

Wang et al. (1996) estimated that distraction-related crashes are
composed of 41 percent roadway departure crashes, 32 percent rear-end
crashes, 18 percent intersection crashes, and 2 percent
lane-change/merge crashes, leaving the remaining 7 percent distributed
across other types of accidents. Looked-but-did-not-see crashes are
composed of 64 percent intersection crashes, 16 percent rear-end
crashes, 7 percent lane-change/merge crashes, and 1 percent roadway
departure crashes, leaving the remaining 19 percent distributed across
other types of accidents. This analysis suggests that roadway departure,
rear-end, and intersection crashes are clearly relevant to driver
distraction and therefore to the SAVE-IT program. Although
lane-change/merge crashes are less frequent and perhaps less directly
related to driver distraction, the SAVE-IT program could contribute to
BSW design by exploring the utilization of more intent-detection
algorithms. Based on the analyses of the collision statistics that have
been discussed in this section and on the development of countermeasure
technology, this literature review will further explore the following
countermeasure systems:

-   Forward Collision Warning (FCW)

-   Lane Drift Warning (LDW)

-   Stop Sign Violation Warning (SSVW)

-   Blind Spot Warning (BSW)

Rear-end collisions are the most prevalent type of collision in the
United States and they appear to be closely related to driver
distraction. In addition, the ACAS FOT program is currently revealing
that the problem of reducing FCW nuisance alerts to a level that is
tolerable to drivers is a tremendous challenge. For these reasons, the
literature review of Task 9 will focus most extensively on FCW research.
Although the other types of warning systems will be reviewed, there
appears to be less literature on these systems available, and the
corresponding sections will be briefer. The next four sections will
review the literature on FCW, LDW, SSVW, and BSW respectively.

## 9.3 FORWARD COLLISION WARNING (FCW)

Forward collision warning systems are designed to protect the driver
against the single largest category of police reported collisions.
Converging crash report statistics suggest that rear-end collisions
account for over a quarter of all police reported collisions. Rear-end
collisions are of particular relevance to the SAVE-IT program because
they also appear to be the category of collisions that are most
attributable to driver inattention and distraction. In addition to
driver inattention, some researchers have suggested that rear-end
collisions may be partially attributable to limitations of the human
perceptual system (e.g., Mortimer, 1990; Hoffman, 1968). The first
subsection will discuss some of the literature regarding the human
perceptions of longitudinal proximity (Section 9.3.1). To support the
development of FCW countermeasures, this section will continue by
discussing FCW algorithm alternatives (Section 9.3.2), followed by a
discussion of two important inputs into the algorithm: braking rates
(Section 9.3.3) and brake reaction time (Section 9.3.4). Section 9.3
will continue with a discussion of cautionary warning stages (Section
9.3.5) and a summary of the driver vehicle interface research and recent
developments (Section 9.3.6). Section 9.3 will conclude with a
discussion of nuisance alerts (Section 9.3.7).

### 9.3.1 Limitations on Human Perception

Although the human visual system is extremely sophisticated, it has
several limitations. Psychophysicists have a long history of measuring
perceptual thresholds, and have amassed a converging body of research
that documents the limitations of the perception of motion. Mortimer
(1990) examined the perceptual limitations relating to rear-end
collisions. He suggested that when headway[^3] is large (400 ft), the
rate of change of visual angle (expansion rate) of the lead vehicle
tends to be sub-threshold and drivers must rely on their perception of a
change in visual angle. Mortimer's review of the literature revealed
that the Weber fraction[^4] for the perception of a change in visual
angle is usually between two and three percent. At shorter headways and
larger closure rates, the human perceptual system can utilize the
perception of expansion rate before a change in visual angle is
perceived. Mortimer's literature review suggests that the psychophysical
threshold for expansion rate is approximately 0.2 deg/s. Hoffman (1968)
claimed that even at high relative-velocity situations, drivers are
unable to discriminate between three or four categories of relative
velocity, proposing that driving performance might be improved if
drivers have access to a display of relative speed information.

Converging psychophysical evidence strongly suggests that the human
visual system is insensitive to the acceleration of objects. Although
acceleration can potentially be inferred if the visual system perceives
a large change in velocity, there is no direct perceptual mechanisms to
support the perception of acceleration (Watamaniuk & Duchon, 1992).
Psychophysical evidence suggests that the human visual system temporally
integrates velocity information in order to filter out noise, directly
counteracting the perception of acceleration (Watamaniuk & Heinen,
1999). Watamaniuk and Heinen (1999) observed that pursuit eye movements
lag behind accelerating targets, intermittently requiring saccades to
catch up with the target. This suggests that the visual system is
insensitive to acceleration.

An insensitivity to visual acceleration has clear implications for
rear-end collisions. Just as the human eye lags behind an accelerating
target, drivers' braking responses to a rapidly decelerating lead
vehicle may also lag behind the deceleration requirement to avoid
collision. Although deceleration may be inferred (e.g., from the
presence of brake lamps), the driver of a host vehicle has little
information regarding the magnitude of the lead vehicle braking action.

Following D. Lee (1976), many psychologists have accepted the theory
that humans and animals can perceive time-to-collision directly. Rather
than dividing distance by relative speed, both of which are perceived
with poor precision, Lee suggested that humans and animals directly
perceive time-to-collision through the ratio of visual angle over
expansion rate. This ratio, referred to as , is approximately equivalent
to the time before the object will collide with the observer's eye
position. This elegant theory provides an explanation for the precise
collision control observed in both humans (e.g., hitting baseballs) and
animals (e.g., gannuts diving beneath the ocean surface to catch fish).
However, research has provided data that is somewhat contradictory.
Smith, Flach, Dittman, and Stanard (2001) demonstrated that many of the
trends observed in the literature can be fit better with a simple
expansion rate model, or a more flexible model that sums the weighted
inputs of visual angle and expansion rate.

Considering the imprecision of visual motion perception and the
inability to perceive acceleration, it is likely that drivers use a
highly attuned perception-action process that relies on closed-loop
feedback. In the rare circumstances that ballistic (open-loop) responses
are required (e.g., emergency braking), it is likely that through
experience, drivers have learned the patterns of information (such as
angle, expansion rate, brake lights, and lead-vehicle pitch) that
correspond with the requirement for full braking.

### 9.3.2 Algorithm Alternatives

Industry, academia, and government have proposed several collision
warning algorithms. Those alternatives include algorithms based on the
criteria of time-headway, time-to-contact, and the underlying kinematic
constraints (i.e., the potential of the host vehicle to decelerate).
Whereas time-headway algorithms (e.g., the Motorola FCW system presented
at the Driving Assessment 2001 conference) offer simplicity and are
consistent with current driving-manual recommendations for safe driving,
they are insensitive to relative velocity. Time-to-contact algorithms
(e.g., suggested by Van der Horst & Hogema 1993, & Graham & Hirst 1994)
are based on D. Lee's theory of direct time-to-contact perception, and
are sensitive to relative velocity. Algorithms based on kinematic
constraints (e.g., CAMP 1999 algorithm, NHTSA algorithm for the ACAS
FOT, GM algorithm for the ACAS FOT program) offer increased accuracy by
calculating the moment that the driver must initiate braking, given an
assumed reaction time and host-vehicle deceleration response. Because
this class of algorithm considers both reaction time and the capacity of
the host vehicle to decelerate, it offers a more comprehensive model
than the other two categories. Algorithms of this class are highly
dependent on assumptions about driver reaction time and braking rate.

#### 9.3.2.1 Time-headway Criterion

Wheatly and Hurwitz (2001) described an algorithm using time-headway as
a criterion for collision warning that they were investigating at
Motorola. Time-headway ($TH$) is defined as the range between the front
bumper of the host vehicle and the rear bumper of the lead vehicle
($R$), divided by the velocity of the host vehicle ($VH$).

$$TH = RVH$$

Most Department of Motor Vehicles recommend that drivers adopt a minimum
two-second time headway for safe driving practice. Time-headway is a
useful metric because it corresponds to the amount of time the driver of
a host vehicle has to match the braking profile of the lead vehicle.
Assuming that both vehicles are traveling at the same speed and that the
host vehicle perfectly matches the average braking rate of the lead
vehicle, time-headway is precisely equivalent to the amount of time
required for the host vehicle to initiate braking in order to avoid
collision.

To illustrate this relationship, the range required for collision
avoidance ($R\text{CA}$) is equal to the stopping distance for the host
vehicle ($D\text{HS}$) minus the stopping distance for the lead vehicle
($D\text{LS}$). This can be expressed in terms of the host vehicle
velocity ($VH$), the average acceleration of the host vehicle ($AH$),
the lead vehicle velocity ($VL$), the average acceleration of the lead
vehicle ($AL$), and the response time of the host driver ($\text{RT}H$):

$$R\text{CA} =$$

Assuming $VH = VL$, $AH = AL$, and therefore that the
quantities$\sqrt{2VH^{2}AH}$and $\sqrt{2VL^{2}AL}$are equivalent, the
expression can be reduced to

$R\text{CA} = VH \cdot \text{RT}H$ or

$$\text{RT}H = R\text{CA}VH$$

This set of equations illustrates that time-headway ($TH$) is equal to
reaction time ($\text{RT}H$) when the range to the lead vehicle ($R$) is
equal to the collision avoidance range ($R\text{CA}$). As a general rule
of thumb, drivers should drive within the constraints of their reaction
time. As GM implemented for the ACAS FOT, a time-headway criterion can
be used to trigger cautionary alerts, in order to make the boundaries on
the safe field of travel[^5] more explicit. A smaller time-headway
criterion could also serve as an imminent warning (e.g., Wheatley &
Hurwitz, 2001).

There are several limitations of the time-headway criterion for an
imminent criterion. Firstly, the time-headway rationale assumes that the
two vehicles are initially traveling at the same speed. Horowitz and
Dingus (1992) estimated that in 75 percent of rear-end crashes, the lead
and host vehicles are in an uncoupled state (not a continuous
car-following event) before the rear-end collision occurs. Furthermore,
Najm et al. 2003 estimated that the lead vehicle is traveling at a
constant and slower speed before 10 percent of rear-end collisions and
in approximately 29 percent of rear-end collisions the lead vehicle was
stationary before the host vehicle entered the scenario. Consequently,
it appears that the assumption of equivalent lead and host vehicle
speeds may be violated in a significant number of cases. Another
assumption that may be limited is that the host vehicle matches the
average braking rate of the lead vehicle. While it seems likely that
host-vehicle braking is somewhat modulated as a function of the lead
vehicle activity, the perceptual limitations that were discussed in the
previous section suggest that drivers may perceive rates poorly. Given
that the human visual system is insensitive to the rate of deceleration,
it is likely that higher lead-vehicle braking rates may (at least
initially) be perceived poorly. If the lead vehicle produces an
immediate maximal braking response, the host vehicle may significantly
lag behind the braking profile of the lead vehicle. Drivers may also
overreact to lower braking rates when the lead-vehicle brake lamps are
observed. Time-headway may be an overly simplistic algorithm to capture
all rear-end pre-crash collision scenarios. However, if it is used only
in cases where the two vehicles are coupled with similar velocities,
time-headway may be a useful criterion, especially for cautionary (lower
severity) warnings.

#### 9.3.2.2 Time-to-collision Criterion

Using a time-to-collision criterion for triggering forward collision
warning alerts is based on the theory that humans directly perceive
time-to-collision (Lee, McGehee, Brown, & Raby, 1999). Several European
researchers have recommended using time-to-collision as the criterion
for forward collision warning systems (e.g., Van der Horst & Hogema,
1993; Graham & Hirst, 1994). Graham and Hirst reported that drivers tend
to initiate braking at a 4-s before collision[^6], and therefore a
criterion of around 5-s should be used to allow for driver's brake
reaction time.

Time-to-collision is equal to the range ($R$) between the front bumper
of the host vehicle and the rear bumper of the lead vehicle, divided by
the range rate ($\overset{}{R}$, expressed as a positive value).

$$TC = - R\overset{}{R}$$

The difference between time-to-collision and time-headway is that
closure speed rather than host-vehicle speed is used in the denominator
and therefore the time-to-collision represents the amount of time before
the headway will be reduced to zero. When the host and lead vehicles are
traveling at similar speeds, time-to-contact approaches infinity, and
when the host vehicle is traveling at a slower speed the
time-to-collision does not logically exist.

Like time-headway, one advantage of using time-to-collision is its
simplicity. If one accepts the theory that drivers directly perceive
time-to-collision, using time-to-collision as the criterion for an FCW
algorithm seems reasonable. Unlike time-headway, a
time-to-collision-based algorithm is extremely sensitive to relative
velocity. A weakness may be that it is only sensitive to relative
velocity, because provided that the lead vehicle is traveling at a speed
greater than or equal to the host vehicle, the algorithm will detect no
threat, even in the extremely dangerous situation where bumpers are mere
inches apart. Another potential limitation is that the time-to-collision
criterion does not take into account instantaneous vehicle
accelerations. Thus, a lead vehicle that is decelerating at maximal rate
is treated as being equivalent to a vehicle that is traveling at a
constant rate, or even accelerating. This limitation could potentially
be removed, if the algorithm used a second-order time-to-collision
formula. The formula would lose its simplicity if it were designed to
account for the fact that decelerations cease when vehicles come to a
stop. Even this more complex formula would still fail to account for
inertial braking constraints that result in vehicles requiring more time
(and distance) to reduce larger velocity differences.

Hirst and Graham (1997) proposed that forward collision warning systems
adopt a time-to-collision criterion that assesses a speed penalty so
that the time-to-collision threshold is higher for larger speeds.
Although the speed penalty addresses some limitations, after modeling
various collision warning algorithms, Brown, Lee, and McGehee (2001)
demonstrated the counterintuitive result that this formula has the
potential of leading to more severe collisions at larger initial
headways.

The basic time-to-collision formula offers simplicity as an advantage,
however, as more modifications are made to the basic formula to
accommodate the limitations, the algorithm begins to lose this
advantage. Whereas the simple formula could be used as one of the
cautionary warning criteria (similar to GM's implementation for the ACAS
FOT program), the various adjustments used to compensate for initial
over-simplification become burdensome, perhaps suggesting that a more
comprehensive model be used as a starting point. In this regard,
algorithms based on the inertial braking constraints may be a more
effective alternative.

#### 9.3.2.3 Kinematic Constraints Criterion

Lee et al. (1999) used the term "kinematic constraints" to refer the
algorithms that are based on the inertial constraints of the vehicles.
The algorithms discussed under this umbrella make the following
assumptions:

1.  The lead vehicle will continue its current rate of
    acceleration/deceleration until it stops.

2.  The host vehicle will respond after a specified brake reaction time,
    by braking at a specified constant deceleration rate.

Using the instantaneous lead- and host-vehicle speeds and accelerations,
these algorithms calculate the minimum collision-avoidance range that
will result in the host vehicle missing the lead vehicle by a specified
margin.

Burgett, Carter, Miller, Najm, and Smith (1998) proposed one of the
first versions of this algorithm. Their algorithm assumed that the host
vehicle will brake at a rate of -0.75 g after a brake reaction time of
1.5 s, and calculated the minimum collision-avoidance range to allow the
host vehicle to miss the lead vehicle by 6.67 ft. Unlike the other
algorithms that will be discussed, this algorithm assumed that the host
and lead vehicles were initially traveling at the same velocity. A
valuable contribution of this paper was that it demonstrated the
importance of considering the three different collision-avoidance zones:

1.  The warning is issued after the lead vehicle stops.

2.  The warning is issued before the lead vehicle stops and the lead
    vehicle stops before the host vehicle.

3.  The warning is issued before the lead vehicle stops and the host
    vehicle stops before the lead vehicle.

These distinctions are important because they determine which set of
equations should be used. If the lead vehicle stops before the host
vehicle, then the equations must compare lead and host vehicle stopping
distances, to make sure the host vehicle stops before colliding with the
stationary lead vehicle. However, if the host vehicle will stop before
the lead vehicle, the two vehicles could potentially come into contact
while they are both still in motion, and the equations evaluate the
distance required to reduce the closing speed to zero. Figure 9.3
illustrates the distinction between the three scenarios.

