[Federal Register Volume 66, Number 9 (Friday, January 12, 2001)]
[Rules and Regulations]
[Pages 3388-3437]
From the Federal Register Online via the Government Publishing Office [www.gpo.gov]
[FR Doc No: 01-973]



[[Page 3387]]

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Part XIII





Department of Transportation





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National Highway Traffic Safety Administration



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49 CFR Part 575



Consumer Information Regulations; Federal Motor Vehicle Safety 
Standards; Rollover Resistance; Final Rule

  Federal Register / Vol. 66, No. 9 / Friday, January 12, 2001 / Rules 
and Regulations  

[[Page 3388]]


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DEPARTMENT OF TRANSPORTATION

National Highway Traffic Safety Administration

49 CFR Part 575

[Docket No. NHTSA-2000-8298]


Consumer Information Regulations; Federal Motor Vehicle Safety 
Standards; Rollover Resistance

AGENCY: National Highway Traffic Safety Administration (NHTSA), DOT.

ACTION: Response to Comments, Notice of Final Decision.

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SUMMARY: The agency has concluded that consumer information on the 
rollover risk of passenger cars and light multipurpose passenger 
vehicles and trucks will reduce the number of rollover crashes and the 
number of injuries and fatalities from rollover crashes. This 
information will enable prospective purchasers to make choices about 
new vehicles based on differences in rollover risk and serve as a 
market incentive to manufacturers in striving to design their vehicles 
with greater rollover resistance. The consumer information program will 
also inform drivers, especially those who choose vehicles with poorer 
rollover resistance, that their risk of harm can be greatly reduced 
with seat belt use to avoid ejection.
    The agency has decided to use the Static Stability Factor to 
indicate rollover risk in single-vehicle crashes and to incorporate the 
new rating into NHTSA's New Car Assessment Program (NCAP). As part of 
these ratings, the agency also has decided to note vehicles that are 
equipped with ``electronic stability control'' technology, which may 
reduce the risk of a vehicle getting into an incipient rollover 
situation. This notice summarizes the comments received in response to 
the agency's June 1, 2000 Request for Comment regarding the addition of 
rollover ratings based on SSF to NCAP, our response to those comments, 
and the procedures and protocol we will use to implement a new rollover 
consumer information program.

FOR FURTHER INFORMATION CONTACT: For the most up to date vehicle star 
ratings call the Auto Safety Hotline at 888-327-4236 or refer to 
NHTSA's website at www.nhtsa.dot.gov. For technical questions you may 
contact Gayle Dalrymple, NPS-23, Office of Safety Performance 
Standards, National Highway Traffic Safety Administration, 400 Seventh 
Street, SW, Washington, DC 20590. Ms. Dalrymple can be reached by phone 
at (202) 366-5559 or by facsimile at (202) 493-2739. For public 
comments and other information related to previous notices on this 
subject, please refer to:
    DOT Docket No. NHTSA-2000-6859, Docket Management, Room PL-401, 400 
Seventh Street, SW, Washington, D.C. 20590 (hours 10:00 a.m. to 5:00 
p.m. Monday through Friday) or on the internet at www.dms.gov/search, 
and Docket No. 91-68; Notice 3, NHTSA Docket, Room 5111, 400 Seventh 
Street, SW, Washington, DC 20590. NHTSA Docket hours are from 9:30 am 
to 4:00 pm Monday through Friday.

SUPPLEMENTARY INFORMATION:
I. Introduction
II. Background
III. Discussion of Commenters' Issues
    A. SSF as a Measure of Rollover Risk
    B. NHTSA's Statistical Analysis Linking SSF to Rollover Rates
    C. Comments on Practical Problems with SSF Ratings
    D. Consumer's Ability to Understand SSF as a Measure of Rollover 
Risk in the Event of a Single-vehicle Crash
    E. The Question of Electronic Stability Control
    F. Alternative Programs Suggested by Commenters
    G. Commenters' Desire for a Minimum Standard Based on a Dynamic 
Test
IV. Rollover Information Dissemination using SSF in NCAP
V. Rulemaking Analyses and Notices
Appendix I  Statistical Analysis in Response to Comments
Appendix II  Proposed List of Test Vehicles for MY2001

I. Introduction

    This notice outlines the plan the National Highway Traffic Safety 
Administration (NHTSA) will use to incorporate a new rollover rating of 
new cars and light trucks into its existing New Car Assessment Program 
(NCAP). NCAP currently gives consumers crashworthiness ratings for new 
light vehicles in frontal and side crashes. The ratings are based on 
vehicle performance with respect to occupant injury criteria gathered 
in crash tests and are presented using one to five stars, one star for 
the highest risk and five for the lowest. We intend to use the same 
star rating system to present the risk of rollover in the event of a 
single-vehicle crash. One star would represent a Static Stability 
Factor (SSF) corresponding to a 40 percent or greater risk of a single-
vehicle crash resulting in rollover, while five stars would represent 
an SSF corresponding to a risk of less than 10 percent. Static 
Stability Factor is one-half the track width of a vehicle divided by 
the height of its center of gravity. As part of the rating based on 
SSF, the agency also has to note vehicles that are equipped with 
``electronic stability control'' technology, which may reduce the risk 
of a vehicle getting into an incipient rollover situation.
    The agency requested comments on its tentative decision to 
implement such a program on June 1, 2000.\1\ The closing date for 
comments was August 30, 2000. Twenty-five commenters responded. This 
notice addresses the major issues presented by the commenters, our 
response to those comments, and the procedures and protocol we will use 
to implement a rollover consumer information program based on SSF. For 
complete background and rationale for the program, please see the June 
1, 2000 notice.
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    \1\ 65 FR 34999 (June 1, 2000).
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II. Background

    Rollover crashes are complex events that reflect the interaction of 
driver, road, vehicle, and environmental factors. We can describe the 
relationship between these factors and the risk of rollover using 
information from the agency's crash data programs. We limit our 
discussion here to light vehicles, which consist of (1) passenger cars 
and (2) multipurpose passenger vehicles and trucks under 4,536 
kilograms (10,000 pounds) gross vehicle weight rating (collectively, 
``light trucks'').\2\
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    \2\ Light trucks include vans, minivans, sport utility vehicles 
(SUVs), and pickup trucks under 4,536 kilograms (10, 000 pounds) 
gross vehicle weight rating.
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    According to the 1999 Fatality Analysis Reporting System (FARS), 
10,142 people were killed as occupants in light vehicle rollovers, 
including 8,345 killed in single-vehicle rollovers. Eighty percent of 
the people who died in single-vehicle rollovers were not using a seat 
belt, and 64 percent were ejected from the vehicle (including 53 
percent who were completely ejected). FARS shows that 55 percent of 
light vehicle occupant fatalities in single-vehicle crashes involved 
rollover. The proportion differs greatly by vehicle type: 46 percent of 
passenger car occupant fatalities in single-vehicle crashes involved 
rollover, compared to 63 percent for pickup trucks, 60 percent for 
vans, and 78 percent for sport utility vehicles (SUVs).
    Using data from the 1995-1999 National Automotive Sampling System 
(NASS) we estimate that 253,000 light vehicles were towed from a 
rollover crash each year (on average), and that 27,000 occupants of 
these vehicles were seriously injured (defined as an Abbreviated Injury 
Scale (AIS) rating of at least 3).\3\ This includes 205,000

[[Page 3389]]

single-vehicle tow-away rollovers with 19,000 serious injuries. Sixty-
five percent of those people who suffered a serious injury in single-
vehicle tow-away rollovers were not using a seat belt, and 50 percent 
were ejected (including 41 percent who were completely ejected). 
Estimates from NASS are that 81 percent of tow-away rollovers occurred 
in single-vehicle crashes, and 87 percent (178,000) of the single-
vehicle rollover crashes occurred after the vehicle left the roadway.
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    \3\ A broken hip is an example of an AIS 3 injury.
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    Based on the 1995-1999 General Estimates System (GES) data we 
estimate that 241,000 light vehicles rolled over each year (on average) 
in police-reported crashes, and that 57,000 occupants in rollover 
crashes received injuries rated as K or A on the police injury scale. 
(The police KABCO scale calls these injuries ``incapacitating,'' but 
their actual severity depends on local practice. ``Incapacitating'' 
injury may mean that the injury was visible to the reporting officer or 
that the officer called for medical assistance.) This includes 205,000 
single-vehicle rollovers with 46,000 K or A injuries. Fifty-four 
percent of those with K or A injury in single-vehicle rollovers were 
not using a seat belt, and 20 percent were ejected from the vehicle 
(including 18 percent who were completely ejected). Estimates from GES 
are that 16 percent of light vehicles in police-reported single-vehicle 
crashes rolled over. The estimated risk of rollover differs by vehicle 
type: 13 percent of cars and 14 percent of vans in police-reported 
single-vehicle crashes rolled over, compared to 24 percent of pickup 
trucks and 32 percent of SUVs.
    The data presented above demonstrate that rollover crashes create a 
serious safety problem and that a reduction in the number of rollovers 
can make a significant contribution to motor vehicle safety.

III. Discussion of Commenters' Issues

    The Request for Comment (RFC) was published June 1, 2000. The 
comment period closed August 30, 2000. Twenty-five commenters replied. 
The respondents were vehicle manufacturers and their associations, 
testing laboratories, independent researchers, consumer safety groups, 
an insurance association, a trial attorney, and two consumers. Two 
commenters agreed with the inclusion of rollover rating in NCAP as it 
was presented in the RFC. The other commenters were divided among those 
who opposed the plan (manufacturers, dealers, testing labs) and those 
who thought it did not go far enough  that a minimum standard, based on 
a dynamic test, is needed for rollover (trial attorney, consumer 
groups). The commenters raised issues in four areas:
    The suitability of SSF as a measure of rollover risk,
     Whether NHTSA's statistical analysis linking SSF to 
single-vehicle rollover rates was correct,
     Whether consumers are capable of understanding the concept 
of single-vehicle crash as exposure to rollover, and
     The need for a minimum standard, or consumer information, 
for rollover based on a dynamic test.

Alternative consumer information programs for rollover prevention were 
also offered by some commenters. Those four issues and the alternative 
programs are discussed in this section.

A. SSF as a Measure of Rollover Risk

    Many respondents to the RFC believe that SSF is not a good measure 
of rollover risk for various reasons. Comments and the parties that 
made them were the following:
     NHTSA has exaggerated the importance of SSF in rollover 
crashes. Vehicles have little to do with rollover; the driver and road 
conditions bear so much of the blame that the vehicles should not be 
rated for rollover.--The Alliance of Automobile Manufacturers 
(Alliance), Association of Import Automobile Manufacturers (AIAM)
     Isuzu SSF is too simplistic. SSF ignores tire properties, 
suspension compliance, handling characteristics, antilock brakes, 
electronic stability control, vehicle shape and structure (post-impact 
rollover), and tripping factors (tires).--Alliance, University of 
Michigan Transportation Research Institute, JCW Consulting, SiSan, 
Automotive Testing Inc., Toyota, Isuzu, Honda
1. Origin of Static Stability Factor
    Static Stability Factor is not a measure of rollover resistance 
invented by the agency. It was introduced to the agency in 1973 by 
vehicle manufacturers as a scientifically valid potential substitute 
for the dynamic maneuver tests the agency wanted to develop regarding 
untripped on-road rollover.\4\ The Motor Vehicle Manufacturers 
Association (which has evolved into the present Alliance of Automobile 
Manufacturers) stated the following about SSF, ``Although this method 
does not embrace all vehicle factors relating to rollover resistance, 
it does involve the basic parameters of [sic] influencing resistance.''
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    \4\ In 1973, NHTSA published an Advance Notice of Proposed 
Rulemaking on Rollover Prevention (38 FR 9598, April 18, 1973). The 
comments cited here can be found in NHTSA Docket No. 73-10; Notice 
1, comments 11 (MVMA) and 14 (GM).
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    In 1973, all of the manufacturers opposed NHTSA's plans for a 
standard regarding rollover prevention in extreme accident avoidance 
maneuvers because of their expectation of negligible benefits, concern 
about banning vehicle types, degradation of vehicle capabilities 
including braking traction and handling performance, and unresolved 
problems with maneuver testing. General Motors presented a very 
detailed set of comments that remain relevant today. For example, its 
observations on the effect of restraint use on rollover fatality rates 
and on the breakdown of the rollover problem between multi-vehicle and 
single-vehicle crashes and on-road and off-road incidences are largely 
supported by present data. Likewise, its discussion of the problems of 
maintaining consistent pavement surface and tire traction properties, 
the use of automatic controls and outriggers, the types of maneuvers 
and their relationship to real crashes is still meaningful. We also 
think its comments regarding SSF (which it called geometric stability 
measurement) are still accurate. General Motors said:

    Resistance to rollover is mainly influenced by the following 
factors:
    1. Height of the center of gravity.
    2. Horizontal distance from center of gravity to wheel track.
    3. Capability for generating large forces in the lateral 
direction of the tire contacts due to high tire friction.
    Lateral forces sufficient for rollover can result from severe 
maneuvers under high tire-road friction conditions; from collisions 
with other vehicles, curbs, or road furniture (signs, lamp posts, 
guard rails), and from maneuvers in roadside soil capable of 
sustaining high lateral forces.

    General Motors qualified the discussion as pertaining to relatively 
simple maneuvers, but cautioned against the use of ``special'' braking 
and steering inputs for rollover maneuver tests as unrepresentative of 
vehicle operation. It also discussed the relative importance of 
secondary vehicle characteristics other than those above which are the 
components of SSF.

    It was noted in a previous section that the dominant factors in 
flat road rollover resistance are the center of gravity height, 
track width, and the ability of the tire-road interface to generate 
high levels of lateral force. Suspension geometry, component 
stiffness factors, allowable ride travel, and tire stiffness factors 
also exert a measurable influence on rollover performance. But, 
these latter factors are considered to be of secondary importance. 
It should be noted that in many cases, very careful laboratory tests 
are required to establish the influence of suspension modifications 
on rollover resistance.


[[Page 3390]]


In its conclusions, General Motors maintained that there was no safety 
need for the on-road rollover resistance standard the agency intended 
to propose and that, if the agency decided to act at all, it should 
pursue consumer information based on SSF.

    If any regulation is required, some benefit may be derived at 
minimal cost by better informing the customer of relative product 
rollover performance, so he can assess this vehicle performance 
factor in making his selection in a free market. This information 
could be based on geometric stability measurements for the full 
range of highway vehicles.