Following this approach, the Collision Avoidance Metrics Partnership
(CAMP)[^7] developed a collision avoidance algorithm, based on human
factors research (Kiefer, LeBlanc, Palmer, Salinger, Deering, & Shulman,
1999). Kiefer et al. instructed drivers on a test track to brake at the
last moment to a stationary or braking lead vehicle target. Based on the
variability of the moment of braking, Kiefer et al. concluded that
drivers responded at relatively consistent levels of required
deceleration[^8]. For this reason and because required deceleration is
connected to the fundamental kinematic variable underlying collision
avoidance, they recommended that the algorithm should use required
deceleration as a criterion for forward collision warning.

***Figure 9.3.*** Burgett et al.'s (1998) three collision avoidance
zones, including where the lead vehicle is stationary (top), where the
lead vehicle stops before the host vehicle (middle), and host vehicle
stops before the lead vehicle (bottom). The arrows illustrate where the
threat of collision is being evaluated. In the first two scenarios, the
algorithm evaluates the threat at the moment that the host vehicle
stops, however, in the bottom scenario the algorithm evaluates the
threat when the host and lead speed are equal.

Like the Burgett et al. algorithm, the CAMP algorithm made the same two
assumptions that were listed above and calculated the minimum
collision-avoidance distance based on the three collision-avoidance
zones. However, the CAMP algorithm differed from the Burgett et al.
algorithm in two respects: the assumption of equivalent lead and host
vehicle speeds was removed, and rather than using a fixed (0.75 g) value
for the assumed host vehicle deceleration response, the deceleration was
a function of other parameters. The function for estimating the host
vehicle deceleration response was modeled on the last-minute braking
data that they collected and is as follows (in units of ft and s):

$AH$=-5.308+0.685($AL$)+2.570(if $VL$\>0)-0.086($VH$-$VL$)

The estimated host vehicle deceleration becomes greater (more negative)
when lead vehicles decelerate, when lead vehicles are stationary, and as
the closing velocity becomes smaller. The fact that the threshold for
braking should be greater (later) when the lead vehicle is decelerating
at a greater rate appears counterintuitive. However, if one assumes that
drivers were braking based on their insensitivity to deceleration, it
would follow that drivers would have a larger required deceleration when
the lead vehicle decelerates at a constant rate. If human visual systems
are insensitive to deceleration, higher rates of lead vehicle
deceleration will likely be underestimated, requiring a larger braking
rate to compensate for later braking. An alternative explanation could
be that the delay in drivers reacting to the lead-vehicle resulted in
more urgent situations for higher braking rates. If either explanation
is true, it may reveal a weakness in the CAMP approach. Basing an
algorithm on driver perception of threat, as indicated by last-minute
braking responses, will mimic the performance limitations of the driver,
rather than maximally enhancing the detection of threatening scenarios.
In other words, just because humans cannot perceive lead vehicle
deceleration, does not mean an algorithm should not. Given that over
half of rear-end collisions involve decelerating lead vehicles (Najm et
al., 2003), perhaps earlier detection of lead vehicle deceleration is
warranted.

In order to promote further algorithm development and the open flow of
communication, the National Highway Transportation Safety Administration
(NHTSA) developed a public-domain algorithm for the ACAS FOT program.
This algorithm is documented in a report by Brunson, Kyle, Phamdo, and
Preziotti (2002). Like the other algorithms discussed in this category,
the imminent-level of the NHTSA algorithm makes the same three
assumptions and divides the problem into Burgett et al.'s three
collision-avoidance zones. Like the previous generation of NHTSA
algorithm (Burgett et al., 1998), this algorithm uses a fixed assumed
host vehicle braking response (-0.55 g). Furthermore, the assumption of
equivalent lead and host speeds was removed and many implementation
details, such as nuisance alarm reduction techniques were added.

The algorithms that are based on kinematic constraints appear to most
comprehensively model the range of pre-crash scenarios. Lead vehicle
deceleration rate, host vehicle braking limitations, and driver reaction
time are represented in the equations. Not only does this type of
algorithm model the reality of collision-avoidance with the greatest
fidelity of the three classes of algorithms, it also appears to directly
map onto the underlying nature of what represents a collision threat
(Kiefer et al., 1999). As a situation requires increasing levels of host
vehicle braking, the situation becomes increasingly threatening. When
the situation requires a level of host vehicle deceleration that is
equal to the maximal level of braking possible, it is literally the last
chance for the driver to brake in order to avoid a collision. Warning
the driver after this moment would no longer prevent a collision (unless
the driver could steer in such a way to avoid the collision), though it
could potentially reduce crash energy.

One potential limitation of this category of algorithm is that, like the
time-to-collision algorithms, it does not detect any level of threat
until either range-rate or range-acceleration is negative. Thus, if one
assumes no minimum collision-avoidance range, the algorithm may register
no threat even when the vehicles are mere inches apart if there is no
relative closure. Although this situation is not registered as a threat
by this type of algorithm, it is actually quite threatening. Any sudden
change in the situation, such as the lead vehicle braking, and collision
may be unavoidable. This type of threat is not instantaneous, but rather
probabilistic in nature. Driving beyond the constraints of human brake
reaction time, represents a threat in that something may happen that
leaves insufficient time for the driver to respond. This limitation
could be remedied in several ways. One approach would be to adopt a
minimum miss distance, such as the 6.67 ft used by Burgett et al. Rather
than assessing the minimum collision-avoidance range for avoiding a
collision, the Burgett et al. algorithm assesses the minimum
collision-avoidance range for avoiding a collision by 6.67 ft. This
ensures an alert when range drops below 6.67 ft. The second generation
NHTSA algorithm also used this miss distance. The GM algorithm in the
ACAS FOT also represented probabilistic by presenting a cautionary alert
level that was based on time-to-collision and the time-headway
algorithms. By providing a cautionary alert when drivers become too
close to the lead vehicle, drivers are warned before an imminent alert
becomes necessary. This minimizes the chance that drivers will drive
beyond their brake reaction time capability.

Most algorithms appear to adopt a brake reaction time of approximately
1.5 s, however, these algorithms can differ greatly because of the
different choices of assumed host-vehicle braking rate. Burgett et al.'s
algorithm employs a value of -0.75 g, which seems to represent the
absolute maximum rate at which the vehicle can attain (given that this
is the average rate after braking is first applied). Adopting a value
this high may represent more of an effort to reduce crash energy than an
attempt to prevent all rear-end crashes. At the other extreme is the
CAMP algorithm, which tends to adopt smaller values (e.g., for a host
vehicle approaching a stationary lead vehicle at 40 mph the assumed host
braking response would be 0.32 g). The CAMP required-deceleration
function may lead to an overly sensitive algorithm that produces an
excessive number of nuisance alerts. Krishnan and Colgin (2002) used a
Monte Carlo simulation to compare the GM ACAS FOT algorithm with the
CAMP and NHTSA ACAS FOT algorithms and demonstrated that the CAMP
algorithm had a greater probability of producing early and nuisance
alerts.

### 9.3.3 Braking Rates

The previous subsection discussed alternatives for the imminent-level
algorithm and described how these algorithms use assumed values of brake
reaction time and braking rate. This subsection will evaluate braking
rates observed across several studies. One way to conceptualize the
"kinematic constraints" algorithm is that as the situation becomes more
severe the braking rate required for avoiding collision increases. When
the required braking rate reaches a threshold that represents the
predicted driver emergency response, the driver should be warned. If the
algorithm is conceptualized in this manner, as it was by the 1999 CAMP
program, the specified braking rate represents the threshold that
differentiates between non-alerts and alert situations. For the purposes
of an FCW algorithm, braking rate will be defined as the average
deceleration that occurs between the moments when the driver first
depresses the brake pedal and when closure rate is reduced to zero. It
is important to differentiate braking rate from the maximum or peak
deceleration rate, which is larger, because of the time required to
increase the deceleration rate from near zero to the maximum rate.

Brown et al. (2002) reviewed previous work to verify that realistic
deceleration for an emergency-braking situation range between -0.4 g and
-0.85 g. Their analysis revealed a mean of -0.5 g, a mean maximum of
-0.75 g and an overall maximum of -0.86 g. If braking rate is defined as
the average rate over the period of time starting with the initial brake
depression, then clearly -0.85 is an excessively large value. Even -0.75
g (the average maximum across subjects) would likely be too large for an
average braking rate if the goal of the threat assessment algorithm were
collision avoidance. However, Burgett et al. (1998) proposed an
algorithm adopting a specified braking rate of -0.75 g. If reduction of
collision energy was the goal of this algorithm, rather than collision
avoidance, such a value might seem reasonable. In a comparison of a
-0.75 g (late) criterion with a -0.4 g (early) criterion for collision
warning, Lee, McGehee, Brown, and Reyes (2002) observed that the late
criterion resulted in a 50 percent reduction in collisions and an 87.5
percent reduction in collision energy, compared with the early criterion
which resulted in an 80.7 percent reduction in collisions and a 90.6
percent reduction in collision energy. Although the early collision
warning resulted in fewer collisions than the late collision warning, in
an on-road functioning system, the earlier warning probably would result
in a greater number of nuisance alerts. This tradeoff between reducing
collisions and reducing nuisance alerts is an important consideration
for selecting a braking rate criterion.

The 1999 Forward Collision Warning CAMP project selected the braking
rate criterion based on the results of an experiment that they
conducted. The experiment was conducted on a test-track and instructed
participants to "brake with hard braking intensity or pressure" at the
latest possible moment in response to a lead vehicle. Trials included
lead vehicle decelerations of -.15 g, -.28 g, and -.39 g and stationary
lead vehicles. Kiefer et al. measured the required deceleration^8^ at
the moment that the driver initiated braking.

In this study, the average required decelerations varied between -0.15
and -0.45 g, with larger required decelerations for greater lead vehicle
deceleration rates and faster initial

host vehicle speeds. Average actual decelerations[^9] followed a similar
pattern as the required decelerations, but were greater, with averages
varying between -0.18 and -0.54 g. The average peak deceleration values
varied between -0.75 and -0.9 g. As discussed in the previous section,
CAMP selected a braking rate criteria that varied as a function of range
rate, lead vehicle deceleration rate, and whether the lead vehicle was
stationary or not. This study provides a good indication of the absolute
magnitudes of braking in a real vehicle, as opposed to a driving
simulator. In particular, it demonstrated that in the most severe
condition (highest speed with greatest lead vehicle braking rate), the
average actual braking rate of participants was -0.54 g. This provides a
useful indication of how hard drivers are likely to brake in an
emergency rear-end collision situation on dry pavement.

It would seem that a simple but effective solution would be to adopt a
fixed braking rate criterion across all kinematic conditions. The
physical capacity for the host vehicle to brake varies relatively little
across the host vehicle speeds in which a warning system would function
(usually greater than 25 mph) and is independent of lead vehicle speeds
and acceleration rates. If we can accurately predict the maximum
capacity of a vehicle to decelerate, then using that braking rate
provides an appropriate threshold for differentiating between
threatening and non-threatening events. The NHTSA algorithm, that was
developed for the ACAS FOT program adopted a fixed braking rate of -0.55
g. The combination of a -0.55-g braking rate and a 1.5-s brake reaction
time was selected based on a Monte Carlo simulation that compared false
alarm and hit (correct positive) rates across different parameter
values.

One parameter that does affect a vehicle's potential to decelerate is
the tire-road coefficient of friction. Assuming a 20 percent level of
wheel-slip, the coefficient of friction can vary between 0.1 (wet ice)
and 0.8 (bare and dry surface) due to weather conditions (Norwegian
Public Roads Administration, 2000). Depending on a warning system's
capacity to detect or estimate the coefficient of friction of a roadway,
the algorithm could adapt the brake rate criterion as a function of the
conditions. However, when large changes to the algorithm parameters are
based on unreliable evidence, such as temperature and windshield wipers,
a large number of nuisance alerts may result. This was recently observed
and contributed to an almost three-fold increase in the number of
nuisance alerts in the Stage 3 pilot testing of the ACAS FOT program
(Ervin, Sayer, & LeBlanc, 2003).

### 9.3.4 Driver's Brake Reaction Time

Brake reaction time is an important variable for determining the timing
of an alert. Brown et al. (2002) demonstrated that algorithms that are
based on kinematic-constraints are quite sensitive to the error in
estimated brake reaction time. For the purposes of forward collision
warning, brake reaction time will be defined as the time between the
initial event (e.g., vehicle initiates braking) and the time that the
driver's foot first comes into contact with the brake pedal. Because the
FCW system may not be able to determine whether an evasive steering
maneuver is possible, threat assessment must conservatively assume that
the driver must engage in a braking maneuver, and therefore use brake
reaction time as an input to threat assessment rather than steering
reaction time (Lee, McGehee, Brown, & Raby, 1999). This section will
summarize the work conducted in the literature that examines reaction
times.

#### 9.3.4.1 Reaction Time Paradigms

Experimental psychologists have amassed a large amount of literature on
reaction time to simple laboratory stimuli. An important distinction is
made in the literature between simple and choice reaction time. In the
simple reaction time task, subjects respond to a simple stimulus, such
as a light or a sound, by making some overt response, such as pushing a
button. Subjects are instructed to make a single type of response after
they observe a single type of stimulus, thus, no choice is required.
Simple reaction time can be conceptualized as a measure of how quickly
neural information travels through the body, from the senses to the
brain and then from the brain to the limb that makes the response. In
the choice reaction time task, as the name suggests, subjects are
required to make a choice between different response alternatives. One
of a set of two or more stimuli is presented to the subject and the
subject must make a corresponding choice between one of a set of two or
more response alternatives.

Simple reaction time depends on several variables. In Wickens' (1992)
review of the literature, the variables that appear to have the most
significant impact on simple reaction time are stimulus modality (e.g.,
visual vs. auditory), stimulus intensity (e.g., how loud or how bright),
and temporal uncertainty (e.g., how likely the stimulus is to be
presented at the current moment). In simple reaction time experiments,
subjects respond to visual stimuli 40 ms slower than audio or tactile
stimuli, and near the sensory threshold, low intensity stimuli tend to
be responded to more slowly, than stimuli that are far above threshold.
Above 300 ms, longer durations between stimuli tend to produce longer
reaction times (Niemi & Naatanen, 1981), probably because subjects have
less reliable information with which to prepare their responses. When
subjects are prepared for the stimuli for 300 ms, simple reaction times
have been recorded of less than 160 ms (Gottsdanker, 1975). The
probability that the stimuli will occur in a specified time interval
also impacts simple reaction time. Naatanen and Koskinen (1975)
discovered that simple reaction times increased by about 40% for a
stimulus that occurred one out of every four trials.

Although a driver's response to a warning is more complex than simple
reaction time, the variables that influence simple reaction time are
likely to have an impact on brake reaction time to collision warnings.
For example, if the collision warning system is considered to be a
reliable source of information with relatively few nuisance alerts, it
is more likely that the driver will respond more quickly to the warning
stimuli. Brake reaction times, however, tend to be of longer duration
than simple reaction times. One possible explanation for this difference
is that brake reaction times involve choices. Rather than blindly
depressing the brake pedal, the driver must perceive the evolving event,
and decide what kind of response is required. For example, if the driver
receives a warning, possible alternatives include doing nothing,
releasing the gas pedal, depressing the brake pedal, steering to avoid
the target, or combinations of these alternatives.