    This comment was made before the NCAP program was established to 
provide consumer information on safety performance and before the 
consumer was faced with such a large range of geometric stability (SSF) 
in non-commercial passenger vehicles. Also, most of the practical 
difficulties in seeking objective, relevant and repeatable driving 
maneuver tests discussed by General Motors in 1973 remain unsolved. 
Note that GM suggested the static laboratory measurement as a 
substitute for maneuver tests when only on-road untripped rollover was 
under consideration. This is an even stronger endorsement of static 
measurements than that represented by NHTSA's reasons for using SSF for 
consumer information on all single-vehicle rollovers, tripped and 
untripped.\5\
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    \5\ Untripped rollover is a rollover induced by tire friction 
with the driving surface alone, resulting from a driving maneuver 
and usually occurring on the roadway. Tripped rollovers usually 
occur when a vehicle runs off the roadway and the tires and wheels 
contact a tripping mechanism (curb, soft soil, pavement drop off) 
which causes the vehicle to roll. A much smaller number of tripped 
rollovers occur on the road as a result of the wheel rim digging 
into the pavement during an extreme maneuver. Whether or not a 
vehicle rolls when it encounters a tripping mechanism is highly 
dependent on the geometric properties represented by SSF. In an 
untripped rollover, SSF is still very important, but other factors 
come into play (such as tire properties). Therefore, GM's suggestion 
to use SSF to characterize a vehicle's tendency for untripped 
rollover was a very strong endorsement of the relationship between 
SSF and vehicle rollover.
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    We view the rollover safety problem as 95 percent a problem of 
tripped rollover and five percent a problem of on-road untripped 
rollover.\6\ Maneuver tests do not represent tripped rollover. Once the 
vehicle is in a tripping situation (e.g., has left the road), tire 
traction is largely irrelevant to tripped rollover. Center of gravity 
height and track width (and to a much lesser extent roll moment of 
inertia) are the only vehicle properties with general applicability to 
tripped rollover situations. So, in 95 percent of rollovers, these 
vehicle properties would be the most relevant vehicle influences on the 
likelihood of rollover. In the five percent of the problem involving 
untripped rollover, a choice exists between using static measurements 
and performance in maneuver tests. To get data to make an informed 
choice between the two, NHTSA conducted a maneuver test program using 
12 vehicles in 1998. That testing confirmed General Motors' opinion of 
25 years earlier that the static measurements correspond well to 
dynamic maneuver tests.\7\ It also confirmed that the problems with 
maneuver testing identified by GM in 1973 are still largely unresolved 
today. Accordingly, we concluded in our June 2000 notice that there 
were no practical improvements in rating overall rollover resistance to 
be gained at this time by using something other than static 
measurements.
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    \6\ In 1998, the agency was performing research on driving 
maneuvers to see if we could develop a way to ameliorate the 
incidence of onroad, untripped rollover, which we estimated at the 
time to be less than 10 percent of rollover crashes. The American 
Automobile Manufacturers Association (one of the predecessors of the 
Alliance) contracted with Calspan Corporation to review all the 
cases in NHTSA's Crashworthiness Data System coded as untripped to 
try to demonstrate that we were misplacing our research funds on a 
very small problem. Consequently our National Automotive Sampling 
System team did its own audit of the 1992-96 rollover data and 
concluded that some tripped rollovers were miscoded as untripped 
rollovers (typically these were onroad rollovers in which the 
vehicle was sliding sideways and tripped on its own wheel rim). 
Using corrected 1992-96 data, our National Center for Statistics and 
Analysis estimated that 3.7 percent of rollovers are untripped and 
3.5 percent are both untripped and onroad, while 4.4 percent of 
single-vehicle rollovers are untripped. (Research Note, ``Passenger 
Vehicles in Untripped Rollovers,'' September 1999.)
    \7\ See the June 1, 2000 Request for Comments for a summary of 
that research.
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2. The Importance of the Effect of SSF on Rollover Rate
    When the agency first sought public comment on rollover issues in 
1973, the industry's position was that the frequency of untripped on-
road rollovers was too low to justify significant vehicle modifications 
and constraints on future vehicle design. The vehicle manufacturers 
questioned the benefit/cost relationship and practicability of a 
minimum standard on rollover resistance, but they did not deny the 
relationship between SSF and rollover crashes. The agency's June 2000 
plan for consumer information on rollover resistance expressed 
considerable agreement with the 1973 industry position on rollover and 
offered a statistical study of modern crash data in order to quantify 
the relationship between SSF and the incidence of rollovers occurring 
in single-vehicle crashes. The Alliance responded in August 2000 with 
the position that vehicle characteristics are now deemed largely 
irrelevant to the occurrence of rollover crashes and consumer 
information on vehicle rollover resistance is inherently misleading. 
The Alliance provided a statistical study purporting to demonstrate 
that the influence of SSF was limited to three to eight percent of the 
variability between vehicles in rollover crashes.
    While the laws of physics prove beyond question that vehicles with 
low SSF roll over at lower lateral accelerations than vehicles with 
high SSF, the effect of SSF must be shown to have a significant 
influence on the outcome of actual crashes (rollover vs. no rollover) 
to be worth using for consumer information. It is a fact that types of 
vehicles with SSFs lower than passenger cars, as a group, have greater 
numbers of rollover crashes than passenger cars, either as a percentage 
of all crashes (passenger cars, 1.6 percent; vans, 2.0 percent; pickup 
trucks, 3.7 percent; SUVs, 5.1 percent) or as a percentage of single-
vehicle crashes (passenger cars, 13 percent; vans, 14 percent, pickup 
trucks, 24 percent; SUVs, 32 percent). The Alliance attributes these 
differences primarily to differences in the driver and road conditions 
associated with the various vehicle types, rather than to the 
characteristics of the vehicles. For example, if young males using 
alcohol and driving on rural roads with high speed limits are over-
represented as drivers of four-wheel drive pickup trucks in crashes, 
could these road-use variables outweigh the vehicle property to the 
point of insignificance? According to the current industry view, the 
correlation between the SSF of a vehicle and its ability to attract 
risky drivers who operate vehicles under adverse road conditions is the 
fundamental reason vehicles with low SSF are involved in a higher 
proportion of rollover crashes.
    The agency agrees that driver behavior and road conditions are 
significant factors in understanding why single-vehicle crashes of any 
type occur, and that they have a strong influence on whether single-
vehicle crashes result in rollover. However, we think that the rollover 
resistance of the vehicle represented by SSF also exerts a strong 
influence on whether single-vehicle crashes result in rollover. The 
statistical study in our previous notice attempted to address the 
important question of whether road-use differences between vehicles 
relegate their difference in

[[Page 3391]]

rollover resistance to insignificance in actual crash experiences. We 
analyzed state accident reports in six states (1994-1997) on 184,726 
single-vehicle crashes with 36,575 rollovers involving 100 vehicle 
make/models. The road-use variables available in all six states 
identified male drivers, young drivers, alcohol involvement, darkness, 
wet or icy surface, speed limit 55 mph or greater, storm, hill, and 
curve. We used multiple linear regression because its ``R-squared 
statistic'' provided an intuitive method of comparing the explanatory 
power of individual variables and because we could control the effect 
of the large differences in the number of crash samples for the various 
vehicles. Each vehicle was represented by its SSF and the average of 
each road-use variable over the number of crashes in each state. 
Systematic differences between states in rollover rate due to factors 
such as accident reporting thresholds were accommodated by the 
inclusion of a dummy variable for each state. The ``R-squared 
statistic'' for the complete model was 0.88, indicating that the model 
explained 88 percent of the observed differences in rollover rate per 
single-vehicle crash between the vehicle make/models.
    The linear regression that used only the SSF and the state dummy 
variables as predictor variables had an ``R-squared'' of 0.73, which 
means that almost three-quarters of the variability in rollover risk 
between vehicle models is explained by the SSF plus the adjustments for 
state-to-state differences in crash reporting. This is greater than the 
``R-squared'' for the best model that used only the road-use variables 
plus the state dummy variables (0.58). Thus, the SSF appears to have 
greater explanatory value than the combination of the road-use 
variables. We conclude that the SSF is not relegated to insignificance 
by the road-use variables in describing rollover risk.
    The Alliance comment criticized the agency's use of linear 
regression because it operates on averages of road-use variables and 
cannot consider the possible interaction among variables. For example, 
the linear regression model would consider that the crashes of a 
particular make/model may involve 30 percent young drivers, 20 percent 
with alcohol involvement and 15 percent on curves, but it cannot 
distinguish crashes in which all of the factors were present 
simultaneously. The Alliance used logistic regression rather than 
linear regression in its analysis. Logistic regression operates on 
every individual crash circumstance sampled, rather than on averages of 
the road-use variables for crashes of each make/model, and thus can 
consider interactions among variables. It is a popular statistical tool 
in the health sciences. The Alliance also introduced the concept of 
scenario risk in its logistic regression model. In this technique, each 
combination of road-use variables (with some states providing as many 
as 14 variables) is a scenario. Scenario risk becomes a continuous 
variable.
    Appendix I of this notice presents a new statistical study which 
adds another year of state crash data to the database of our previous 
notice and contrasts analyses of the crash data using logistic 
regression of individual variables and risk scenarios to the linear 
regression method used in the previous notice. We found that it made 
very little difference to the logistic regression models whether the 
road-use variables were used as individual variables or combined to 
form risk scenarios, but that the curve estimating rollovers per 
single-vehicle crash produced by the logistic regression was slightly 
different from that previously reported for linear regression.
    The estimated risk of rollovers per single-vehicle crash is six 
times as high for a vehicle with an SSF of 1.00 as for a vehicle with 
an SSF of 1.53 (the range of the observed data) based on the linear 
regression model. The average slope of the rollover risk versus SSF 
curve for the linear regression model (Figure 1) in the range of 
observed data was -0.713. The slope of the corresponding curve of the 
logistic models is -0.598 or -0.580, depending on whether we use the 
individual variables or the scenario-risk variable. Both the linear and 
logistic approaches produced models that fit the data well, and both 
estimated a coefficient for the SSF term that was very important (in 
terms of statistical significance and the magnitude of the effect).
    The linear regression is judged by the ``R-squared'', a measure of 
fit that is familiar to many people. The logistic regression is less 
well known, but it also has a standard measure of fit, the association 
of predicted probabilities and observed responses. The percentage of 
concordant pairs for our logistic models was very high (for example, it 
was 71.4 percent for the six-state combined model).
    We can also measure the ``Chi-square'' value for the coefficient of 
the SSF term in each model to describe the significance of that term. 
Logistic regression models were calculated for the original six states, 
plus Ohio and New Mexico, which report rollover only if it is the first 
harmful event. In seven of the eight states, the ``Chi-square'' 
statistic for SSF is greater than for any of the other variables in the 
logistic model using individual variables. In the logistic model using 
scenario risk to combine all the variables except SSF, the ``Chi-
square'' statistic for SSF is greater than that of the scenario risk 
variable in three of the eight states. This result also contradicts the 
Alliance's assertion that SSF is relegated to insignificance by the 
importance of road-use variables on the rollover experience of vehicles 
in use.
    The Alliance's assertion that the effect of SSF on rollover is 
negligible was not a consequence of the possible superiority of 
logistic regression over linear, nor of the use of scenario risk rather 
than individual variables. Instead, the Alliance assertion depends upon 
a subtle change in the definition of the variables which serve as 
alternatives to SSF in explaining rollovers.
    NHTSA used the number of police-reported single-vehicle crashes as 
a measure of each make/model's exposure to rollover risk. We did not 
include collisions with pedestrians or animals in the roadway in our 
definition of single-vehicle crashes because, while those crashes 
generate a police report, the collision itself poses no risk of 
rollover of the vehicle. Our sample size was large enough that we did 
not need to further investigate pedestrian and animal crashes for 
relevance. We did include collisions with parked vehicles because they 
represented a type of roadway departure and a collision with a fixed 
object, although these collisions offer the least exposure to typical 
tripping mechanisms.
    Our analysis examined the effects of road-use variables because 
their correlations with SSF were the basis of an alternative theory of 
rollover causation. It is plausible that the greater rate of rollover 
of vehicles with low SSF is not caused by low SSF but rather by 
characteristics of drivers and roads which happen to be correlated with 
low SSF vehicles. The example of young males being the predominant 
driver population of particularly low SSF pickup trucks shows that this 
alternative has plausibility.
    However, the Alliance departed from the road-use variables as 
alternative causes of rollover. The Alliance analysis was not an 
explanation of alternative theories of rollover causation but rather an 
attempt to show that there is little, if any, effect of SSF on rollover 
causation. To do this, the Alliance created a category of ``non-
vehicle'' variables. This category allowed the addition of one variable 
whose effect overwhelmed the effects of all other