As one might expect, choice reaction times are of longer duration than
simple reaction times. The Hick-Hyman Law quantifies the relationship
between choice reaction time and the amount of information (the
reduction of uncertainty quantified in bits), dictating that the choice
reaction time increases by approximately 150 ms for each doubling of the
number of equally-likely alternatives (Wickens, 1992). Almost half a
century of experiments has replicated the linear relationship between
choice reaction time and the amount of information that is presented to
subjects. This relationship appears to hold for alternative ways of
manipulating the amount of information, such as the probability of
different stimuli and the number of stimulus alternatives. Although
there is no simple mapping of this law to the more complex problem of
collision warning timing, the Hick-Hyman Law correctly predicts that
brake reaction times are far longer than simple reaction times. A far
greater amount of information is involved in the process of perceiving a
collision threat and responding appropriately.

#### 9.3.4.2 Brake Reaction Time

Brake reaction times have been investigated for forward collision
scenarios both with and without collision warning systems, and under a
range of driver expectation levels. In Johansson and Rumar's (1965)
literature review, they cited work that examined the brake reaction time
to simple laboratory stimuli when subjects had a high degree of
expectancy. Under these conditions the brake reaction time was found to
vary in the range of 0.45 and 0.60 s, with a movement time of 0.15 s. In
these laboratory situations, where driver expectation was high, the time
between the presentation of the stimulus and the time that the foot
first began to move was approximately 0.25 s, with relatively little
variation. Curiously, brake reaction times were found to be about 0.1 s
longer in a moving vehicle than in a stationary one. Johansson and Rumar
replicated these results in a moving vehicle, where subjects expected a
stimulus to occur within the space of 10 km, reporting a median brake
reaction time of 0.66 s. Because of the high level of driver expectation
and the simplicity of the stimulus-response requirement, these
experimental circumstances represent an absolute lower bound on brake
reaction time. In more realistic circumstances when the events are less
predictable, brake reaction times are much greater.

Olson and Sivak (1986) examined brake reaction times in response to an
obstacle placed on a rural roadway. Olson and Sivak measured drivers'
responses to an unexpected yellow foam rubber obstacle. The obstacle was
placed over the crest of a hill, so it was not visible until the vehicle
was partway into the event. The 50^th^ percentile for brake reaction
time was just over 1 s, with a 95^th^ percentile of approximately 1.5 s.
The movement time, defined as the time it takes to move the foot from
the accelerator to the brake pedal, contributed about 0.4 s to the brake
reaction time. Brake reaction time varied little between age groups.
Olson and Sivak also examined drivers' brake reaction times to an
expected light. The median for this condition was 0.6 s, with a 95^th^
percentile of 0.8 s, reinforcing the theory that uncertainty increases
reaction time.

With the development of relatively low-cost but high-fidelity driving
simulators, the University of Iowa has conducted several experiments
that examine the effect of FCW systems on brake reaction time. Lee et
al. (2002) reported that mean accelerator releases were 1.35 s for
distracted drivers who were alerted with an early FCW system compared
with 2.21 s for distracted drivers without the system. The movement time
(from accelerator to brake) was consistently around 0.5 s for all
conditions, so to compare these accelerator release times to brake
reaction times, the values are approximately 1.8 and 2.7 s,
respectively. When drivers were not distracted, mean brake reaction
times (including movement time) with an FCW system were 1.5 s compared
with 2.2 s, demonstrating that the system could also potentially provide
benefit for attentive drivers.

In comparing these driving simulator results to the on-road results of
Olson and Sivak, the reaction times of attentive drivers with no warning
system (2.2 s) were considerably larger than those of the unexpected
yellow foam obstacle condition (1 s). This could in part be an artifact
of the driving simulator. However, a more likely explanation is that the
yellow obstacle that Olson and Sivak used was more salient than the
braking of the lead vehicle in the Lee et al. study. As discussed in
section 1.3, drivers' visual systems are limited in their ability to
accurately perceive relative motion, especially decelerations. This
suggests that the time required may be quite significant for the optic
flow to reach threshold levels. Lee et al. used relatively large
time-headways (1.7 and 2.5 s), which would increase the time required
for the optic flow to reach threshold levels. They reported that drivers
responded later in the 2.5-s condition than the 1.7-s condition, even
though the larger time-headway was coupled with a larger lead vehicle
deceleration (0.55 g compared with 0.4 g). Smith (2002) also
demonstrated a strong positive correlation between time-headway and
reaction time.

Perhaps the brake reaction time study that is most representative of the
conditions relating to a threat assessment algorithm was that of Kiefer
et al. in the 1999 CAMP project. Kiefer et al. studied the distribution
of brake reaction times in response to different FCW interfaces on a
test track. When drivers were distracted with a search for a
non-existent telltale light in the vehicle interior, the lead
vehicle[^10] began decelerating with disengaged brake lamps. The 50^th^
percentile brake reaction time for this condition was 0.92 s, with a
95^th^ percentile of 1.52 s. In another condition, drivers were
distracted with a question and answer session with the experimenter in
the back seat. This condition yielded brake reaction times that were 126
ms less than the telltale-search condition. In both of these conditions,
drivers choose their own headway and were not expecting the lead vehicle
to begin braking. It is also important to stress that these brake
reaction times were recorded when drivers were alerted with an FCW
system, which is what the threat assessment algorithm would also assume.

Following McGehee (1995), Brown et al. (2002) proposed that a reasonable
estimate for mean reaction time in rear-end collision scenarios should
fall between 1.2 and 1.5 s, with a maximum reaction time of 2.5 s.
Reaction time is likely to vary considerably across parameters such as
the timing of the warning system, the type of threat event (e.g., lead
vehicle braking compared with lead vehicle stopped), and perhaps most of
all, the level of attentiveness of the driver. For greatly distracted
drivers, it is reasonable to expect brake reaction times approaching 2.5
s, however, for drivers who are highly attentive or expecting an
incident, much shorter brake reaction times are likely. Ideally the
system could detect the state of the driver (e.g.,
distracted/non-distracted and drowsy/alert) and adapt the reaction time
accordingly. This possibility will be discussed in later sections.

### 9.3.5 Cautionary Alerts

The question of whether to include a cautionary alert level in an FCW
system has received relatively little attention in the literature until
recently. Although the two FCW algorithms in the ACAS FOT algorithms
include a cautionary phase, the CAMP (1999) program recommended that
only single (imminent) stage warnings be used. Lerner, Kotwal, Lyons,
and Gardner-Bonneau (1996b) differentiated between an imminent alert,
which "requires an immediate corrective action" and a cautionary alert,
which "alerts the operator to a situation which requires immediate
attention and may require a corrective action." This section will
discuss the arguments for and against including a cautionary stage in
FCW systems, before criteria for a cautionary alert phase are discussed.

#### 9.3.5.1 Cautionary Alert Stage Discussion

Lerner et al. (1996b) proposed a set of human factors guidelines for the
design of collision warning systems. After reviewing the human factors
literature in a range of different fields (e.g., aviation, military,
nuclear power plant), Lerner et al. recommended that all warning systems
should be capable of producing at least two levels of warning. This
recommendation was based on the fact that the most effective stimuli for
alerting the driver are characterized by intrusiveness and urgency.
Unfortunately, because these stimuli almost force the driver to attend,
they are also the most annoying when unwarranted. The inherent trade-off
between a more sensitive system and more nuisance alerts demands that
system designers balance the intrusiveness of an alert with the
probability of nuisance alerts. Lerner et al. recommend using multiple
stages of alert as an approach to minimize the conflict between broader
protection and greater annoyance. Rather than choosing between a less
attention-getting display that provides earlier warning and a more
attentive-getting display that provides later warning, system designers
can effectively choose both. This may provide the benefits of an earlier
display with less of the cost associated with nuisance alerts. Because
visual stimuli tend to be less annoying than auditory stimuli, Lerner et
al. recommend that cautionary displays be limited to visual stimuli. For
imminent alerts, Lerner et al. recommended using both a visual stimulus
(qualitatively different from the cautionary visual stimulus) and an
auditory stimulus. Auditory stimuli, although potentially disruptive,
have an advantage over visual stimuli, in that they can acquire the
driver's attention independent of where the driver is oriented.

Kiefer et al. (1999), however, recommended against using a cautionary
phase for FCW systems, and restricted the CAMP project investigation to
imminent alerts. To support this recommendation, they argued that
including a cautionary phase would increase the number of in-path
nuisance alerts and that cautionary alerts require a more complex
driver-vehicle interface, that could restrict the potential for
implementation across multiple vehicle platforms.

In a preliminary effort to develop an interface for the ACAS FOT
algorithm, Smith (2002) compared multiple-stage visual alerts with a
single-stage visual alert in a driving simulator study. Drivers
experienced the interface for 12 min while following a vehicle that
changed speed erratically. After a fixed interval, the lead vehicle
abruptly decelerated, requiring that the driver of the host vehicle
respond quickly to avoid a rear-end collision. The multiple-stage visual
alerts, which included a "looming" (expanding image) rather than "scale"
quality, facilitated significantly faster reaction times than the
single-stage alert. Subsequent subjective data also revealed that most
drivers preferred multiple-stage displays, although there was a subgroup
(who tended to be younger) that preferred single-stage alerts. Smith
recommended that the ACAS FOT use a multiple-stage display. Thus far,
the preliminary data collected from the ACAS FOT suggests that drivers
are less tolerant of the auditory stimulus accompanying the imminent
alert, than of the visual-only cautionary-alert stimulus. Although the
subjective ratings from the first five ACAS FOT participants are
consistently negative, it does not appear that the cautionary alert is
responsible.

There are several analytical arguments that can be made for including a
cautionary stage. Perhaps the most compelling argument is one that also
underlies the time-headway alert criterion. Although an imminent threat
may not exist at an instantaneous moment in time, when a driver
tailgates and the lead vehicle abruptly decelerates, the driver of the
host vehicle may be left with insufficient time to respond. If a system
designer strictly adheres to Lerner et al.'s (1996b) definition of an
imminent state ("requires an immediate corrective action"), a
kinematic-constraints algorithm may allow the driver of the host vehicle
to drive with no distance between the lead and host vehicle. However,
although this state may not represent a definite and immediate threat, a
driver who follows the lead vehicle with virtually no headway may be
exposed to great danger over time. A cautionary alert allows the system
designer the flexibility to reserve imminent alerts for definite and
immediate threats, where the FCW system communicates to the driver that
unless immediate action is taken, a collision will result. The
cautionary alert would then protect the driver against operating in
circumstances in which danger is probable. Such a system could promote
safer driving behavior. The cautionary alert could also be viewed as a
pre-imminent cue. In Section 9.3.4.1 it was discussed how preparatory
cues decrease reaction time. Lee et al. (1999) argued that FCW systems
not only warn the driver in imminent situations but also have the
potential to contribute to safety by making the boundary conditions of
safe driving more visible.

Perhaps the most important consideration in the decision to include a
cautionary stage, is the consequences for nuisance alerts. As Kiefer et
al. (1999) suggested, the inclusion of a more conservative alert
criterion is likely to increase the overall number of nuisance alerts.
Fortunately, the added nuisance alerts will involve less intrusive
stimuli, which are likely to have less impact on driver annoyance. It is
even possible that the inclusion of a cautionary stage could reduce the
negative impact of nuisance alerts. One of the major difficulties with
imminent alerts is that the ratio of nuisance alerts over all alerts is
extremely high. A conservative estimate, based on LeBlanc, Bareket,
Ervin, and Fancher (2002) data, is that at least 77 percent of imminent
alerts are nuisance alerts. For the cautionary alert, the ratio of
nuisance alerts over all alerts may be much smaller. This is because the
more conservative criteria behind a cautionary alert will result in more
appropriate cautionary alerts, so although the numerator (nuisance
alerts) might increase, the denominator is proportionally increased by a
larger amount. Cautionary alerts provide the driver with an opportunity
to experience the alert behaving appropriately, an opportunity that is
rare with imminent alerts. In this way, cautionary alerts may serve to
build the driver's confidence in the warning system. They may also
assist the driver with learning to associate the visual display of the
warning with the appropriate meaning (Horowitz & Dingus, 1992).

#### 9.3.5.2 Criteria for a Cautionary Alert Stage

If a decision is made to present a cautionary alert to the driver, one
must then select a cautionary warning criterion. Lerner et al. (1996b)
defined the cautionary stage as one that "alerts the operator to a
situation which requires immediate attention and may require a
corrective action." In the ACAS FOT program, both algorithms use a
cautionary alert. The NHTSA algorithm for the ACAS FOT uses a fixed
distance-headway rather than time-headway. The rationale for this was
not explicitly described in the Brunson et al. (2002) document nor did
it appear that this aspect of the algorithm was explicitly tested. The
algorithm used some logic to ensure that the situation was one in which
the host vehicle was tailgating (e.g., the lead vehicle must be
persistent, moving, and traveling at a similar speed to the host
vehicle), rather than some fleeting transition period. The other mode of
the cautionary algorithm was similar to the imminent criteria, except
with a more conservative value for the assumed braking rate. This
algorithm used assumed braking rate values of 0.27, 0.32, and 0.38 g,
depending on the driver-selected alert-timing setting that the driver
selects.

Another potential criteria for triggering a cautionary alert is to
select different values of assumed lead vehicle braking and brake
reaction time. Delphi recently evaluated the use of these criteria for
triggering a cautionary alert. It appeared that the most effective
cautionary algorithm evaluates two functions and selects the most
conservative alternative. The two alternatives involve using the
kinematic-constraints imminent criterion with either a more conservative
value of assumed brake reaction time or a greater magnitude of lead
vehicle deceleration. Whereas the brake reaction time parameter
addresses situations where the host vehicle is closing on the lead
vehicle, it does not address tailgating situations (because with no
closure there is nothing to react to). The lead vehicle deceleration
parameter addresses tailgating situations but does not address
stationary-vehicle situations (because stationary vehicles can't
decelerate). The algorithm compared these two alternatives because using
both alternatives simultaneously resulted in an algorithm that was
excessively conservative.

### 9.3.6 Driver Vehicle Interface

This Section will review the work that is relevant for guiding the
design of the driver-vehicle interface for an FCW system. It will
discuss the modality of the warning stimuli, the form and number of
stages of the icon sequence, and the most recent developments in various
programs in which Delphi has been involved.

#### 9.3.6.1 Warning Modality

After reviewing standards and recommendations for other warning systems
(e.g., aviation, air traffic control, nuclear power plane, military,
medical, and highway systems) and other relevant theoretical research,
Lerner, Kotwal, Lyons, and Gardner-Bonneau (1996b) produced a set of
human factors guidelines for the display of collision warnings.
Consistent with the principles of redundancy gain, Lerner et al.
recommended that the imminent crash avoidance warnings must be presented
across at least two modes. The redundancy gain principle proposes that
when a message is expressed in more than one way, the likelihood that
the message is correctly perceived increases (Wickens, Gordon, & Liu,
1998). This is especially evident when messages are presented across
more than one sensory modality because factors degrading the message
over one modality are not likely to degrade the message across the other
modalities.

For automotive collision warnings, Lerner et al. argued that an imminent
message should be presented across the visual modality and either the
auditory or tactile modality. The advantage of the visual modality is
that icons can be created to unambiguously communicate information
efficiently, compared with auditory tones that may be ambiguous, or
speech that requires more time to comprehend. Lerner et al. also argued
that visual stimuli tend to be less annoying to the driver than auditory
stimuli, an observation that has been reinforced by recent observations
of the ACAS FOT program. Lee, McGehee, Brown, and Raby (1999) also
argued that the visual modality facilitates recollection and
comprehension. The Lerner et al. guidelines suggest that the imminent
display should employ a prominent rapidly flashing red icon, flashing at
between three and five times per second with a 50 percent duty cycle).