[[Page 3392]]

variables. That variable was ``first harmful event, collision with a 
traffic unit.'' It separated crashes which were collisions with 
pedestrians, animals or parked vehicles from other single-vehicle 
crashes. In essence, the extra variable separates crashes with minimum 
exposure to tripping mechanisms from all other single-vehicle crashes. 
This would seem to be a meaningless addition because there is no reason 
to expect a significant correlation between SSF and collisions with 
pedestrians, animals and parked vehicles. However, it sets up what the 
Alliance calls its ``low risk scenario'' which serves as a basis for 
comparison of rollover risk factors.
    The Alliance then compared the effect on rollover risk of increased 
SSF to the effect on rollover risk made by moving from the scenarios of 
actual crashes to the ``low risk scenario''. The effect on rollover 
risk of moving actual crash scenarios to the ``low risk scenario'' is 
essentially the effect on rollover risk of eliminating tripping 
mechanisms. The effect is huge. In simplified terms, the Alliance has 
argued that the effect on tripped rollover gained by an increase of SSF 
is minimal compared to the effect on tripped rollover of removing 
tripping mechanisms. The statistical study in Appendix I includes a 
discussion of how this type of analysis, in which characteristics of 
the crash itself are used to define the risk scenarios, is equally 
useful for ``demonstrating'' that seat belts have negligible safety 
benefit.
    We do not find the Alliance analysis persuasive. It may well be 
true that changing a single-vehicle run-off-the-road crash (where there 
is a high risk of rollover) into a crash in which the vehicle, for 
example, hits an animal in the road (where there is no risk of 
rollover) virtually eliminates the risk of rollover, and may do far 
more to minimize rollover risk than changing any single vehicle or 
driver factor. However, the point of this is unclear. One could also 
show that if vehicles could fly, there would be far fewer rollover 
crashes, based on the experience of actual aircraft. Since vehicles can 
not fly, and run-off-the-road crashes can not be changed into different 
types of crashes, positing these impossibilities as a means of 
analyzing, or addressing, the real world problem of more than 10,000 
Americans dying each year in rollover crashes does not seem either 
helpful or insightful.
    NHTSA seeks ways to address real world safety problems 
constructively. In the real world, driver and roadway factors are 
certainly important factors in all crashes, including rollovers. That 
is why NHTSA spends so much effort to increase belt use, reduce 
speeding, eliminate impaired driving, and so forth. However, the 
vehicle is also a significant factor in crash safety. If we take the 
driver and roadway conditions as givens (for example, a young male 
driver in a rural area), the physical attributes of different vehicles 
determine different outcomes when, for example, the vehicle drops two 
wheels off the road, and the driver responds incorrectly. Some vehicles 
will roll over much more often than others in these situations. Such 
vehicle differences have been shown to strongly correlate with rollover 
resistance expressed by SSF. We believe the American public should have 
this information available to consider when making purchase decisions.

B. NHTSA's Statistical Analysis Linking SSF to Rollover Rates

    The Alliance commented that the method NHTSA used to analyze the 
statistical relationship between state crash data and SSF used in the 
RFC failed to take into account possible interactions between the 
various non-vehicle variables, and therefore underestimated the role of 
the non-vehicle factors in rollover risk. The possible interaction 
between alcohol involvement and the crash occurring on a curve in a 
particular crash was given as an example. The commenter suggested using 
logistic regression to resolve the problem of variable interaction.
    As introduced in the previous section, Appendix I of this notice 
presents a new statistical study which adds another year of state crash 
data to the database relied on in our previous notice and contrasts 
analyses of the crash data using logistic regression of individual 
variables and risk scenarios to the linear regression method used in 
the previous notice. The model curves estimating rollovers per single-
vehicle crash using logistic regression were nearly identical 
regardless of whether the road-use variables were entered individually 
or as combinations in risk scenarios. However, logistic regression does 
produce a slightly different curve estimating rollovers per single-
vehicle crash from that previously reported for linear regression.
    Figure 1 shows the comparison between the updated linear regression 
analysis of the summarized data and the two logistic models (the six-
state models using either the individual variables or the scenario-risk 
variable). The linear regression curve of the previous notice was 
essentially unchanged by the addition of another year of state crash 
data (for a total of 226,117 single-vehicle crashes with 45,574 
rollovers). The logistic models are very similar to each other, and all 
the models indicate that the SSF is very important in understanding 
rollover risk. As noted previously, the average slope of the rollover 
risk vs. SSF curve estimated by the linear regression model in the 
range of observed data was -0.713, and the average slope of the 
corresponding curve of the logistic models is -0.598 or -0.580, 
depending on whether we use the individual variables or the scenario-
risk variable. Also, logistic regression estimates a greater risk of 
rollover than does linear regression for vehicles with SSFs higher than 
1.10.

[[Page 3393]]

[GRAPHIC] [TIFF OMITTED] TR12JA01.042


BILLING CODE 4910-59-C
    The logistic regression and linear regression separate the effects 
of vehicle rollover resistance and those of road-use variables by 
different processes, and the logistic regression predicts a curve with 
a lower average slope. The Alliance commented that logistic regression 
considers the potential effect of variables in combination which may 
intensify or dilute their individual effects, but that linear 
regression would neglect combination effects. With this possibility in 
mind, we considered whether the use of the curve corresponding to 
logistic regression on individual variables would serve as a better 
basis of rollover risk for the vehicle star ratings than the linear 
regression curve proposed in our June 1, 2000 notice.
    The proposed rating system was based on equal intervals of risk and 
positioned the five-star level at a value of SSF achievable by 
favorably designed family sedans. It also positioned the one-star range 
where it captured some popular SUVs and pickup trucks of the recent 
past. The manufacturers of the one-star vehicles generally have 
improved the current versions of the equivalent vehicles to the two-
star level, but we believe the one-star rating ceiling would be 
stringent enough to discourage companies from returning to old design 
practices or from importing less advanced vehicles. A fortuitous 
feature of the ratings based on the linear regression curve was that 
reasonable one-star and five-star SSF boundaries occurred at predicted 
levels of rollover risk of 10 percent and 40 percent, permitting three 
equal intervals of risk between them divisible by ten for the two-star, 
three-star and four-star boundaries. Having the star rating intervals 
bounded at 10, 20, 30 and 40 percent rollover risk levels would make 
the meaning of the ratings easier to explain to consumers. Figure 2 
presents the proposed rating system in graphical form. The updated 
linear regression curve in Figure 1 is nearly identical to the linear 
regression curve in Figure 2, except that it would set the one star 
boundary for 40 percent rollover risk at 1.03 instead of 1.04.

[[Page 3394]]

[GRAPHIC] [TIFF OMITTED] TR12JA01.043


BILLING CODE 4910-59-C
    We considered the merits of the various ways in which the rollover 
risk versus SSF curve produced by logistic regression (Figure 1, 
Individual Variables) could be used to replace that produced by linear 
regression (also in Figure 1) as the basis for defining rollover risk 
in the rating system. If the proposed rating intervals in terms of SSF 
(1.04, 1.12, 1.24, 1.45) were maintained, they would no longer satisfy 
their rationale of representing equal increments of rollover risk in a 
single-vehicle crash. Conversely, if the risk intervals at 10, 20, 30 
and 40 percent are maintained, the one-star SSF level would become 1.01 
and the five-star level would become 1.51. A one-star level of 1.01 is 
so low that we know of only one vehicle (not in current production) 
that it would describe. Similarly, a five-star level of 1.51 appears to 
be out of reach for even the most stable family sedans which have 
demonstrated very good performance in resisting rollover. We believe 
that maintaining the 10, 20, 30 and 40 percent star boundaries with the 
logistic regression curve would have the practical effect of replacing 
the five-star rating system with a three-star rating system. At the low 
end of the SSF scale, the distinction between some historically poor 
performing vehicles and their improved replacements would be lost. At 
the higher end of the SSF scale, the distinction between some very good 
performing mid-sized and large sedans and some clearly poorer 
performing sub-compacts would be lost.
    It would appear that the best way to incorporate the rollover risk 
levels estimated by logistic regression while maintaining the 
usefulness of the rating system to the consumer is to maintain the 
proposed one-star and five-star boundaries as closely as possible. This 
approach would require adjustment of the equal risk intervals between 
the one-and five-star boundaries to reflect the difference in average 
slope between the linear regression curve and the logistic regression 
curve. A five-star boundary of 1.46 corresponds to a rollover risk of 
less than 12 percent on the logistic regression curve. (The previous 
boundary of 1.45 would require a statement of risk of 12.1 percent 
which would not be desirable for consumer information). Similarly, a 
one-star boundary of 1.05 would correspond to a rollover risk greater 
than 36 percent. These one-star and five-star boundaries would allow 
for equal risk intervals of eight percentage points between the other 
star boundaries. A change from 10 percent risk intervals to eight 
percent risk intervals would be proportional to the difference in 
average slope between the linear regression curve and the logistic 
regression curve. Figure 3 illustrates this idea for using the logistic 
curve in a revised rating system in a graphical form comparable to 
Figure 2.

[[Page 3395]]

[GRAPHIC] [TIFF OMITTED] TR12JA01.044

    However, this idea also has serious drawbacks. It would move the 
three star level from 1.13 SSF to 1.17 and the four star level from 
1.25 to 1.29 because the logistic regression shows less of the 
asymptotic shape of the raw data (Figure 1 of Appendix 1) than does the 
linear regression (of the log of SSF) curve previously proposed. This 
is troubling for two reasons. The shape of the original linear 
regression curve conforms better than does the logistic regression 
curve to the expectation that a given increase in SSF produces a 
substantially greater benefit for a vehicle with a low SSF than for one 
with a high SSF. Also, NHTSA believes that the proposed star rating 
levels may have become design goals for manufacturers seeking to 
improve rollover resistance. A change in star rating levels at this 
time may have the counterproductive effect of denying manufacturers 
recognition for substantial improvements in rollover resistance of 
vehicle designs.
    While we do not deny the theoretical advantages of logistic 
regression cited by the Alliance regarding interactions between road 
use variables, the similarity in curves describing rollover risk as a 
function of SSF in the linear and logistic regression approaches 
suggests that such interactions do not exert a great influence on the 
effect of SSF. Therefore, we do not believe that the difference in risk 
analysis methods is great enough to compel a change in the proposed 
star rating levels to the detriment of manufacturers who are trying to 
achieve them and to the detriment of consumers who we believe will find 
the proposed rating system simpler. We also note that the linear 
regression curve presents a more conservative estimate of rollover risk 
for vehicles with SSF greater than 1.10, and we anticipate vehicles 
with SSF lower than 1.10 becoming rare in light of manufacturers' 
reported efforts at improving rollover resistance.
    The rating system that NHTSA will use to define rollover risk and 
assign star rating is based on the updated linear regression curve in 
Figure 1 of this section. It would be described verbally as follows:
    One Star : Risk of Rollover 40 percent or greater in a 
single-vehicle crash is associated with SSF 1.03 or less.
    Two Stars : Risk of Rollover 30 percent or greater but 
less than 40 percent is associated with SSF 1.04 to 1.12.
    Three Stars : Risk of Rollover 20 percent or 
greater but less than 30 percent is associated with SSF 1.13 to 1.24.
    Four Stars : Risk of Rollover 10 percent or 
greater but less than 20 percent is associated with SSF 1.25 to 1.44.
    Five Stars : Risk of Rollover less 
than 10 percent is associated with SSF 1.45 or more.

C. Comments on Practical Problems with SSF Ratings

1. Difficulty of Improving Vehicles
    The Alliance and the import manufacturers' organization, AIAM, 
asserted that improvements in a vehicle's SSF are not practicable since 
SSF is largely determined by its vehicle type. That is, the track 
widths and c.g. heights of pickups, SUVs, vans, and passenger cars are 
more or less fixed within certain limits. Significant changes to those 
measurements would simply eliminate the vehicle attributes which are 
common to the category and which are presumably desirable to consumers. 
These comments noted, for example, that significantly lowering the c.g. 
(thus raising the SSF) of an SUV could be accomplished by decreasing 
ground clearance, but doing so might make it unappealing compared to 
other vehicles in the SUV category. Conversely, the comments contended 
that marginal changes to track width and c.g. height small enough to 
maintain attributes in a vehicle category would not improve rollover 
risk. They conclude that SSF is not a useful design

[[Page 3396]]

criterion, and it lacks the potential to reduce rollover rates if 
current vehicle types are to be preserved.
    We disagree that significant improvement in SSF will necessarily 
eliminate desirable attributes within a class of vehicles. We are aware 
of a recent redesign of a production SUV \8\ in the U.S. that achieved 
a decrease in c.g. height of approximately 2.0 inches (along with a 
significant increase in track width) while actually increasing the 
ground clearance. We estimate those changes represent an improvement in 
SSF equivalent to at least one star rating interval, and we would 
expect a significant decrease in rollover risk in single-vehicle 
crashes.
---------------------------------------------------------------------------

    \8\ Mitsubishi Montero redesign from model year (MY) 1991-99 
design to MY 2000 version of the same nameplate.
---------------------------------------------------------------------------

    We also would note that passenger car-based SUV's with 
significantly better SSFs than traditional, truck-based SUVs have been 
gaining popularity in the absence of any consumer information program 
for rollover. The range of SSF among ten SUVs in our 1998 SSF 
measurements of a group of 32 then-new vehicles was equivalent to a 
rollover risk reduction of approximately 14 percent using the 
predictive curve from the linear regression analysis explained in this 
notice. So-called ``crossover'' vehicles promise even greater 
improvement. While these vehicles may offer less of some attributes of 
traditional SUVs, like overall ride height, the increasing popularity 
of crossover vehicles indicates that those attributes may be less 
important to consumers than the ones which they maintain in common with 
traditional SUVs, such as cargo room and traction on snowy roads. Thus, 
the suggestion that no changes to current vehicle designs are possible 
without significant customer resistance appears to be an assertion 
unsupported by what has happened recently in the market.
    On the other hand, one of the models that scored highest among the 
ten SUVs in the 1998 measurements was a more or less traditional 
design, i.e., it was not passenger car-based.\9\ This gives evidence 
that more stable light truck design is not incompatible with 
traditional design attributes.
---------------------------------------------------------------------------

    \9\ Isuzu Rodeo.
---------------------------------------------------------------------------

    The fact that SUVs are seldom used off-road indicates that not all 
SUV buyers really want off-road capability. Buyers who are aware of the 
tradeoff in risk of rollover that such off-road capability usually 
entails, may decide they can obtain the attributes they want or need in 
a more rollover-resistant vehicle. As a contrasting example, buyers who 
desire passenger and cargo capacity may choose a van or minivan over a 
conventional station wagon after deciding that their priorities 
outweigh the increase in rollover risk associated with that choice.
    We believe that vehicle modifications to improve rollover 
resistance ratings are both achievable and beneficial. Press accounts 
suggest that manufacturers are, in fact, making such modifications as 
they redesign their light trucks. However, the ratings do not force 
manufacturers to modify vehicles, nor do they force consumers to accept 
only certain vehicle alternatives. The ratings will have a positive 
effect on the light vehicle rollover problem by making consumers more 
aware of trade offs in rollover stability, allowing consumers to make 
more informed purchase decisions, and influencing their awareness of 
the need to wear seat belts to prevent ejection in rollover crashes. 
This improvement will accrue even if the manufacturers make no changes 
to vehicles whatsoever in response to the program.
2. Possible Consequences of Improving SSF
    Honda and the Alliance also suggested that, with a design criterion 
like a rollover rating based on SSF, manufacturers may be inclined to 
``design for the test.'' The manufacturer of a vehicle whose score 
falls just below a rating cutoff point might be able to make design 
adjustments that shift the vehicle's score into the next higher 
category. We believe there is no reason to discourage manufacturers 
from taking such actions because an improvement in SSF will result in a 
corresponding improvement in rollover risk. In fact, we believe that a 
major advantage of SSF, one that distinguishes it from other measures 
of rollover resistance, is that it ``does no harm.'' Since SSF is a 
fundamental measure of inherent vehicle stability, there is no 
realistic risk that increasing SSF will degrade actual rollover rate or 
have other unintended, negative consequences. In contrast, improvement 
in other metrics can result in trade-offs that compromise overall 
safety. For example, maximizing a vehicle's Tilt Table Ratio can be 
accomplished by trading off some vehicle directional control 
(oversteer/understeer) characteristics. As another example, it is 
apparent that the Stability Margin metric can be improved by reducing 
tire grip, which could decrease driver control of the vehicle.\10\ 
Furthermore, SSF is relevant to stability under virtually any 
circumstance, whether it be a run-off-the-road crash, an obstacle 
avoidance scenario, or even collisions with objects or other vehicles, 
though it is obviously more significant in some of those events, i.e., 
single-vehicle crashes, than in others, i.e., collisions, where impact 
forces can overwhelm other factors.
---------------------------------------------------------------------------