Auditory tones and haptic stimuli tend to be more difficult for the
driver to understand, because, with the exception of automatic braking,
the stimulus-response relationship tends to be somewhat arbitrary and
must therefore be learned. However, unlike the visual modality, the
auditory or tactile modalities do not require that the driver be
oriented in any particular way to receive the message. To ensure that
the driver receives the warning, Lerner et al. recommended accompanying
the visual display with either an auditory non-speech tone or a tactile
display. They argued that the effect of using a haptic stimulus for
warning the driver is not well understood and should therefore be used
with caution pending further research. Seven years later, this is still
relatively true. The ACAS FOT program decided against using a haptic
stimulus in the fleet of vehicles, largely because of the lack of
research supporting the use of haptic stimuli in a vehicle and the
corresponding difficultly in acquiring Institutional Review Board (IRB)
approval for the FOT. Lerner et al. recommended that if haptic stimuli
are used, the driver should be able to form a natural association
between the stimulus and the crash avoidance situation it represents.

The 1999 CAMP FCW program investigated the effectiveness of several
multiple-modality single-stage FCW displays. One of their experiments
compared a High-head-down display (HDDD) with non-speech audio with a
HDDD with a 0.24-g oscillating brake pulse. In the surprise
lead-vehicle-moving trials, drivers reacted almost half a second earlier
to the HDDD-plus-audio condition than the HDDD-plus-brake-pulse
condition, a difference that was significantly different. A later study
investigated the effects of adding a brake pulse to the HDDD-plus-audio
condition. During the surprise lead-vehicle-moving trials, drivers
reacted 165 ms earlier to the condition without the brake-pulse,
indicating that the brake-pulse may slow rather than expedite the
driver's response. Although they tended to react later, drivers in the
brake-pulse condition were actually in a less threatening situation when
they reacted because the brake-pulse had already decelerated the
vehicle. This fact reveals how using a brake-pulse as a warning stimulus
blurs the boundary between warning and autonomous control. Although the
brake pulse clearly exhibits a near ideal level of stimulus-response
compatibility, it may raise important questions regarding taking control
of the vehicle and the potential shifting of the driver's position that
may result.

Other forms of haptic stimuli include vibrating the accelerator,
steering wheel, or driver's seat. Vibrating the accelerator may not be
an effective means of alerting the driver because it requires that the
driver's foot be situated on the pedal, which will frequently not be the
case (e.g., during coasting or while cruise-control is engaged).
Vibrating the steering wheel is likely to have poor stimulus-response
compatibility because steering wheel vibration frequently accompanies a
mechanical problem with the vehicle (e.g., damaged tires or poor
alignment) or could suggest a steering maneuver when such a maneuver may
not be appropriate. One rationale behind vibrating the driver's seat is
that it can be implemented to feel similar to external rumble strips.
Rumble strips have been used on roadways to alert the driver that the
vehicle is beginning to depart the roadway. Although the forward
collision warning represents a different scenario, rumble strips appear
to be an effective means of acquiring the driver's attention and so
virtual rumble strips (seat vibration) could potentially be an effective
stimulus. As suggested in the Lerner et al. (1996) guidelines, the use
of seat vibration to signal potential threat of a rear-end collision
requires investigation. Lerner et al. suggested using vibrational
frequencies in the range of 500 to 300 Hz and suggested that frequencies
of around 3 Hz should be avoided because they could result in motion
discomfort. Delphi is currently investigating using a seat belt
pre-tensioning pulse to communicate warnings to the driver. Given that
seat belt pre-tensioning is used primarily for pre-crash events (where
collision is unavoidable), pulsing the seat belt at a lower amplitude
may be an appropriate way to communicate the danger of collision to the
driver.

Tan and Lerner (1995) investigated the most appropriate forms of
auditory stimuli for alerting the driver of a potential
collision-warning situation. After comparing different auditory stimuli,
Tan and Lerner concluded that acoustic warnings are more appropriate
than voice stimuli, based on the criteria of noticeability,
discrimination, meaning and urgency. Lerner, Dekker, Steinberg and Huey
(1996a) also observed that drivers are more tolerant of false alarms
when a non-voice tone is used rather than a voice alert. When a
non-voice tone was used, drivers were able to tolerate four-times as
many false alarms as when a voice alert was used, for the same level of
annoyance ratings. During the CAMP FCW program, Keifer et al. (1999)
compared the performance of a visual-plus-acoustic warning with a
visual-plus-speech warning. Participants responded significantly faster
to the visual-plus-acoustic warning.

Tan and Lerner (1995) compared the subjective ratings of various
auditory warning candidate tones. Based on these ratings, they made
suggestions on which alerts were more appropriate for warning systems.
It is important to mention, however, that most of the alerts that they
suggested were described as appropriate based only on the criteria of
noticeability, meaning, and urgency ratings but these alerts were also
far less tolerable in terms of annoyance ratings. Lerner et al. (1996b)
recommended that the audio frequencies should range between 500 and 3000
Hz, and designers should consider the masking effects of the ambient
vehicle-cabin noise. McGehee (2000) also recommended for the J2400
guidelines that FCW systems use an auditory tone between 500 and 3000
Hz, and recommended, based on the CAMP project that peaks of 2500 and
2650 Hz be used. Observations made during the ACAS FOT suggest that such
tones may be overly annoying to the driver. The alert that Tan and
Lerner selected as most appropriate (the low-fuel aircraft warning) that
was also similar to the sound used in the CAMP FCW project, was quickly
removed from consideration in the ACAS FOT program, in favor of a less
intrusive tone. The piercing sounds of the low-fuel warning would be
likely to make even the smallest number of nuisance alerts intolerable.
Simple auditory tones nearer to 2000 Hz are likely to be more
appropriate. Following the CAMP FCW project, McGehee (2000) recommended
that the intensity of the auditory stimulus should be 75 dBA. This is
similar to the sound pressure level of the tone used in the ACAS FOT
program. Lerner et al. (1996b) recommended that the intensity should not
exceed 115 dBA.

The modality of the warning stimulus may differ across imminent and
cautionary alerts. Whereas an imminent level should be accompanied with
an auditory stimulus, Lerner et al. suggested making the cautionary
stimulus visual only because visual displays are less annoying than
acoustic messages. Horowitz and Dingus (1992) had made a similar
recommendation, arguing that pilots prefer visual over auditory stimuli
when they have enough time to react, however, when an immediate response
is required, auditory stimuli tend to be more effective. The ACAS FOT
display followed these guidelines, not implementing an auditory tone for
the cautionary levels. This decision was made based on a large number of
hours of subjective engineering evaluation. Cautionary stimuli should
also be less intrusive than imminent stimuli. For cautionary visual
stimuli, Lerner et al. recommended using either constant (not-flashing)
red or constant amber icons. Lerner et al. (1996b) recommended that the
imminent-level alert should be qualitatively different from the
cautionary levels, using a red icon flashing between 3 and 5 Hz with a
50 percent duty cycle.

Many interface parameters may be dependent on the choice of the visual
display apparatus. For example, if a Head-down display (HDD) is used
visual cautionary displays may not be appropriate. The frequent
activation of a cautionary alert may actually draw the driver's
attention away from the forward roadway, potentially acting as a
distraction. This visual distraction would be timed to draw the driver's
attention away from the roadway at the most inopportune times. For a FCW
system, there are many arguments that can be made in support of Head-up
displays (HUDs).

McGehee, Mollenhauer, and Dingus (1994) recommended HUDs for FCW
systems, arguing that because inattention is the leading cause of
rear-end crashes, warning information should be displayed in a manner
that will orient the driver's attention to the forward roadway. An alert
that draws the driver's visual resources away from the forward roadway
may actually be counterproductive. Kiefer and Gellatly (1996) described
arguments in favor of a HUD for general display of information to the
driver. One argument in favor of HUDs, referred to as the "improved
forward visibility claim", claims that drivers are still able to
perceive forward scene events while they sample display information. If
for example, the driver, foveates the HUD information, the optic array
specifying forward-scene events will fall on the peripheral of the
driver's retina, allowing for the detection of sudden changes. Across
several tasks, Kiefer and Gellatly observed more accurate detection of
forward scene obstacles when drivers were glancing at a HUD than at a
HDD, supporting the forward visibility claim. Another argument in favor
of HUDs is that the transition time between the forward scene and the
display is shorter than between the forward scene and HDDs. Kiefer and
Gellatly also cited several studies that support this argument.

Not only does information presented on a HUD have the potential to
distract the driver to a lesser extent but information presented on the
HUD may also be easier to detect. Because HUDs are located in close
angular proximity to the forward scene, when the driver fixates on the
forward scene, the information presented on the HUD falls on the
peripheral receptors of the driver's retina. For some salient displays,
the driver may not even need to foveate the HUD to acquire the presented
information. Because the HUD is located in close proximity to the
forward visual scene and renders an image located several meters in
front of the driver, it has the potential of offering drivers the
opportunity of attending to the forward scene and the HUD content
simultaneously. Grant, Kiefer and Wierwille (1995) compared the
detection of telltales between HUD and HDD presentations. Unexpected
brake telltales were presented up to four times while subjects drove on
the road, in what they were informed was a familiarization run. Drivers
detected and identified telltales sooner, and with greater probability
when the telltales were presented on the HUD than on the HDD. In the HUD
group, seven of eight subjects detected the first brake telltale,
compared with only two of eight in the HDD condition. Kiefer et al.
(1999) compared the effects of an imminent warning presented on the HUD
with those presented on the High-head-down displays (HHDD) during the
CAMP FCW program. The icon used in the CAMP project is depicted in
Figure 9.4. Although reaction times were not significantly different
across the two conditions, participants displayed a preference for the
HUD over the HHDD. McGehee's (2000) J2400 guidelines recommended that
either a HUD or HHDD be used, and that designers should avoid using
HDDs. The ACAS FOT program installed HUDs on the fleet of test vehicles
and used the HUD as the only visual display of FCW information.

![](media/image4.wmf){width="3.8993055555555554in"
height="1.8263888888888888in"}

***Figure 9.4.** A depiction of the CAMP icon. This icon was accompanied
by "WARNING" text below.*

#### 9.3.6.2 Form and number of stages for the visual alert

Once the modality of the alert has been selected, the next question
concerns how the visual warning information should be presented to the
driver. If only one-stage of warning is implemented, this question
becomes a simple matter of determining the most appropriate icon. An
icon such as the one that the CAMP FCW program used would probably be
appropriate (see Figure 9.4). However, if multiple stages of warning are
used, the question becomes multifaceted. The designer must not only
determine the optimal number of stages, but also how the display will
transition between those stages. The designer must determine what
dynamic visual properties to employ for communicating an increasing
level of threat. The stimuli must be selected to balance the
attention-getting and informational requirements constraints of driver
acceptance.

Dingus, McGehee, Manakkal, Jahns, Carney, and Hankey (1997) developed
and tested several time-headway displays. Unlike the CAMP experiments
that used a single stage display, Dingus et al. used time-headway to
drive their multiple-stage displays. Dingus et al. evaluated the three
displays that are presented in Figure 9.5 and are described as follows:

![](media/image5.png){width="2.0701388888888888in"
height="1.5631944444444446in"}![](media/image6.png){width="2.095138888888889in"
height="1.5520833333333333in"}![](media/image7.png){width="2.029166666666667in"
height="1.5416666666666667in"}

***Figure 9.5.** The three displays that Dingus et al. (1997) compared,
including from left to right, the car icon display, the bars display,
and the blocks display.*

1.  Car Icon Display -- as headway decreased, a car icon expanded and
    moved down a sequence of three trapezoids, that represented the road
    in front of the driver. From top to bottom, the trapezoids were
    colored green, amber, and red, indicating the level of caution to
    the driver as headway decreased. The display was composed of four
    stages, including the three colors plus a flashing red condition for
    the most severe state.

2.  Bars Display -- as headway decreased, a sequence of three green
    (top), three amber (middle), and three red (bottom) trapezoids would
    successively illuminate. Like the car icon display, the bars would
    flash during the most severe state.

3.  Blocks Display -- one of two blocks (amber and red) would flash
    based on the current headway. When a target was acquired, the amber
    block flashed, and when time-headway fell beneath 0.9 s, the red
    block would flash.

Analyses of coupled headway events revealed that only the Car Icon
Display significantly increased time-headway. Analyses of braking events
revealed that all three displays significantly increased the
time-headway during these events. Subjects exhibited a preference for
the Car Icon and Bars displays over the Blocks display. Dingus et al.'s
experiments suggest that multiple-stage displays may have the potential
of enhancing driving performance, while still being acceptable to
drivers.

The Car Icon Display used expansion of the car-icon image to communicate
the increasing proximity of the lead vehicle. It has been demonstrated
that a wide range of humans and animals of all ages, display an
avoidance response to a quickly expanding pattern of optic flow (Schiff,
1965). This pattern of optical expansion, referred to as "looming" is a
powerful source of information to specify impending collision and plays
an important role in collision control behavior (see Smith, Flach,
Dittman, & Stanard, 2001). Hoffman (1974) proposed that drivers adjust
headway based on change in angular size of the lead vehicle. Given that
drivers naturally use the angular size of the lead vehicle to control
their relative position and avoid collision, it is likely that a display
using size change to code the forward threat level would be immediately
understandable and intuitive to drivers.

Dingus et al. (1997) had also employed a nine-bar display, presenting a
clear scale to convey more fine-grained information to the driver. A
scale stimulus is likely to be more effective than a looming stimulus
for precisely communicating a specific value of a given dimension,
relative to other potential values. The presentation of a scale permits
the system to communicate more finely grained information, allowing a
greater number of discriminable display states. Because an
expanding-icon (looming) stimulus lacks an explicit point of reference,
it may not communicate a specific value precisely. When used in
isolation, this may limit the number of differentiable states. However,
the advantage of an expanding-icon (looming) display is that the
stimulus is more salient and could potentially yield a greater benefit
in recapturing a distracted driver's attention. Looming was also
expected to be more easily understandable because of its natural
association with impending collision.

To support the driver-vehicle interface development for the ACAS FOT
program, Smith (2002) investigated the effects of looming and scale
stimuli. To investigate this issue, several sets of displays were
developed (see Figure 9.6). One display was developed to present a
looming stimulus without a scale (the "looming" display) and another was
developed to present a clear scale without looming (the "scale"
display). A display was designed to balance the presentation of both
scale and looming stimuli (the "looming-plus-scale" display). The
conditions of no looming or scale (the 1-stage display), "looming"
display, "scale" display, and the "looming-plus-scale" display represent
the factorial combination of scale and looming stimuli, supported an
analysis of which stimulus is more effective for an FCW display.
Displays were also included to investigate the optimal number of display
stages. The displays of Figure 9.6 include sequences of 1-stage,
2-stage, 3-stage (looming), and 5-stage (looming-plus-scale or scale).
Note that the number of stages does not include the "vehicle detected"
icon, because the "vehicle detected" icon does not represent a "warning"
per se.

The icon that is displayed in the right-hand panels of Figure 9.6, was
designed to be a rear-end perspective version of the CAMP icon. The
imminent icon was designed to follow the Lerner et al. guidelines in
being distinct from the preceding stages. This was achieved by
developing a bright yellow and red imminent stimulus and having it flash
at 4 Hz. In some informal paper and pencil studies the two-color
imminent icon (Figure 9.6) was preferred over the single-color CAMP icon
(Figure 9.4).

Smith (2002) instructed drivers to follow behind a lead vehicle while
they evaluated the quality of the new driving simulator. This "simulator
evaluation" instruction was a ruse, designed to prevent drivers from
expecting a collision. After 12 min of continuous car following, the
lead vehicle suddenly decelerated at a rate of 0.5 g. Brake reaction
times were recorded to evaluate the performance of the different display
alternatives. Because the pre-braking behavior of the lead vehicle was
quite erratic, many drivers were either braking or coasting (foot off
the accelerator) at the moment that the braking event began. To allow a
more sensitive measure of brake reaction time, the brake reaction time
was defined as the time between the event and the time that the brake
pedal was depressed by 50 percent. The value of 50 percent was chosen
because it represents a brake level that occurs infrequently unless the
driver perceives an imminent threat. To more appropriately attribute the
variance of time headway at the moment the braking began, time headway
at event onset (THEO) was included in the statistical model as a random
covariate. BRT values (evaluated at the THEO mean value) are plotted as
a function of display type in Figure 9.7.