    \10\ These metrics are explained in detail in the June 1, 2000 
notice.
---------------------------------------------------------------------------

    It was suggested in the comments that vehicle characteristics which 
an SSF-based rating ignores, like body shape and tire profile, 
influence rollover rate because they determine how a vehicle interacts 
with roadside objects and terrain during a crash event. As an example, 
Honda suggested that lowering a vehicle's c.g., thus improving its SSF, 
by equipping it with low-profile tires could increase the risk of 
tripped rollover by making sideward wheel contact with tripping 
mechanisms more likely. This is speculative and not persuasive. Each 
single-vehicle crash is, more or less, a unique event, because of the 
variety and complexity of circumstances involved. Although we agree 
that tripping usually initiates through interaction of a vehicle's 
wheels (i.e., tires and/or rims) with the roadway environment, 
generalizations about the influence of low-profile tires, or 
differences in body shape, on tripping frequency are extremely 
difficult to substantiate, given the limitless combinations of terrain, 
pavement condition, shoulder design, barriers, soil, vegetation, etc. A 
vehicle feature like taller, more flexible tire sidewalls may help 
avoid tripping in a few crashes, but is likely to be ineffective in the 
vast majority of others, and may be counterproductive in some cases. 
Even if it were possible for a manufacturer to identify tires and rims 
that were supposedly more resistant to tripping, safe handling and road 
holding considerations should certainly weigh more heavily in tire and 
rim selection.
    A notable exception to this involves the problem of tire debeading. 
Clearly, a wheel rim that becomes exposed when a tire debeads either as 
a precursor to a single-vehicle crash or in the course of one, can 
become a primary tripping mechanism. We believe that tire and rim 
combinations that are more resistant to debeading may indeed lessen the 
risk of rollover in a single-vehicle crash. The agency is already 
planning to improve debeading requirements in FMVSS No. 109.
    A further difficulty in identifying vehicle features that might 
improve tripping resistance is that crash data is limited. The minute 
level of detail required to thoroughly analyze the interaction of a 
vehicle's wheels,

[[Page 3397]]

undercarriage, body components, etc., with the roadway environment in a 
run-off-the-road event is generally unavailable in state or national 
crash databases. NHTSA's NASS-CDS database does contain a high level of 
detail, but it focuses on a relatively small sampling of crashes. In 
contrast, the SSF of vehicles in crashes can be determined as long as 
the data contain a few details about the vehicle, like make and model. 
Availability of extensive crash data is important for analyses like 
NHTSA's statistical analysis of crashes in six U.S. states as reported 
in the RFC and in Appendix I here.
    Honda also suggested that problematic suspension behavior such as 
``suspension-jacking'' can lead to a higher risk of rollover regardless 
of SSF, and that this exemplifies why SSF alone is not an adequate 
indicator of rollover resistance. Although vehicles with particular 
suspensions, most notably ``swing-axle'' designs, historically may have 
been associated with rollover, we believe those represent relatively 
few cases out of a very large population of rollover crashes and that 
such examples of suspension design are uncommon in current vehicles. 
Furthermore, suspension behavior is less important than SSF once a 
vehicle has left the roadway, where factors like shoulder condition and 
terrain interact with the basic stability characteristics of the 
vehicle to determine crash outcome.
3. SSF Measurement Accuracy
    Honda stated in response to the RFC that the Vehicle Inertia 
Measurement Facility(VIMF) that NHTSA will use to ascertain SSF is not 
accurate enough to repeatably give useful vehicle ratings. Honda 
suggested that for c.g. height measurement the measurement error is the 
sum of 0.5 percent ``repeatability'' error and 0.5 percent ``accuracy'' 
error, giving a total measurement error of 1.0 percent of 
the measured value. Honda believes an error of that magnitude is 
significant, compared to the small differences between vehicles being 
compared, and that a vehicle could be assigned an incorrect number of 
stars due to measurement error.
    Honda appears to have misinterpreted the published reports 
available on the VIMF. The document cited in Footnote 19 of the RFC 
does indicate, in Table 1, ``error bounds'' for c.g. height of 
0.5 percent of the measured value.\11\ Other 
documents,\12,\ \13,\ \14\ describing the design of the VIMF give the 
same value for ``repeatability'' or ``two standard deviation error'' 
for c.g. height measurements.
---------------------------------------------------------------------------

    \11\ Heydinger, G.J., et al; ``Measured Vehicle Inertial 
Parameters--NHTSA's Data through November 1998''; Society of 
Automotive Engineers 1999-01-1336; March, 1999.
    \12\ Heydinger, G.J., et al; ``The Design of Vehicle Inertia 
Measurement Facility''; SAE Paper 950309; February 1995.
    \13\ Bixel, R.A., et al; ``Developments in Vehicle Center of 
Gravity and Inertial Parameter Estimation and Measurement''; SAE 
Paper 950356; February 1995.
    \14\ Heydinger, G.J., et al; ``An Overview of a Vehicle Inertia 
Measurement Facility''; Intl. Symposium on Automotive Technology; 
Paper 94SF034; October 1994.
---------------------------------------------------------------------------

    Basically, ``repeatability,'' as used in the referenced documents 
in regard to the VIMF, is not separate from the ``accuracy'' of the 
system. It is incorrect to assume that the total VIMF system error in 
c.g. height measurements is the sum of the 0.5 percent repeatability 
and 0.5 percent accuracy, for a total system error of one percent in 
c.g. height measurements. The total system error of the VIMF for c.g. 
height measurement is 0.5 percent or less, as explained below.
    When the VIMF was under development, an error analysis was 
conducted based on experience with NHTSA's Inertial Parameter 
Measurement Device (IPMD), a precursor to the VIMF. Over the course of 
several years, the IPMD underwent successive updates and improvements, 
culminating in a fifth and final version of the machine that ultimately 
served as a model for the VIMF. The error analysis accounted for all 
the known sources of error arising from each system component, for 
example, platform deflection and vehicle restraint rigidity, as 
experience with the IPMD had indicated. By mathematical modeling, the 
contribution of each component to the whole system error was 
determined. The final design specifications for the VIMF were set by 
that analysis. Each component was selected or fabricated so as to limit 
the combined error from all the known contributions to 0.5 percent of 
the measured value for c.g. height. The details of the error analysis 
are discussed in the referenced documents.
    Since it was designed and constructed, the accuracy of the VIMF has 
been evaluated using a custom-built calibration fixture with a known 
c.g. location. This fixture is a heavy weldment made from stock steel 
plates and box section beams whose individual c.g. locations are easily 
determined by geometry. Because it is a very rigid body and is 
fabricated from such geometrically simple components, the calibration 
fixture's c.g. location, as well as its mass moments of inertia, are 
known theoretically, and it is thus a benchmark for reckoning the 
accuracy of the VIMF. The calibration fixture can be set up in either a 
light or heavy configuration, the latter achieved by adding weight in 
precise locations to increase the c.g. height by a known amount. In the 
light configuration, the fixture is representative of the mass and c.g. 
height of a mid-size passenger car. In the heavy configuration, it is 
representative of a light truck.
    In calibration tests using this fixture, the VIMF consistently 
measures the c.g. location to within 0.5 percent of the known value. 
Tables 6 and 7 of the 1995 Heydinger paper cited here indicate that the 
VIMF was able to measure the c.g. height of the fixture to within 0.46 
percent (2.6 mm in 561.2 mm) and 0.32 percent (2.6 mm in 809.2 mm) of 
its theoretically known values in the light and heavy configurations, 
respectively. Those results correspond well with the VIMF error 
analysis which predicts that the degree of accuracy should be somewhat 
higher when measuring heavier, higher c.g. vehicles. That is, the 
measurement accuracy for vehicles which are likely to fall into the 
lower SSF categories is significantly better than 0.5 percent.
    While we believe the NHTSA measurements will be sufficiently 
accurate, no degree of measurement accuracy can prevent borderline 
cases. There is always a possibility of a vehicle score falling so 
close to a cutoff point between star ranges that applying even a small 
amount of measurement uncertainty to the score results in ambiguity 
about the category to which the vehicle belongs. This situation is 
characteristic of any rating scheme and is no different from what 
currently exists in the NHTSA frontal and side NCAP. We plan to use 
conventional rounding methodology to determine the SSF of each test 
vehicle to two decimal places and assign stars based on that result.
    If a manufacturer determined that one of its models was on the 
border between star levels, the manufacturer could, if it wished, make 
changes to the vehicle to improve its SSF to the point where it falls 
comfortably in the higher category. If the vehicle was indeed on the 
border, the changes necessary would probably be very minor, and it 
would be voluntary, not mandatory.