> Threat Level

*Vehicle detected Caution Warning Imminent*

![](media/image8.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image8.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image10.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image8.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image11.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image11.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image11.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image9.png){width="0.9826771653543307in"
height="0.9826771653543307in"}![](media/image12.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image13.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image14.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image15.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image16.png){width="0.9807086614173228in"
height="0.9811023622047244in"}![](media/image8.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image17.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image18.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image19.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image20.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image21.png){width="0.9811023622047244in"
height="0.9811023622047244in"}![](media/image22.png){width="0.9811023622047244in"
height="0.9811023622047244in"}

*1*

*2*

*L*

*S*

*LS*

***Figure 9.6.** The one-stage (1), two-stage (2), three-stage or
looming (L), scale (S), and looming-plus-scale (LS) displays used in
Experiment 1 as a function of threat level. Note that the number of
stages does not include the "vehicle detected" icon.*

The variables of looming and scale can be considered as separate
factors, allowing the independent manipulation of each factor into the
four factorial combinations: 1 (no looming or scale), S (scale without
looming), L (looming without scale), and LS (looming plus scale). In
terms of brake reaction performance, L and LS are statistically
equivalent, but different from C and S, which are also statistically
equivalent. Adding scale to either no display or a looming display
yielded no performance benefit. There were no observable performance
effects of scale, nor was there an interaction between scale and
looming. The significant differences between these four conditions can
be entirely accounted for by the effects of looming. In short, the
looming display reduced BRT, whether accompanied by the scale or not.

The looming and looming-plus-scale displays were consistently ranked as
being superior on the desirable dimensions (where more implies better).
They were preferred to the scale, one-stage, and line displays,
considered to be more discriminable than the one-stage, two-stage and
line displays, more understandable than the scale, one-stage, two-stage,
and line displays, and more attention-getting than the one-stage and
line displays. The inclusion of a scale, however, appeared to have a
negative effect on the undesirable dimensions (where more implies
worse). The scale display was considered to be more interfering than the
looming and line displays, and more annoying than the one-stage,
two-stage, looming and line displays. The looming-plus-scale display was
also considered to be more annoying than the one-stage, two-stage,
looming and line displays.

![](media/image23.wmf){width="5.927777777777778in" height="4.43125in"}

***Figure 9.7.** Brake-reaction-time (evaluated at the THEO mean value)
as a function of display type. The error bars represent plus or minus
one standard error of the mean. The gray boxes represent groups of
displays that are not statistically different, according to LSD pairwise
comparisons using an alpha level of 0.05. If one display does not
co-occur with another display in any of the boxes, then the two displays
are statistically distinct. For example, L, LS, and 2 are statistically
different from S, 1, and C.*

The two experiments revealed little evidence that the scale addition
provided any benefit to the looming display. Participants in the
looming-plus-scale display condition demonstrated no brake reaction time
benefit over participants with the looming display. The scale display
failed to provide any benefit over having no display. These results
suggest that the scale is an ineffective means of presenting FCW
information. One explanation for the failure of the scale component may
be that it is overly graphical and complex in nature, requiring too much
attention from a driver who must react immediately. Whereas the
two-stage and looming displays present a global change in color and size
between each stage, the change in a scale display is more local,
occurring in only a small portion of the display. The fine-grained
distinction provided by the scale may be unnecessary given that the
driver controls the position of the vehicle using the external visual
scene rather than the internal instruments. Given that the driver is
able to use the external visual scene to make fine tuning speed
adjustments, salience may be more important than precision in an FCW
display. The primary purpose of the FCW display is to draw the driver's
attention to a critical event rather than to provide a complete
surrogate for the natural optic flow. Lerner et al. (1996) advised
against presenting graphical information for warning displays because of
the limited time for the driver to respond in an urgent situation.

The decreased driver acceptance of the scale display may relate to the
fact that the scale display violates the "display by exception" axiom of
display design, suggesting that displays should only present information
when the message is important and relevant. Even when no vehicle was
detected, the scale and looming-plus-scale display presented an empty
scale on the HUD. The ever-present scale provided little additional
information and may have perceptually masked the arrival of more urgent
states. Lerner et al. (1996b) claimed that it is easier for drivers to
detect a change from nothing to something than it is to detect a change
from something to something else.

The experimental design included displays of one, two, and three stages
(C, 1, 2, and L). Note that the "vehicle detected" icon was not
considered to be a stage because it does not represent a warning per se.
Performance data revealed little additional benefit after the display
contained at least two stages. There was no statistical basis to
differentiate the displays with two or three stages, but both displays
decreased BRT more than the one-stage and control conditions. The
subjective data mirrored this, with similar ratings for the displays
with two and three stages. The looming display, however, was ranked as
being more preferred, more discriminable, and more understandable than
the two-stage display.

Smith's (2002) second experiment indicated that the age of participants
appeared to have a large impact on how they rated the different display
alternatives. Younger drivers rated the more complex displays
(especially the looming-plus-scale display) as less effective (in terms
of headway maintenance and collision avoidance), more annoying, and less
desirable. Indications of their willingness to buy the product dropped
dramatically for younger drivers when the scale was added to the
looming-display. The middle and older age groups, on the other hand,
rated the more complex displays as being more effective. The middle age
group indicated a general increase in annoyance associated with more
complex displays, whereas, older drivers indicated little increase in
annoyance as a function of display complexity. The middle age group also
indicated that they would be more likely to buy the two-stage and
looming displays than the one-stage and looming-plus-scale displays,
whereas, the older drivers revealed a buy rating that monotonically
increased with display complexity. Averaged across age groups, the
looming-plus-scale display was rated as being the most distracting
display candidate.

Before the onset of the field operational test in the ACAS FOT program,
the color of the "vehicle detected" indicator was changed from green to
the same cyan color that was used for the vehicle speed, ACC set speed
text, and gap/sensitivity setting text on the HUD. Maintaining a
consistent color on the HUD when no threat was present conformed more
closely with the design axiom of "display by exception". Given that
"vehicle detected" is not an inherently urgent state, the icon
representing this state should be less salient to the driver, so that it
can be ignored (when desired). In the absence of a cautionary or warning
state, the HUD presented a monochromatic display, however, when
attention is demanded, an amber or red color would appear (see Figure
9.8).

![](media/image24.png){width="3.1181102362204722in"
height="1.561023622047244in"}![](media/image25.png){width="1.3791338582677166in"
height="0.21811023622047243in"}![](media/image26.png){width="1.4311023622047243in"
height="1.061023622047244in"}![](media/image27.png){width="0.40551181102362205in"
height="0.20826771653543308in"}![](media/image28.png){width="0.3901574803149606in"
height="0.6673228346456693in"}![](media/image29.png){width="0.3901574803149606in"
height="0.6673228346456693in"}![](media/image24.png){width="3.119291338582677in"
height="1.5625984251968503in"}![](media/image26.png){width="1.4326771653543307in"
height="1.0625984251968503in"}![](media/image27.png){width="0.4062992125984252in"
height="0.2015748031496063in"}![](media/image28.png){width="0.3901574803149606in"
height="0.6444881889763779in"}![](media/image29.png){width="0.3901574803149606in"
height="0.6444881889763779in"}![](media/image30.png){width="1.4318897637795276in"
height="1.0625984251968503in"}

***Figure 9.8.*** The vehicle-detected and imminent-warning states of
the ACAS FOT FCW display. The right half of the left display contains
three elements: the alert-level icon at the top, the message line in the
middle, and the warning setting line at the bottom. The left image
displays the vehicle-detected state and the right image displays the
imminent warning state.

Although the "looming display" was selected, prior to the launch of the
field operational test, the warning icons were changed to provide a
single-color multi-level cautionary phase. It was argued that there was
insufficient rationale for changing between an amber and red cautionary
stage. Although this change in color was relatively salient, it was
supported by a somewhat arbitrary criterion. Instead of a qualitative
change (color), the display instead used a more continuous change of
icon size. To accentuate the "looming" effects, more icons were added to
the cautionary phase. The intent of this change was to allow smaller
changes in threat to produce smaller changes in the display. However,
when the level of threat changes rapidly, the display should cycle
through the different cautionary icons, producing a "looming" effect
that may effectively warn the driver prior to the imminent phase. The
sequence of icons is displayed in Figure 9.9.

*.*

![](media/image30.png){width="1.0783464566929133in"
height="0.797244094488189in"}![](media/image31.png){width="1.0346456692913386in"
height="0.7653543307086614in"}![](media/image32.png){width="1.0346456692913386in"
height="0.7653543307086614in"}![](media/image33.png){width="1.0751968503937008in"
height="0.7964566929133858in"}![](media/image34.png){width="1.0346456692913386in"
height="0.7653543307086614in"}![](media/image26.png){width="1.0751968503937008in"
height="0.7964566929133858in"}

***Figure 9.9.** The final sequence of icons implemented in the field
operational test phase of the ACAS FOT program.*

Despite some unfavorable subjective evaluations about the overall FCW
system during the ACAS FOT, early subjective data seems to suggest that
drivers tended to find the visual displays to be tolerable. The wide
range of sensitivity setting levels (which control the amount of
pre-imminent warning that the driver received) also suggests that many
drivers found the cautionary phase to be useful.

#### 9.3.6.3 Implementing Multiple Warning Systems 

The "looming" display has been extensively evaluated throughout the
months of the ACAS FOT program. It is likely to be the most thoroughly
tested sequence of icons in existence for a FCW display. For this
reason, the "looming" display is an appropriate choice for a vehicle
that has only one form of collision warning system. However, when more
than one form of collision warning exists on a single vehicle, the
"looming" display may be problematic. The tail-end perspective does not
lend itself to the integration of additional warning systems. For
example, it is difficult to conceive of a way to integrate a blind-spot
or lane drift warning display to the "looming" display format. Rather
than developing a single collision-warning display area, it is likely
that if the "looming" display were used, several display areas would
need to be included, perhaps one for each zone of coverage. This
fragmented implementation would probably be less effective than a single
warning area that can convey the status of multiple collision warnings.

A recent Delphi show vehicle was designed that incorporated FCW,
Blind-spot warning, and Back-up aid systems. Like the ACAS FOT fleet of
vehicles, this vehicle presented warnings on a full-color reconfigurable
HUD. However, rather than the tail-end perspective used in the icons
displayed in Figures 9.6, 9.8, and 9.9, the collision warning system
presented icons that used a top-down perspective. These icons are
displayed in Figure 9.10.

![](media/image35.wmf){width="1.1791666666666667in"
height="1.8472222222222223in"}

![](media/image43.png){width="0.3736111111111111in"
height="0.6076388888888888in"}

***Figure 9.10.** Collision warning icons displayed using a top-down
perspective. The three FCW icons on the left progress from
vehicle-detected (left), caution warning, and imminent warning. The two
icons on the right show a left blind-spot warning and back-up aid
functioning.*

To provide a point of reference for the display, a host vehicle icon is
displayed at the bottom of the warning area. Next to the host vehicle,
are trapezoids that are used to indicate the zones that the sensor is
covering, in the case of FCW a forward-facing trapezoid is used to
indicate the forward zone of coverage. The presence of the trapezoid is
used to communicate whether the collision warning is currently
functioning. For example, at slow speeds, the FCW system would be
disabled and therefore the trapezoid would not be present. The lead
vehicle icon, if present, appears above the FCW trapezoid. When the
vehicle is detected, the icon is fairly small and appears in a similar
color to the rest of the HUD content. The threat level is coded not only
in the size and color of the lead vehicle icon, but also with the
distance between the lead and host vehicles. As the threat level
increases the color changes first to amber (for caution) and then if the
threat level increases to a flashing red (imminent) display. For the
imminent display, both the lead vehicle icon and the warning-zone
trapezoid flashed. This sequence of icons produces a visual effect of
decreasing distance (between lead and host vehicles) that is likely to
be an appropriate stimulus to communicate the threat level, and a
"looming" effect.

The advantage of this top-down collision-warning format is that several
collision-warning zones can be easily added to the collision warning
display. To display a Blind-spot warning, for example, vertical
trapezoids can be added onto the sides of the host vehicle icon (see
Figure 9.10). Whereas cyan-colored (the same as the host vehicle icon)
side trapezoids indicate that the blind-spot system is functioning, an
amber or flashing red side trapezoid would indicate a cautionary or
imminent blind-spot-warning level. When the driver shifts the vehicle
into reverse, a backward-facing trapezoid could be displayed to face the
rear zone of coverage (see the rightmost cell of Figure 9.10). The
top-down collision warning display format appears to be the most
versatile for displaying multiple collision warning systems in a
consistent location and format. Although this display has not received
as much validation as the "looming" display format for FCW, as OEMs
increasingly implement multiple collision warning systems on vehicle
platforms, the top-down format is likely to become increasingly useful.
Thus the top-down format may be a more versatile
collision-warning-display format to support the expanding needs of the
future.

### 9.3.7 Nuisance Alerts

Nuisance alerts present a serious challenge for FCW algorithms. Nuisance
alerts may not only decrease driver acceptance of the system, but could
potentially undermine the effectiveness of the alert, by reducing the
credibility of the system (Lerner, Dekker, Steinberg & Huey, 1996a; Lee,
McGehee, Brown, & Raby, 1999, Horowitz & Dingus, 1992). Horowitz and
Dingus (1992) argued that frequent warnings are likely to be ignored
because they may be perceived as "crying wolf". Preliminary data (the
first 15 subjects) from the ACAS FOT suggests that the alert rate was
approximately 4 alerts/100 mi (1.5 alerts/hr). During the first phase of
the ACAS FOT program a great amount of effort was involved in reducing
the nuisance alert rate to this level

Lerner et al. (1996a) investigated drivers' tolerance of nuisance
alerts. They manipulated the false alert rate by equipping vehicles with
a system that generated random alerts. Drivers were instructed that when
the audio alert was accompanied with a flashing light, the alert should
be considered to be an appropriate alert. Inappropriate alerts could be
differentiated by the absence of a flashing light. Drivers received \$4
for each correct response (button push) to appropriate alerts and were
penalized \$1 for each response to inappropriate alerts. This method of
simulating nuisance alerts was selected so that nuisance alert rates
could be experimentally manipulated without confound. The goal of this
study was to determine how many nuisance alerts are acceptable.

Nuisance alerts were simulated at rates of 4/hr, 1/hr, 0.25/hr, and
0.125/hr for alerts with non-voice audio, and at a rate of
1/hr-with-voice audio. Analyses revealed that the 4/hr and
1/hr-with-voice were significantly more annoying and less acceptable
than the other rates. Average annoyance rating increased with the
frequency of nuisance alerts, however even the most annoying frequency
of 4/hr was rated as 3.85 on a 9-pt scale, where 1 represented "not at
all annoying", 5 represented "tolerable", and 9 represented "extremely
annoying". This suggests that a rate of 4/hr with non-voice audio would
be more than tolerable for most drivers. The 4/hr and 1/hr-with-voice
conditions were not significantly different from each other in terms of
annoyance, suggesting that drivers are more tolerant of non-voice than
voice nuisance alerts. Lerner et al. revealed that drivers had large
individual differences in their tolerances of nuisance alerts.

The validity of their method for simulating nuisance alerts is
questionable, given that the system was not a safety warning system and
does not potentially require a quick response to avoid danger. It might
be argued that the threat of danger may be an important constraint in
the tolerance of nuisance alerts that was not represented in this study.
In addition, rewarding participants with \$4 for every "appropriate"
alert, might also have increased drivers' tolerance of nuisance alerts
compared to a real FCW system that may only rarely provide noticeable
benefit to the driver.