D. Consumers' Ability to Understand SSF as a Measure of Rollover Risk 
in the Event of a Single-vehicle Crash

    Some commenters had misgivings about consumers' abilities to 
understand and use the new rollover rating information in three areas. 
They believe:
     Consumers are not capable of understanding that the star 
rating

[[Page 3398]]

describes the risk of rollover in the event that the vehicle is 
involved in a single-vehicle crash.
     Consumers will not find the information useful in making a 
vehicle choice.
     Even if consumers use the information, the new program 
will not lead to a decrease in rollover crashes.
Each of these areas are discussed and responded to below.
1. Are Consumers Capable of Understanding That the Star Rating 
Describes the Risk of Rollover in the Event That the Vehicle Is 
Involved in a Single-vehicle Crash?
    Auto manufacturers and the National Automobile Dealers' Association 
(NADA) believe that consumers are not capable of understanding that the 
star rating describes the risk of rollover in the event that the 
vehicle is involved in a single-vehicle crash. The following is a list 
of comments and the commenters who made them:
     Consumers will be confused because the rollover ratings 
are not in terms of injury risk like other NCAP ratings--Alliance
     Consumers will not understand that the rollover ratings do 
not include crashworthiness attributes--AIAM
     Consumers will think the rollover risk is the life-time 
rollover risk from driving the vehicle or the risk of rollover each 
time they drive the vehicle--Alliance, Suzuki, Toyota, Honda
     Consumers will think risk is the same for all drivers in 
all conditions and have the false impression that the vehicle design is 
the principal cause of rollover--Suzuki, NADA
    The language that will be used in consumer information products 
concerning this rollover rating (see Section IV) was developed using 
the outcome of focus group testing. As discussed in the June 2000 
notice, in April 1999 NHTSA conducted a series of six focus groups to 
examine ways of presenting comparative rollover information. As a 
result of the comments to our June 2000 notice, NHTSA conducted another 
series of focus groups in November 2000. Two versions of explanatory 
language were presented to a total of 12 groups of nine consumers each 
in two different cities. NHTSA asked the focus groups to evaluate a 
short version of rollover rating explanatory language that read as 
follows:

Description of Rollover Resistance Rating

    Most rollover crashes occur when a vehicle runs off the road and is 
tripped by a ditch, soft soil, a curb or other object causing it to 
roll over. These are called single-vehicle crashes because the crash 
did not involve a crash with another vehicle. The Rollover Rating is an 
estimate of your risk of rolling over if you have a single-vehicle 
crash. The Rollover Rating essentially measures how ``top-heavy'' a 
vehicle is. The more ``top-heavy'' the vehicle, the more likely it is 
to roll over. The lowest rated vehicles (1-star) are at least 4 times 
more likely to roll over than the highest rated vehicles (5-stars).
     Here are the Rollover Ratings:

In A Single-vehicle Crash, a vehicle with a rating of:

Five Stars  
Has a risk of rollover of less than 10%
Four Stars  
Has a risk of rollover greater than 10% but less than 20%
Three Stars  
Has a risk of rollover greater than 20% but less than 30%
Two Stars  
Has a risk of rollover greater than 30% but less than 40%
One Star  
Has a risk of rollover greater than 40%

We also asked the focus groups to evaluate the following longer 
version:

Description of Rollover Resistance Rating

     Thousands of crashes occur each year when a driver loses 
control of his/her vehicle and runs off the road. These are called 
single-vehicle crashes because the crash did not involve a collision 
with another vehicle. Once the vehicle leaves the road it can hit an 
object (pole, tree, guardrail, etc.), or the wheels can contact a 
ditch, soft soil, a curb or other object, tripping the vehicle and 
causing it to roll over. Single-vehicle rollovers can also occur on the 
road, but most rollover crashes occur when a vehicle runs off the road, 
usually sliding sideways.
     The National Highway Traffic Safety Administration (NHTSA) 
has provided consumers with frontal and side impact crash test ratings 
for several years. Because more than 10,000 people die each year in 
rollover crashes, NHTSA has added a Rollover Rating to provide 
consumers with better overall safety information on new vehicles.
     The Rollover Rating is an estimate of your risk of rolling 
over if you have a single-vehicle crash. If that happens, the risk of 
rollover for the highest rated vehicles (5-star) is less than 10%, but 
that risk factor increases by a factor of 3 to 4 for the lowest rated 
vehicles (1-star).
     The Rollover Rating essentially measures how ``top-heavy'' 
a vehicle is. The more ``top-heavy'' the vehicle, the more likely it is 
to roll over. Based on a study of 185,000 single-vehicle crashes, this 
measurement has been shown to relate very closely to the real-world 
rollover experience of vehicles.
     NHTSA's Front and Side Crash Test Ratings predict a 
vehicle occupant's chance of serious injury if the vehicle is involved 
in that type of crash. The Rollover Rating predicts the risk of a 
rollover if your vehicle is involved in a single-vehicle crash. (It 
does not, however, predict the likelihood of that crash.)
     While the Rollover Rating does not directly predict the 
risk of injury or death, keep in mind that rollovers have a higher 
fatality rate than other kinds of crashes. Even the highest rated 
vehicle can roll over, but you can reduce your chance of being killed 
in a rollover by about 75% just by wearing your seat belt.
     Here are the Rollover Ratings:

In A Single-vehicle Crash, a vehicle with a rating of:

Five Stars  
Has a risk of rollover of less than 10%
Four Stars  
Has a risk of rollover greater than 10% but less than 20%
Three Stars  
Has a risk of rollover greater than 20% but less than 30%
Two Stars  
Has a risk of rollover greater than 30% but less than 40%
One Star  
Has a risk of rollover greater than 40%

The focus group testing pointed out areas of difficulty in 
comprehension that were addressed in writing the final language.
    Focus group participants felt that while the shorter explanation 
was too short to fully comprehend the rating, the longer version was 
overwhelming and included unnecessary information. Based on the focus 
group inputs, we have developed the following language:

Description of Rollover Resistance Rating

     Most rollover crashes occur when a vehicle runs off the 
road and is tripped by a ditch, curb, soft soil, or other object 
causing it to roll over. These crashes are usually caused by driver 
behavior such as speeding or inattention. These are called single-
vehicle crashes because the crash did not involve a collision with 
another vehicle. More than 10,000 people die each year in all rollover 
crashes.
     The Rollover Resistance Rating is an estimate of your risk 
of rolling over if you have a single-vehicle crash. It

[[Page 3399]]

does not predict the likelihood of that crash. The Rollover Resistance 
Rating essentially measures vehicle characteristics of center of 
gravity and track width to determine how ``top-heavy'' a vehicle is. 
The more ``top-heavy'' the vehicle, the more likely it is to roll over. 
The lowest rated vehicles (1-star) are at least 4 times more likely to 
roll over than the highest rated vehicles (5-stars).
     The Rollover Resistance Ratings of vehicles were compared 
to 220,000 actual single-vehicle crashes, and the ratings were found to 
relate very closely to the real-world rollover experience of vehicles.
     While the Rollover Resistance Rating does not directly 
predict the risk of injury or death, keep in mind that rollovers have a 
higher fatality rate than other kinds of crashes. Remember: Even the 
highest rated vehicle can roll over, but you can reduce your chance of 
being killed in a rollover by about 75% just by wearing your seat belt.
     Here are the Rollover Resistance Ratings:

In A Single-Vehicle Crash, a vehicle with a rating of:

Five Stars  
    Has a risk of rollover of less than 10%
Four Stars  
    Has a risk of rollover between 10% and 20%
Three Stars  
    Has a risk of rollover between 20% and 30%
Two Stars  
    Has a risk of rollover between 30% and 40%
One Star  
    Has a risk of rollover greater than 40%