To date, the most valid data on nuisance alert tolerance may come from
the pilot studies and preliminary data of the ongoing ACAS FOT program.
These studies provide the subjective responses of drivers to a real
functioning FCW system that they experienced on the roadway. In the
Stage 2 pilot testing, twelve drivers drove a representative route,
while they were accompanied with an experimenter. Ninety-two alerts were
experienced in total, 50 of which involved moveable targets (targets
either currently moving or previously recorded as moving) and 42 of
which involved stationary targets. It is likely that most of these
alerts could be classified as nuisance alerts, and the alert rate was
6.6 alerts/100 mi, which corresponds to approximately 5 alerts/hr.
Drivers were asked how often they received alerts that were false and
responded with an average of 5 on a 7-pt scale, where 1 represented
"frequently" and 7 represented "infrequently".

In Stage 2.5 pilot testing, after some modifications had been made to
address many of the nuisance alerts, 6 subjects drove the same test
route. The nuisance alert rate was reduced by 59 percent, and subjects
indicated that they perceived alerts that were false slightly less
frequently, at 5.5. In Stage 3, 6 drivers were provided with the ACAS
vehicles for a week, unaccompanied by an experimenter. At the present
time, only the results of the first 4 drivers have been disclosed.
Strangely, the alert rate increased to approximately 9.8 alerts/100 mi,
in-part due to the algorithm becoming overly cautious in colder weather.
Despite this dramatic increase in alarm rate, the frequency ratings only
dropped slightly to 5.3. Across the three pilot tests, the subjective
ratings appeared to suggest that drivers perceived the rate of nuisance
alerts as being relatively infrequent. This data appeared to reinforce
the findings of Lerner et al. (1996a), that drivers should be relatively
tolerant of the nuisance rates around 4/hr, and apparently even high.

However, preliminary data from the field operational test (FOT) phase of
the ACAS FOT program suggests otherwise. Although the nuisance-alert
rate on average was approximately 4 alerts/100 mi and 1.5 alerts/hr (for
the first fifteen subjects), the subjective responses indicated that
they tended to consider the system to be "unreliable". Although
responses to the ACC system were uniformly positive, the responses to
the FCW system tended to be negative. One explanation for the large
difference between the FOT and Stage 3 pilot data is the much longer
exposure duration. Perhaps it took over a week of exposure for the
feelings of novelty to decline, and once subjects began to view the
system as a potential product, tolerance rapidly declined. The
inconsistency of the Stage 3 and FOT data suggests that exposure
duration is an important variable for tolerance of nuisance alerts.

Perhaps the negative subjective ratings are not surprising if one
considers the ratio of nuisance alerts over all alerts. Although on a
national scale, rear-end collisions are clearly a severe problem,
Knipling et al. (1992) estimated that the probability of a vehicle
striking another vehicle in a police-reported rear-end collision in its
functional lifetime (11.5 years on average) is 0.09. Horowitz and Dingus
(1992) estimated that rear-end collisions occur once every 25 years for
a given individual. Given the rarity of rear-end collisions on the
individual scale, the opportunity for a given FCW system to prevent an
accident occurs infrequently. If we also consider near-miss
circumstances as warranting an alert, the number of circumstances in
which alerts are appropriate increases. However, if nuisance alerts
occur at a rate of over one per hour, they will exceed the number of
appropriate alerts by several orders of magnitude. As the proportion of
nuisance alerts over all alerts approaches unity, the system is likely
to appear to be extremely unreliable. Alert rates even as low as 1/hr
could easily be perceived as intolerable. Horowitz and Dingus (1992) had
warned (p. 1011) "If the technology is not reliable, i.e., many false
alarms occur, the system will be deemed useless and annoying by the
driver and lose its effectiveness." The J2400 guidelines recommend that
the nuisance alert rate should be less than one per week.

## 9.4 LANE DRIFT WARNING (LDW)

Single Vehicle Roadway Departure (SVRD) crashes are the single largest
cause of driver fatalities in the United States, accounting for 36
percent of all roadway fatalities (U. S. Department of Transportation,
1997). Lane Drift Warning (LDW) systems have been designed to prevent
drivers from unintentionally drifting out of their current lane while
they are inattentive or impaired. Various vigilance-monitoring
technologies that monitor and alert the driver for drowsiness,
distraction, or intoxication may address the problem of SVRD crashes
indirectly (Mironer & Hendricks, 1994). LDW systems directly address the
problem of vehicle's unintentionally departing the roadway. This section
will discuss the different algorithm alternatives, the driver vehicle
interface, and the nuisance alerts associated with LDW systems.

### 9.4.1 Algorithm Alternatives

The LDW system uses information about the host vehicle's position on the
roadway, the geometric characteristics of the upcoming road segment, the
vehicle's dynamic state, and whenever possible, driver intention
(Pomerleau et al., 1999). This information is fused in an attempt to
determine whether the host vehicle is likely to depart the roadway in
the near future. Several alternatives exist for how LDW systems fuse
this information in order to assess the level of threat.

Pomerleau et al. (1999) described several versions of lane drift
algorithms. Each algorithm was based on a calculation of the
Time-to-line-crossing (TLC). TLC is a calculation of the time until the
vehicle crosses a lane boundary, based on the distance between the
vehicle and the lane line and the host vehicle's lateral movement. Four
different versions of TLC algorithms were described, including
zero-order, first-order, second-order, and kinematic-based TLC
calculations. The order of the TLC formulae refers to which levels of
temporal derivative of lateral position are incorporated in the formula.
The zero-order formula uses no temporal derivative of lateral position
and therefore relies only on the lateral position relative to the lane
line. Pomerleau et al. referred to the zero-order formula as "electronic
rumble strips" because like the rumble strips on the roadway, a LDW
system based on this formula would merely alert the driver when the
vehicle deviates from the lane based on a distance criterion. This
criterion is represented by the following formula:

$$\text{if}\begin{matrix}
 & d
\end{matrix} \leq dT\begin{matrix}
, & \text{warn}
\end{matrix}$$

where d is the distance between outside edge of tire and the outside
edge of the lane boundary and *d~T~* is the distance threshold.

Pomerleau et al. suggested that the main advantage of the zero-order TLC
algorithm is mathematical stability. Small errors in the measurement of
lateral position lead to only small errors in the warning time. The
disadvantage of this simple algorithm is that it ignores the vehicle
trajectory, which specifies how quickly the host vehicle is departing
the lane. Situations in which the vehicle is departing the lane at a
large rate of lateral velocity will be treated identically to situations
in which the vehicle has a lateral velocity of almost zero. During
large-angle trajectories, the driver may be warned too late to prevent
departure, and in small-angle trajectories, the level of risk may not be
sufficient to warrant an alert and the warning could be perceived as a
nuisance alert.

The first-order version of TLC remedies this disadvantage by including
the first temporal derivative of lateral position (lateral speed or
*v~l~*) in the equation. This equation can be expressed as follows:

$$\text{if}\begin{matrix}
 & \frac{d}{vl}
\end{matrix} \leq dT\begin{matrix}
, & \text{warn}
\end{matrix}$$

Relative to the second-order and kinematic versions of TLC, the
first-order version has the advantage of utilizing variables that are
easier to measure. Pomerlea et al. revealed that this version of TLC in
comparison to the zero-order version has the tendency of amplifying
measurement errors in lateral position at higher velocities. Because the
second temporal derivative (lateral acceleration or *a~l~*) is not
considered, this algorithm assumes that the lateral speed will remain
constant. This assumption could be considered as a disadvantage compared
to a second-order version of TLC, if the lateral speed is changing
quickly. The second-order version alleviates this disadvantage by
eliminating the assumption of constant lateral speed.

The second-order version of TLC can be expressed in the following
equation:

$$\text{if}\begin{matrix}
 \\
\frac{- vl + \sqrt{vl^{2} + 2al}}{al} \leq dT\begin{matrix}
, & \text{warn}
\end{matrix}
\end{matrix}$$

This version of TLC can further amplify measurement errors if the
measurement of lateral position may be used to estimate both lateral
speed and lateral acceleration. Lateral acceleration, however, could
alternatively be measured using an accelerometer. Only the vector of
acceleration that is orthogonal to the direction of the roadway would be
used, which could potentially differ from the vector that is orthogonal
relative to the heading of the vehicle. However, using an accelerometer
could require the addition of a sensor that might not otherwise be
present, potentially increasing the overall system cost. Although
Pomerleau et al. claimed that the disadvantages of the second-order
version of TLC may result in this version being impractical, they
exclusively used a second-order version in their later simulation
analyses.

The final version of TLC algorithm that Pomerleau et al. described, they
referred to as Kinematic TLC. This version fuses information about the
forward velocity, the yaw angle, the radius of curvature of the current
host vehicle path, and the radius of curvature of the upcoming road
segment to project how long before the host vehicle crosses the lane
line. Because this formula takes into account the upcoming curvature of
the road, it has the potential of increasing system accuracy, especially
in curve entry road segments. The disadvantage of this version of TLC is
that it is more complex and has far more demanding sensor requirements
(Pomerleau et al., 1999). Because of the more challenging sensor
requirements and greater complexity of the algorithm, this version of
TLC will not be considered for the SAVE-IT program. After weighing the
constraints of complexity and accuracy, the first and second-order
alternatives appear to be the most appropriate algorithms for the
SAVE-IT program implementation.

Tijerina, Jackson, Pomerleau, Romano, and Peterson (1996) conducted a
driving simulator study to investigate the effects of different LDW
parameters. They evaluated the driving performance of sixty volunteers.
These volunteers were distracted by a task that required them to turn
and look over their shoulders in order to count the number of horizontal
bars. Tijerina et al. compared two different algorithms, each with early
and late thresholds. The first type of algorithm used a first-order
(speed-based) TLC calculation. The second type of algorithm was referred
to as Time-to-trajectory-divergence (TTD). The TTD algorithm compared
the driver's heading with the optimal heading, defined as the heading
that would bring the vehicle to the center of the lane a fixed distance
ahead. When the optimal and actual headings differ by a specified
threshold, a LDW alert is triggered. For the early and late TLC
conditions, thresholds of 0.7 s and 0 s were selected respectively. For
the early and late TTD conditions, threshold arc separations of 0.55 m
and 0.75 m were selected respectively. For both early and late TTD
conditions, Tijerina et al. used a look-ahead time of 1.2 s and a TTD of
1.13 s.

Results indicated that there were significantly more steering reversals
for the TLC algorithm than the TTD algorithm, suggesting that drivers
expended more effort to control the vehicle in the TLC condition. There
were also more steering reversals in the TLC condition than in the
control condition, where no LDW system was implemented. Drivers
subjectively indicated a preference for the TLC algorithm over the TTD
algorithm and Tijerina et al. suggested that TLC appeared to lead to a
greater benefit than TTD under high hazard situations. The difference in
the number of roadway departures in the LDW groups (1) and control group
(5) approached significance, indicating that the LDW system may have
provided a measurable safety benefit.

Steering reaction times to the lateral disturbance ranged from 0.3 to
0.9 s across conditions. Although participants indicated a preference
for the later onset algorithms than the earlier onset algorithms, the
mean number of lane exceedences to the left was smaller in the earlier
warning conditions (2.4) than in the later warning conditions (5.7), and
both were smaller than in the control condition (10.67).

### 9.4.2 Driver Vehicle Interface

In addition to their comparisons of different LDW algorithms, Tijerina
et al. (1996) investigated the effects of the driver vehicle interface.
Tijerina et al. compared haptic, auditory, and haptic-plus-auditory
display modalities and compared directional displays (that indicated to
the drivers in which direction they were drifting) with non-directional
displays. The auditory display provided a half-second tone to the driver
from the direction in which the vehicle was drifting when a directional
display was implemented and from the center when a non-directional
display was implemented. When the haptic condition was coupled with a
non-directional display, the steering wheel would vibrate for a
half-second at 10 Hz to alert the driver. However, when the haptic
condition was coupled with a directional display, the steering wheel
would provide a 0.5-s 2-Nm torque in the direction that would reduce
lane drift. The lack of any statistically significant effect of the
display modality suggests that the haptic condition was as effective as
the auditory condition. Subjective judgments revealed little difference
in preferences for the haptic or auditory stimuli, however, subjects did
indicate that they preferred using only one modality to combining haptic
and auditory stimuli. The directional displays resulted in fewer lane
exceedences to the left and were rated as being more preferable compared
to the non-directional displays. In a similar study, Suzuki (2002)
revealed that although stereo (directional) audio did not decrease
driver reaction times to the stimulus, almost all drivers preferred
stereo audio. This directionality could also help to disambiguate the
warning stimulus from potential other warning systems that may be
present in the vehicle (e.g., Forward Collision Warning)

Haptic stimuli may be a promising alternative to auditory stimuli,
because they may be less annoying to the driver (Pohl and Ekmark, 2003)
and because Suzuki (2002) and Sato, Goto, Kubota, Amano, and Fukui
(1998) found that drivers react faster to haptic steering wheel warnings
than auditory warnings when drivers were not anticipating the warning
condition. However, there may be a potential safety drawback in using
directional haptic steering torque. Several researchers have observed
that many drivers in simulator experiments have responded to steering
wheel torque feedback in the opposite direction from that which would
correct the lane departure (Suzuki, 2002; Bishel, Coleman, Lorenz, and
Mehring, 1998). Based on drivers' comments, Suzuki (2002) suggested that
this occurs because drivers mistakenly perceive the torque stimulus as a
sudden lateral disturbance (e.g., wind gust) that they must manually
correct. Suzuki (2002) also observed that even when driver expected a
lane departure warning, one quarter of drivers responded in a way that
would amplify the lane departure. This response-reversal phenomena
indicates that more research is required before steering wheel torque
can be used to communicate a LDW alert.

Other possible sources of haptic stimuli that may be appropriate for an
LDW system include steering wheel vibration or seat vibration. One of
the possible drawbacks of using a vibrating steering wheel is that it is
difficult to implement in such a way that the vibration signal is not
masked by road vibration. Furthermore, the level of vibration required
to overcome the effects of road vibration may begin to disrupt vehicular
control. Relatively little research has focused on evaluating using
haptic seat stimuli to communicate lane departure alerts. Although this
stimulus-response mapping is not as clear as a haptic steering wheel
stimulus, seat vibration could be used to mimic rumble strips. If this
association is successful, many drivers may be able to understand the
meaning behind a seat vibration stimulus quite rapidly. Seat vibration
can also be implemented directionally by placing one vibration source on
each side of the seat. The directional nature of this stimulus could
also help to communicate the meaning of lane departure.

Little research has investigated using a visual display to indicate lane
departure warnings. Tijerina et al. did not include a visual stimulus in
their analyses, because they argued that the visual display could
distract the driver when visual attention is required. Based on the same
arguments for using a HUD with an FCW system (see Section 9.3.6.1),
visual displays may have the advantage that they may be less annoying to
the driver and may more effectively communicate the meaning of the
warning. A visual-only/cautionary warning could also be useful for
demonstrating to the driver that the LDW system is accurately detecting
lane deviations that may otherwise be suppressed due to use of turn
signal or other indications of driver intention (e.g., sudden steering
wheel inputs).

If a visual display is used for the LDW system, it will be incorporated
into the same display area in which FCW alerts are presented to the
driver. To differentiate LDW alerts from BSW alerts, it may present a
line or pair of lines (representing lane markings), rather than
trapezoid, to communicate the concept of the lane boundary. Although
some pilot testing would be required, the display might appear similar
to either of those displayed in Figure 9.11. The concept on the left
displays a rear-end perspective that could accompany a similar
perspective that was used for FCW in the ACAS FOT. The concept on the
right uses a top-down perspective that may be more appropriate if more
warning systems than FCW and LDW are being used on a single platform.

![](media/image47.png){width="1.75in" height="1.3020833333333333in"}

***Figure 9.11.** LDW display incorporated into the main safety warning
countermeasures display area using a rear-end perspective (left) or
top-down perspective (right).*

In the same way as when a brake pulse is used to warn the driver for an
FCW alert, using a directional steering torque for an LDW alert blurs
the line between warning and autonomous control. If the driver does not
resist the steering torque, it will begin to counteract the problem that
is causing the alert. If the reverse steering torque is of sufficient
magnitude, the driver may not be required to respond at all. Schumann,
Godthelp, and Hoekstra (1992) argued that using a stimulus that
counteracts the threat (what they referred to as an "active control
device") maximizes stimulus-response compatibility. In support of the
European GIDs program, they investigated using a continuous corrective
steering torque that was related to the amount of steering error. The
system appeared to be an effective means of aiding the driver's lane
keeping performance. In a later study, Schumann, Lowenau, and Naab
(1999) observed a reduction in driver control effort (defined
objectively) for a system that continuously provided steering torque in
proportion to the steering error. However, because lane-keeping, like
adaptive cruise control, represents autonomous control rather than a
warning system, systems that utilize continuous steering torque to
counteract lane deviation are beyond the scope of the SAVE-IT program.

### 

### 9.4.3 Nuisance Alerts

Nuisance alerts are likely to be just as problematic in LDW systems as
they are in FCW systems. Pomerleau et al. (1999) estimated that
police-reported road departures occur on average once every 84 years of
driving. If road-departure is truly a "once-in-a-life time" event, it is
likely that nuisance alerts will greatly outweigh true alerts. To
compensate for the low rate of true threats, system designers must be
careful to ensure that nuisance alerts are kept to a minimum.

This problem is further made difficult by the large amount of variation
of natural lane-keeping performance across individuals and conditions
(Pomerleau et al., 1999). Pomerleau et al. observed that drivers tend to
spread out to cover the width of the lane, so that in wider lanes there
is a wider distribution of lane positions. Their analyses also revealed
that drivers of most passenger vehicles are biased to the right of the
lane center, except when driving on country roads, where there is less
risk of a head-on collision. Based on their simulation analyses,
Pomerleau et al. estimated that their system could achieve a 92 percent
protection rate in a passenger car with a nuisance-alert rate of 1 per
hour, assuming 6-ft of maneuvering room on both sides of the lane.
Assuming only 4-ft of maneuvering room on both sides, they estimated
that their system could achieve a 59 percent protection rate in
passenger cars, with less than 2 nuisance alerts per hour. Pomerleau et
al. recommended using a minimum operating speed of 35 mph, because 76
percent of inattention and steering-wheel-relinquish SVRD accidents
occur at speeds of 35 mph or higher and almost 100 percent of SVRD
accidents caused by drowsy drivers occur at speeds of 35 mph or higher.

The early indications from the ACAS FOT program suggest that 1 or 2
nuisance alerts per hour may be excessive. To reduce the rate of
nuisance alerts while maintaining an acceptable level of system
coverage, driver state information may be extremely useful. It is
unlikely that drivers who are attentive to the driving task will depart
the roadway, unless the vehicle is traveling at an excessive speed for
the road conditions or there is some kind of mechanical failure. Because
lane drift systems are not designed to prevent collisions caused by
mechanical failure or excessive-speed, it is likely that suppressing
alerts when the driver is attentive will be an effective means of
reducing nuisance alerts.

## 9.5 STOP SIGN VIOLATION WARNING (SSVW)

Pierowicz et al. (2000) developed a simple warning system to assist in
the prevention of stop-sign violations. This stop sign violation warning
(SSVW) system monitors the driver's compliance with the stop sign by
monitoring the distance between the host vehicle and the intersection
and the speed of approach to determine the level of deceleration that is
required to stop at the intersection. When more than 0.35 g of
deceleration is required to prevent entry into the intersection, it is
assumed that the driver does not intend to stop and the system triggers
an alert to notify the driver. The following equation can be used to
calculate the level of braking required to avoid entering the
intersection (*a~p~*) as a function of the host vehicle velocity (*v*)
and the distance to the intersection (*d*).

$$ap = \frac{v^{2}}{2d}$$

The pilot testing of Pierowicz et al. (2000) indicated that drivers
brake at stop-sign-controlled intersections at a mean rate of 0.19 g.
They observed that the level of braking required to avoid entering the
intersection is diagnostic of whether the driver intends to stop. After
testing alert-criteria thresholds between 0.25 and 0.45 g, they
determined that the lower values in the range resulted in an excessive
number of nuisance alerts and that the higher values in the range
resulted in alerts that appeared to be too late. The middle value value
of 0.35 g, however, appeared to provide a reasonable balance of early
warning and driver acceptance. Pierowicz et al. also observed that there
were a large number of nuisance alerts at low speeds and small distances
from the intersection. They argued that this was due to the fact that
vehicles can stop almost instantaneously at low speeds. In the
implementation that they tested, they disabled the warning system at
speeds of less than 5 mph.

For the driver vehicle interface, Pierowicz recommended using the
combination of a HUD that displays a stop-sign symbol, a pulsed tone of
approximately 2000 Hz, and a haptic brake pulse. In their testing, the
stop-sign symbol was subjectively rated as being far more effective than
the other alternatives. On a five-point scale that indicated the level
of meaningfulness, where 5 corresponded to "extremely meaningful", the
stop-sign symbol was rated with a mean of 4.94 and a standard error of
0.06. The SSVW could easily be implemented in the safety warning
countermeasures display area, as illustrated in Figure 9.12.

To determine the distance between the host vehicle and the intersection,
the system can use a GPS signal in combination with a digital
map-matching system. Unfortunately, Pierowicz et al. (2000) discovered
that the current digital maps that are available do not provide
sufficient accuracy to support this system. In addition, the digital
maps did not indicate the type of traffic control system used at each
intersection. To remedy this problem, Pierowicz et al. augmented the map
datafile for their testing area to provide higher precision and
inclusion of the type of traffic control.

![](media/image37.png){width="0.5098425196850394in"
height="0.8429133858267717in"}![](media/image38.png){width="0.19724409448818897in"
height="0.46811023622047243in"}![](media/image39.png){width="0.19724409448818897in"
height="0.46811023622047243in"}![](media/image40.png){width="0.37440944881889765in"
height="0.610236220472441in"}![](media/image35.wmf){width="1.1791338582677164in"
height="1.847244094488189in"}![](media/image41.wmf){width="1.1791338582677164in"
height="1.847244094488189in"}![](media/image43.png){width="0.37283464566929136in"
height="0.6074803149606299in"}In the United States, there are
approximately 198 thousand accidents caused by stop-sign violations
(Pierowicz et al., 2000). The SSVW is a relatively simple system that
may be able to reduce this number.

***Figure 9.12.** An SSVW display incorporated into the main safety
warning countermeasures display area.*

## 9.6 BLIND SPOT WARNING (BSW)

The Blind Spot Warning (BSW) system is designed to prevent lane
change/merge collisions by alerting the driver about the presence of a
vehicle in the driver's blind spot. Because these systems merely alert
the driver about the presence of an object in the sensor-coverage area,
the algorithms are relatively simple. The challenge of designing BSW
systems is to design the system so it is not overly annoying. The
presence of a vehicle in the coverage-zone of a blind-spot sensor is not
an infrequent occurrence. If the driver is notified with an intrusive
warning every time an object is detected, the driver may find the system
to be intolerable. To prevent this from occurring, systems should be
designed to warn the driver only when there is a threat of collision. A
threat of collision only exists when an object is detected and the host
vehicle is currently or will soon be moving in that direction. The NHTSA
Benefits Working Group (1996) estimated that drifting accidents, where
the driver unintentionally changes lanes, account for approximately 17
percent of all lane change/merge collisions. For most of the other
classes of lane change/merge accidents, the driver intends to change
lanes but may be unaware of the presence of the other vehicle. BSW
systems therefore need to monitor both lateral movement and intent to
determine whether a warning is required.

Because over three-quarters of all lane change/merge collisions occur
with relative speeds of less than 15 mph, the blind spot sensor can be
of relatively short range, extending no more than one or two car lengths
behind the host vehicle (Young, Eberhard, & Moffa, 1995). The NHTSA
Benefits Working Group suggested that the sensor should extend one lane
away from the host vehicle. Although this range would be sufficient for
detecting most relevant obstacles, Young et al. suggested that ideally
the sensor should extend two lanes over to detect the threat of both the
host vehicle and POV simultaneously changing into the same lane. To
allow a 2-s reaction time for relative speeds of 15 mph, Young et al.
argued that a zone length of 50 ft fore and aft is required. To provide
the same 2-s reaction time for relative speeds of 30 mph, the zone
length would need to be doubled to 100 ft fore and aft.

Mazzae and Garrott (1995) reviewed several existing BSW systems that
were currently on the market. They described a set of desirable features
that appeared to separate the more acceptable systems from the more
annoying systems. Among this set they included providing a simple and
straightforward interface with a visual display located on or near the
line of sight of the appropriate side-view mirror. They recommended that
systems should indicate the presence of an object in the blind spot with
a visual-only display. Auditory stimuli should be reserved only for
imminent alerts, when the turn signal is activated or some other
indication is present that the driver intends to change lanes.

Chovan, Tijerina, Alexander, and Hendricks (1994) similarly recommended
using the turn signal to detect the driver's intention to change lanes
in order to reduce nuisance alerts. However, Lee, Olsen, and Wierwille
(2004) observed that drivers only used the turn signal on average 44
percent of the time before they changed lanes during a naturalistic lane
change field test. Chovan et al. also stated that drivers do not always
use the turn signal before a lane change maneuver, however, they
suggested that using the turn signal to trigger the BSW system could
actually promote turn signal use. They argued that drivers may activate
the turn signal in order to request BSW information. Nevertheless,
Chovan et al. suggested that BSW systems could use a more sophisticated
intent-detection algorithm that monitors the driver-vehicle system for
idiosyncratic combinations of variables that may signal an intention to
change lanes.

Tijerina and Hetrick (1997) argued that a turn-signal-only system would
not be capable of preventing slow-drift crashes, when the driver
unintentionally changes lanes. To counteract this problem, they
suggested that the BSW system should utilize three stages of warning. In
stage 1 the sensor detects an object in the blind spot but the turn
signal is not active and there is no indication that the vehicle is
currently moving toward the blind spot. For this stage of warning,
Tijerina and Hetrick recommended providing a visual-only stimulus. In
stage 2 the sensor detects an object in the blind spot and the turn
signal is activated. For this stage of warning, they recommended
presenting a visual-only "augmented alert", perhaps where the icon
flashes to attract the driver's attention. Stage 3 represents an
imminent threat, when the sensor detects an object in the blind spot and
the host vehicle is moving toward the occupied blind spot area. To
prevent the BSW from annoying the driver, Tijerina and Hetrick
recommended reserving multi-modality (including auditory or haptic in
addition to the visual stimulus) alerts for this imminent situation.

Tijerina and Hetrick (1997) also recommended integrating the visual
display into the mirror systems. Similar to Chovan et al.'s argument for
using a turn-signal-activation rule, they argued that implementing the
display in the mirrors could promote mirror check behavior. Lee, Olsen,
and Wierwille (2004) observed that in the 3 s prior to lane change, all
drivers glanced at the forward-center area, and the average number of
glances to the forward-center area was over two for both left and right
lane changes. Glances toward the mirrors, however, were less
predictable. For left lane changes, drivers glanced at the center
rear-view mirror with a probability of 0.53 and at the left side-view
mirror with a probability of 0.52. For right lane changes, drivers
glanced at the center rear-view mirror with a probability of 0.55 and
only glanced at the right side-view mirror with a probability of 0.21.
The lower probability of using the right side-view mirror suggests that
unless Tijerina and Hetrick are correct in assuming that the display
will promote mirror-checking behavior, a display located in the right
side-view mirror could easily be missed. The prevalence of glances
toward the forward-center area suggests that a HUD would be an effective
media for displaying BSW information. If the SAVE-IT program
investigates BSW systems further, it is likely that BSW visual
information will be presented on the HUD (in the main safety warning
countermeasures area) and redundantly in the side-view mirrors, to
ensure that the driver has access to the BSW information. If such a
system is included, the HUD display is likely to appear similar to that
shown in Figure 9.13.

Like the other countermeasures, the BSW system could suffer from
nuisance alerts. Objects that do not pose a real threat may activate the
system. For example, a driver may activate the turn signal with an
intention to make a right hand turn while a guardrail is present in the
right blind spot. One way to differentiate vehicles from other objects
is to determine whether the object is moving. However, long continuous
targets, such as guardrails, frequently appear to the sensor to be
moving at the same speed as the host vehicle. Perhaps the most effective
way to reduce these kinds of nuisance alerts would be to determine
whether there is another lane on the other side of the vehicle. If there
is no lane present but the vehicle is approaching an intersection, it
could potentially be assumed that the driver is intending to turn at the
intersection rather than change lanes. A more simple method of
mitigating these nuisance alerts is to impose a minimum speed
requirement, suppressing alerts below a specified speed.

![](media/image48.png){width="0.19724409448818897in"
height="0.46614173228346456in"}![](media/image49.png){width="0.19645669291338583in"
height="0.4653543307086614in"}![](media/image43.png){width="0.37283464566929136in"
height="0.6082677165354331in"}

***Figure 9.13.** A BSW display incorporated into the main safety
warning countermeasures display area. This sequence shows a left BSW
warning with the icons from left to right corresponding to BSW
system-not-activated (e.g., when the speed is below a minimum level),
BSW system-activated but no object detected, object present in blind
spot, and imminent alert level).*

## 9.7 ADAPTIVE ENHANCEMENTS

The primary purpose of the SAVE-IT program is to investigate adaptive
enhancements to vehicle information systems, including both distraction
mitigation and safety warning countermeasures. This section will focus
on methods of using driver state information to adaptively enhance
safety warning countermeasures. Piersma (1993) defined an adaptive
system in the following manner (p. 325).

> *A system is adaptive if its performance is both sensitive to its
> environment and changed in ways to improve the quality (on average) of
> the system's performance. A system is adaptive if it changes its
> behavior mostly for the better dependent on the momentary
> circumstances.*

An adaptive system attempts to create the optimal environment to support
effective human-machine interaction (Hancock & Verwey, 1997). Piersma
described several sources of information to which a system can adapt in
an attempt to achieve this goal. These sources of information included
the driver preferences, the secondary tasks currently being performed,
the current levels of workload, the traffic environment, the driving
tasks currently being performed, and the individual driving history.
Although several researchers have examined methods for adapting a system
to the driver preferences or previous driving history (e.g., Onken &
Feraric, 1997), this task of the SAVE-IT program will specifically focus
on adapting the safety warning countermeasures to information concerning
the current levels of workload, the traffic environment, and the driving
tasks currently being performed (or intended). In the terms used by the
SAVE-IT program, these Safety Warning Countermeasures (Task 9) may be
adapted to Driving Task Demand (Task 2), Cognitive Distraction (Task 5),
Visual Distraction (Task 7), and Intent (Task 8). Perhaps the specific
requirements of this task are more closely related to Hoedemaeker, de
Ridder, and Janssen's (2002) definition of an adaptive system (p. 7):

> *A system that in some way takes into account the momentary state of
> the driver, in particular his present level of workload, in
> determining the appropriate timing and the content of the supporting
> message or intervening activity the system will produce.*

Kraiss (1989) described four methods of adaptive user-interface
management. They included information filtering, selection of sensory
modality, display formatting and adaptive control of the display and
message (e.g., alarm sequencing). In the GIDS (Generic Intelligent
Driver Support System) program in Europe, the prototype vehicle was
designed to suppress or postpone lower-priority messages when the level
of driving demand was high (Hoedemaeker et al., 2002). Hoedemaeker et
al. reported that 60 to 70 percent of the drivers considered this system
to be useful and 80 percent anticipated that it would enhance safety.
Although adaptive systems have shown some promise, Hoedemaker et al.
warn that many efforts to introduce adaptive support systems have failed
because of poor user acceptance. In particular, they argued that systems
that intervene with the driving task are viewed as being an intrusion
into the drivers responsibility. System engineers must be careful in
designing the systems to ensure that the driver still feels in control
and understands the behavior of the system.

Hancock and Verwey (1997) described the problem of designing adaptive
systems as determining how and when the system should adapt without
contradicting the typical human adaptive response. Changing the nature
of the system has the potential of leading to perceptions of system
inconsistency. The driver may observe the system to behave one way at
one moment only to later observe different behavior. If implemented
poorly, the driver may perceive the system to be unpredictable, which
could result in poor driver acceptance.

Piersma (1993) argued that humans process information more quickly if it
is in a familiar format, and therefore suggested that dynamically
altering modes of information presentation should be kept to a minimum.
Farber and Farber (1984; cited in Piersma 1993) clamed that spoken
warnings tend to be perceived as blaming the driver, especially when
other passengers are present. For example, if the car verbally instructs
the driver to increase the distance to the lead vehicle, the message may
imply that the driver is performing inadequately. Farber and Farber
suggested that the alert should be worded instead as a suggestion to the
driver such as "check the distance to the car in front". It also seems
likely that if the verbal message is designed to appear informational
rather than instructional, such as "the lead vehicle is braking" or "new
stationary vehicle detected", the driver may perceive the message as
being more tolerable.

![](media/image36.png){width="0.5318897637795276in"
height="0.4590551181102362in"}![](media/image43.png){width="0.25354330708661416in"
height="0.4035433070866142in"}![](media/image43.png){width="0.25354330708661416in"
height="0.402755905511811in"}![](media/image37.png){width="0.3720472440944882in"
height="0.13543307086614173in"}![](media/image52.png){width="0.5070866141732283in"
height="0.30748031496062994in"}![](media/image36.png){width="0.34448818897637795in"
height="0.13070866141732285in"}Another problem that we may anticipate is
oscillations caused by the closing of the information loop between human
and machine. In the worst-case hypothetical scenario that is depicted in
Figure 9.14, a driver is engaging in a distracting non-driving task
while following a lead vehicle. The driver-state monitor detects driver
inattention and so the warning threshold is changed, which leads to an
imminent warning. The imminent warning attracts the driver's attention
so that the driver becomes attentive. When the driver is diagnosed as
being attentive, the warning threshold changes and the FCW status is
dropped back to a "vehicle-detected" state. When the driver observes the
"vehicle-detected" state, he resumes the distracting non-driving task
activity and the loop continues indefinitely.

***Figure 9.14.** A "worst-case" hypothetical problem of closed-loop
oscillations in an FCW system.*

As described, this problem represents a "worst-case" adaptive system
that is poorly designed. The system designer could use several commonly
practiced engineering techniques such as hysteresis or using longer
time-scales to prevent this problem. Although this specific example is
unlikely to occur unless the system is poorly designed, it does
illustrate some of the potential pitfalls that we must anticipate in
order to avoid. Perhaps the most effective means of ensuring that these
kinds of problems are avoided is to engage in an iterative process of
testing and refinement, in order to develop a system that behaves in an
expected and appropriate manner. In the context of aviation, Billings
(1997) argued that the adaptation must be predictable so that the user
can form a clear mental model of the system's present and expected
behavior. The remainder of this section will discuss adaptive
enhancements that apply specifically to the FCW, LDW, SSVW, and BSW
systems.

### 9.7.1 Forward Collision Warning (FCW)

The feedback from the ongoing ACAS FOT program has highlighted many of
the challenges associated with FCW nuisance alerts. From the early ACAS
FOT subject comments, it appears that they find warnings to be more
useful when they are inattentive. Otherwise, the warning system may only
be providing information that is already known. Horowitz and Dingus
(1992) summed up the problem in the following statement (p. 1011):

> *Ideally, warnings should be issued only when the driver is not
> focusing on the road or when a dangerous change occurs rapidly in the
> position and speed of a vehicle in front. However, as long as the
> collision warning system does not have information about the driver's
> state of attention, warnings will likely be issued even when the
> driver is fully aware of the danger. For example, situations may occur
> when the driver prepares to apply the brakes or steers to avoid
> danger, while at the same point in time the collision warning system
> issues a warning startling the driver. The driver then has to
> interpret the warning, thus his attention is shifted from action to a
> new unexpected stimulus. This adds to the cognitive lead and
> potentially leads to stress, delay in action, and incorrect
> responses.*

One method for mitigating excessive numbers of nuisance alerts is to
adjust the warning criteria so that the alert timing is later. This has
the effect of filtering out some of the nuisance alerts. However,
McGehee and Brown (1998; cited in Lee, McGehee, Brown, and Reyes, 2000)
claim that poorly timed warnings may actually undermine driver safety.
If this is true, then unilaterally changing the bias on the alert
criterion may not solve the FCW nuisance alert problem. Instead, the FCW
system may have to become more intelligent, taking into account both
environmental and human states for determining the level of threat. If
the alert criterion is modified, it must be modified in real time as a
function of driver state, taking into account variables such as driver
distraction.

As discussed in the previous sections, one of the primary inputs into
the FCW threat-assessment algorithm is a prediction of the driver's
brake reaction time (BRT). Drivers' BRTs have been demonstrated to
change as a function of driver distraction. For example, Lee, Caven,
Haake, and Brown (2001) reported that drivers responded to a lead
vehicle braking at 2.1 m/s^2^ with a BRT of 1.32 s when distracted by an
e-mail task, compared to 1.01 s without. Similarly, Lee et al. (2002)
reported that drivers who were warned by an FCW system responded to a
0.4 g decelerating lead vehicle with a BRT of 1.04 s, when distracted,
compared to 0.76 s without. One simple method for adaptively enhancing
FCW systems is to change the BRT estimate as a function of the driver's
level of distraction. Adjusting the driver's BRT will ensure that
distracted drivers receive earlier warnings and attentive drivers
receive later warnings, or in some situations, no warning at all.

The data from the CAMP FCW program suggests that drivers may accept
adaptive warning timing. In one of the conditions of these experiments,
experimenters distracted drivers by instructing them to search for a
telltale light that did not exist (Kiefer et al., 1999). For this
condition Kiefer et al. used an assumed BRT of 1.5 s for the threat
assessment algorithm. After participants had performed in the surprise
condition, they performed the task without being distracted, expecting
the lead vehicle braking. Because Kiefer et al. predicted that
participants would react faster in this condition, they used an assumed
BRT of 0.52 s. In the distracted/surprise condition participants scored
the timing of the algorithm (using a BRT of 1.5 s) on average with a
value of 4.4, where a 1 corresponded with "much too early" and a 7
corresponded with "much too late". In the non-distracted/follow-on
condition participants scored the timing of the algorithm (using a BRT
of 0.52 s) on average with a value of 4.5. The measured BRTs for the
distracted/surprise condition and the non-distracted/follow-on condition
were 881 and 683 ms respectively. The lack of a difference between the
ratings for the timings assuming a BRT of 0.52 and 1.5 s, may provide
some preliminary evidence that drivers may accept adaptive warning
timing.

A more extreme option that the SAVE-IT team may wish to consider is
suppressing alerts completely when the driver is attentive. Although
this option is more likely to result in a failure to detect a true
threat, it is likely to be a powerful means of suppressing nuisance
alerts. The decision on whether to implement this alert suppression will
likely depend on the success of the adaptive timing adjustment and on
the subjective data regarding whether this alert suppression technique
is likely to be acceptable. If the BRT timing adjustment is sufficiently
successful in reducing nuisance alerts, the system may not need to
resort to the more extreme option of suppressing alerts completely.

Another option for alert suppression is to suppress alerts when a
relevant driver intention is detected. Because nuisance alerts are
common during and preceding driver maneuvers such as merges and lane
changes, suppressing alerts during these periods should be an effective
means of eliminating many nuisance alerts. One possible drawback to this
approach is that prior to maneuvers, drivers may miss important
transitions in the state of the lead vehicle while searching for an
opportunity to engage in a maneuver. Thus, suppressing alerts while the
driver intends to change lanes may actually lead to some misses of true
threats.

One final adaptive enhancement that will be considered is to use an
auditory stimulus for cautionary states when the driver is engaged in
high levels of visual distraction. One problem with not accompanying
cautionary alerts with an auditory stimulus is that when the driver gaze
is directed away from the forward scene, the cautionary warning icon on
the HUD will produce little benefit. During these situations, the driver
will have to rely completely on the imminent warning. Although
adaptively modifying the alert timing as a function of distraction-level
will assist in this regard, a more direct means of assisting drivers in
this situation is to provide an auditory stimulus when the driver is
glancing away from the forward scene. In Section 9.3.6.1, it was argued
that acoustic rather than voice stimuli are preferred for communicating
to the driver quickly. However, this stimulus would be used to accompany
a cautionary rather than an imminent warning, and in this situation the
driver is likely to be relatively "out of the loop". For
visual-distraction cautionary alerts, the FCW system could alert the
driver with a cautionary alert that verbally informs the driver of what
is occurring. For example, the auditory caution alert could articulate
"lead-vehicle braking" or "new stopped object detected". In these
situations, a descriptive voice message may be an effective means of
quickly bringing the driver back into the loop. These and other methods
of adaptive enhancement will be developed and evaluated in Task 9
(Safety Warning Countermeasures).

### 9.7.2 Lane Drift Warning (LDW)

Like FCW, it is likely that Lane Drift Warning (LDW) systems will
produce a challenging problem of excessive nuisance alerts rates. Lane
keeping performance varies widely across situations and environmental
parameters and the system may have a difficult task in distinguishing
the beginning of an actual lane departure from normal lane-keeping
variability. Adaptive enhancements are likely to produce great
assistance with this problem. Unless the driver is traveling at an
excessive speed for the road conditions or the vehicle is experiencing
some kind of mechanical failure, attentive drivers tend to be extremely
reliable in keeping the vehicle on the roadway. If the LDW threshold is
adaptively modified to reflect this fact, the system should produce few
nuisance alerts while the driver is attentive. When the driver is not
attentive, information regarding the drifting of lane position should be
considered to be quite relevant and beneficial. Like the FCW system, the
LDW system could either suppress warnings when the driver is attentive
or adaptively modify the warning threshold as a function of driver
attentiveness, or some combination of both.

Many researchers have suggested that LDW systems could also benefit
greatly from information about the driver's intention. For example,
Pomerleau et al. (1999) wrote (p. 24):

> *A LDWS should attempt to determine driver intentions in order to
> minimize nuisance alarms. It should attempt to avoid issuing warnings
> for intentional lane excursions which can result when performing a
> lane change, driving onto the shoulder to avoid obstacles in the
> travel lane, or stopping beside the road for a vehicle or passenger
> emergency.*

The reliable detection of driver intention would allow the LDW system to
distinguish between the driver unintentionally drifting out of the lane
from the driver drifting out of the lane for the purposes of a lane
change or turning maneuver. This provides a strong rationale for the LDW
system suppressing alerts during and preceding certain maneuvers.

If directional steering-wheel force feedback is used, the gain factor
relating the magnitude of the counterforce to the amount of
steering-wheel error could be modified as a function of the driver's
level of distraction. During periods of driver distraction or
inattentiveness the gain could be set to a high level, so that the
haptic stimulus is relatively intrusive and may even counteract the
drifting. However, when the driver is attentive or intends to engage in
some maneuver, the gain could be set to a low level, so that the haptic
stimulus does not annoy the driver or oppose the steering the driver's
behavior. Yuhara and Tajima (2001) investigated adapting an intelligent
steering system as a function of driver intention. Although this was not
an LDW or lane-keeping system, the intelligent steering system
adaptively adjusted the weights on lateral position and yaw angle as a
function of driver mood and intention. Adaptively modifying the alert
system as a function of driver state is likely to greatly enhance LDW
countermeasures.

### 9.7.3 Stop Sign Violation Warning (SSVW)

The Stop Sign Violation Warning (SSVW) system uses a relatively simple
algorithm to determine whether it is likely the driver will stop at the
upcoming intersection. The SSVW is also a relatively recent development
and to date has received little attention in the literature. Because of
this, the development of adaptive enhancements will be simple and
relatively preliminary in nature. If this system is tested during the
SAVE-IT program, we will develop a more comprehensive understanding of
this system that may guide the development of further adaptive
enhancements.

It seems that the most likely enhancement to be made to SSVW systems is
to use driver-state information to adaptively modify the warning
threshold. If an intention to brake is detected, the SSVW system could
be suppressed or if the driver appears to be relatively attentive, the
SSVW system could increase the a~p~ threshold parameter (see Section
9.5) to a higher level. These simple enhancements may serve to reduce
the number of nuisance alerts and increase driver tolerance.

### 9.7.4 Blind-spot Warning (BSW)

One potential application of driver state information for enhancing
Blind Spot Warning (BSW) systems is to utilize driver intent to enable
alerts. Rather than relying on monitoring the turn signal for
intent-detection, Chovan et al. suggested that

> *A better alternative for designing lane change crash avoidance
> systems would be one that was keyed off of a signal of the driver's
> intent. Turn signals provide this but drivers do not always use them
> properly. It may be possible to discover other indicators of the
> driver's intent to change lanes, if not the start of a lane change.
> However, if such indicators can be found (e.g., idiosyncratic
> combinations of lane position, steering wheel movements, or eye
> movements), they may take appreciable time to collect and collate into
> a warning or signal for FACS intervention.*

To help prevent accidents caused by the driver changing into an
already-occupied lane, the BSW system would monitor the sensor coverage
area for the presence of an object and monitor the driver for signs of
intent. When a driver intends to change into an occupied lane, the BSW
could provide an early warning to the driver. If the sensor detects an
object in its coverage area but the driver does not intend to change
lanes, the BSW would merely display the presence of the object in a
non-intrusive manner. Using driver intent to trigger BSW alerts may help
to provide a system that delivers more timely and appropriate
information to the driver.

The NHTSA Benefits Working Group (1996) estimated that drifting
accidents account for approximately 17 percent of all lane change/merge
collisions. To provide the driver with coverage of accidents when the
driver does not intend to change lanes, the BSW system could monitor the
vehicle for lateral movement toward an occupied blind spot area. To
mitigate the occurrence of nuisance alerts, the threshold for the
parameter governing this warning could be adjusted as a function of
driver distraction. When the driver is distracted, the algorithm could
be adjusted to be more sensitive to the threat of drifting
lane-change/merge accidents.

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

[^2]: This most recent analysis of collision statistics focused on
    crashes involving at least one light-vehicle. These crashes
    represent 96 percent of all 6.4 million police-reported collisions.

[^3]: Distance headway is defined as the distance between the front
    bumper of the following vehicle and the rear bumper of the lead
    vehicle. Headway is often expressed in units of time, referred to as
    "time-headway" where distance headway is divided by the speed of the
    following vehicle.

[^4]: A Weber fraction is the amount of stimulus change divided by the
    initial value of the stimulus required for the perceptual system to
    detect a just noticeable difference in the stimulus

[^5]: See Gibson and Crooks (1938).

[^6]: Smith et al. (2002) reviewed the literature and found that
    responses consistently vary as a function of variables such as
    relative speed and object size, indicating it is likely that a
    variable other than time-to-collision is being used.

[^7]: CAMP is a partnership between several OEMs (including Ford and GM)
    to research pre-competitive research questions.

[^8]: Required deceleration is the minimum constant level of
    deceleration required to avoid a crash.

[^9]: Average Actual deceleration is the average level of deceleration
    that the host vehicle actually adopts in avoiding the collision. In
    the CAMP 1999 study the actual deceleration was greater than the
    required deceleration because whereas the required deceleration
    predicted drivers to avoid collision by a negligible margin, drivers
    actually decelerated sufficiently to leave a gap between host and
    lead vehicles at the conclusion of the event. This is likely to be
    the result of drivers initially overestimating the level of threat.

[^10]: For safety reasons, a surrogate target that could sustain small
    impacts without damage was used in place of a lead vehicle. From the
    rear perspective, this surrogate target appeared to be quite
    realistic.