The length of the final version is midway between the two versions 
tested. It adds information not included in the tested short version 
that participants felt was particularly important in understanding the 
information and/or particularly compelling to cause them to pay 
attention to the information. It deletes information in the tested long 
version that participants felt was unnecessary and/or confusing. In 
addition, the explanation of the star ratings was simplified because 
the original format seemed to cause some confusion about whether more 
stars or less stars was a better rating. Finally, NHTSA has chosen to 
use the term ``Rollover Resistance Rating'' rather than ``Rollover 
Rating'' as this seemed to help participants understand the rating.
    The potential confusions cited by the commenters did not occur in 
the focus groups. From the discussions during the focus groups, it is 
clear that participants are aware that rollover is heavily influenced 
by driver and road characteristics. In almost all groups the first 
cause of rollover cited by participants was speed. Participants also 
mentioned road conditions and driver behavior and/or experience as 
factors. However, the participants also seemed to understand that the 
vehicle can also play a part in determining whether or not a rollover 
occurs, and that this rating was only a measure of that factor.
    NHTSA notes that the explanatory language will be used in the 
Buying a Safer Car brochure, and other places that present the star 
ratings. This brochure's primary focus is how a person can purchase a 
safer vehicle. It does not include extensive discussion of driver 
behaviors that can increase safety, as those types of issues tend to be 
addressed by other agency programs. NHTSA will include additional 
information about rollover in the form of Q&A's on the agency's 
website, and is considering developing additional rollover consumer 
information, both of which would be more appropriate places for 
discussion of other factors that can reduce the risk of rollover.
2. Will Consumers Find the Information Useful in Making a Vehicle 
Choice?
    The commenters listed below believe that even if consumers do 
understand the risk represented by the stars, this information will not 
be useful to them in choosing a vehicle. They assert the following:
     Consumers pick a vehicle class before they select a 
particular model. There are not enough differences in star ratings 
among vehicles in the same class to make the information useful to 
consumers. The stars reflect only tiny differences on each side of the 
dividing line.--Alliance, Ford, BMW, CU
     The difference in SSF made by options and configurations 
available on a single vehicle are too great to allow meaningful 
ratings--Alliance
    While it is true that many consumers limit their vehicle choices 
early in the purchase-decision process (e.g., must be an SUV), many 
others are also considering vehicles in more than one class (e.g., a 
van or an SUV). As the availability of rollover resistance rating 
information becomes more widely known, consumers will begin to know 
that certain types of vehicles have better ratings than others. In 
addition, while we cannot predict the final spread of ratings for the 
2001 models that will be tested, in our research there was usually a 
two- to three-star rating range for each class. Thus, by his or her 
vehicle choice alone, a consumer could reduce his or her chance of a 
rollover in a single-vehicle crash by up to 24% in some cases.
    In addition, another safety benefit of the NCAP program is the 
general improvements manufacturers have made to vehicles as the result 
of publishing such ratings. These improvements benefit all consumers 
regardless of their choice of vehicle. Over the years, manufacturers 
have responded to the frontal NCAP program and as a result the number 
of models achieving a five-star rating today is 2.7 times what it was 
when the program started in 1979. As for the criticism that star 
ratings do not indicate the tiny difference among vehicles near the 
dividing lines, this is also true for the frontal and side NCAP 
ratings. Just as with these ratings, the actual scores for the vehicles 
will be available on the NCAP website to anyone who is interested.
    Finally, with regard to comments that options can cause wide 
difference in the rating for a specific model, over the years that we 
have been researching vehicle inertial parameters, four-wheel drive is 
the only equipment option for which we have observed a large potential 
effect on SSF. NHTSA intends to test the most common versions of all 
vehicles. Where two- and four-wheel drive versions of the same vehicle 
are available, we will test them both and report them as separate 
models. We will accurately describe the actual test vehicle in the 
literature reporting the rating.
    Manufacturers who believe there are significant differences in SSF 
for different vehicle configurations may fund an optional NCAP 
measurement, just as they may fund optional frontal or side NCAP tests. 
Then if the difference in equipment or configuration makes a difference 
in the SSF, that difference will be available to the public.
3. Even If Consumers Use the Information, Will the New Program Lead to 
a Decrease in Rollover Crashes?
    Some commenters believe that even if consumers do use the new 
ratings, the outcome of that use will be other than what we desire. The 
following are comments and who made them.
     Rollover ratings will encourage consumers to purchase cars 
instead of trucks and cars are less safe than trucks.--Alliance
     A system based on RO/SVC may cause the choice of a less-
safe vehicle because it doesn't take the make/model's risk of becoming 
involved in a crash into account.--Suzuki, Tenneco
     Consumers will think that if they drive a vehicle with a 
high SSF they

[[Page 3400]]

will be immune to rollover and this will lead them to drive unsafely--
Alliance
     There is no demonstrated safety benefit of rollover 
rating.--Alliance, BMW
    The best indicator of the potential benefits of any new ratings 
program is the frontal NCAP program. As discussed previously, there are 
now many more five-star vehicles than when the frontal NCAP program 
started. Research also indicates that a five-star rating correlates to 
enhanced real-world safety. Therefore, all consumers benefit from these 
improvements in the vehicle fleet even if they don't make purchase 
decisions based on the star ratings. Both of these types of analysis 
will be possible for side impact and rollover NCAP after an adequate 
number of years of experience. There is no evidence that consumers have 
responded to vehicles with high frontal NCAP scores or other safety 
features by riskier driving behavior, and no reason to believe that 
they will respond differently to rollover ratings. Similarly, there is 
no indication that consumers believe they are immune to injury by 
driving a vehicle with a five-star frontal or side NCAP rating or with 
additional safety features.
    NHTSA disagrees that cars are less safe than light trucks. Occupant 
fatality rates (average 1991-98, FARS data) across all crash types 
indicates that large cars have a lower fatality rate than SUV's and 
small pickup trucks, and the same as the rate for standard pickups. 
Medium cars have a rate about the same as SUV's and lower than the rate 
for small pickup trucks. Small cars and small pickup trucks have about 
the same rate. See Figure 4. If we narrow the picture to rollover 
crashes, as in Figure 5, we see that SUV's and small pickups have the 
highest rates, at least 75 percent higher than the rate for small cars. 
The rates for medium and large cars are below any of the light truck 
types.
[GRAPHIC] [TIFF OMITTED] TR12JA01.045


[[Page 3401]]


[GRAPHIC] [TIFF OMITTED] TR12JA01.046


BILLING CODE 4910-59-C
    However, NHTSA is aware that as we expand the areas in which we 
provide consumer information ratings, it is becoming more and more 
important to provide consumers with guidance on how to weigh ratings in 
different categories. For example, it is quite common for SUVs to 
receive five-star ratings in side impact NCAP, but our research 
indicates that these vehicles will have rollover ratings in the one- to 
three-star range. NHTSA can help consumers understand these differences 
by providing them with information on the frequency of various crash 
types, as we have been doing with the front and side impact NCAP 
ratings, and we plan to do for rollover crashes. In addition, NHTSA has 
been considering possible ways to provide consumers with a single 
summary rating of a vehicle's safety.

E. The Question of Electronic Stability Control

    Continental Teves objected to the use of SSF to rate rollover 
resistance because the ratings would not reward manufacturers for 
equipping vehicles with Electronic Stability Control (ESC). It was also 
dissatisfied with language in the notice promising consumer information 
about ESC as part of the rating presentation after there is some 
evidence of its effectiveness. BMW, Toyota, Isuzu, Tenneco and the 
Alliance offered similar comments. All expressed confidence that the 
technology would reduce the number of on-road loss-of-control 
situations that often result in off-road tripped rollovers. The 
Alliance suggested that ESC may also reduce the risk of untripped 
rollover, and Continental believes that it may help drivers regain 
control after they leave the roadway. Many commented that ratings based 
on SSF would stifle and undercut advanced vehicle technology. The 
notice specifically asked commenters to share any data they may

[[Page 3402]]

have on the effectiveness of stability control technologies in 
preventing single-vehicle crashes, but none did so.
    The NCAP program rates the risk of rollover in the event of a 
single-vehicle crash. Most of these single-vehicle crashes involve 
hitting a curb or running off the road accidently and encountering soft 
soil, a ditch or something that trips the vehicle. To repeat, 95 
percent of rollovers are tripped. Once a vehicle is in this situation 
and strikes a tripping mechanism, its chances of rolling over depend 
heavily on its SSF.
    The promise of ESC is not that it can change what happens when a 
vehicle hits a tripping mechanism but that it may help the driver to 
avoid going off the roadway in the first place. ESC can apply one or 
more brakes automatically to keep the yaw rate of the vehicle 
proportional to its speed and lateral acceleration. Essentially, it 
corrects for vehicle understeer or oversteer, and some systems may 
override a driver's failure to brake when in fear of losing control. 
This benefit could minimize the driver's chances of compounding his or 
her driving errors in a panic situation. However, it cannot keep a 
vehicle from leaving the roadway if the vehicle is going too fast for 
the maneuver the driver is attempting.
    Like frontal and side NCAP ratings, the Rollover Resistance Rating 
is concerned with vehicle attributes that affect the outcome of a 
crash. None of the present ratings attempt to describe the probability 
of a vehicle's involvement in a crash. For example, the frontal 
crashworthiness star rating does not reward manufacturers who equip 
vehicles with advanced braking systems. Also, the agency cannot rely on 
skid pad demonstrations to determine the effectiveness of a safety 
device in the hands of the public. Anti-lock brakes were once 
considered likely to reduce rollover crashes because they had the 
potential to reduce the number of vehicles exiting the road sideways as 
a result of rear brake lock-up. This expectation has not been realized 
in passenger cars according to years of crash statistics. There has 
actually been an increase in the rollover rate of passenger cars 
equipped with anti-lock brakes that researchers have not yet been able 
to explain.
    The commenters suggest that NHTSA should abandon SSF as a basis for 
rollover rating because it does not reward ESC in the star rating and 
that without such a reward the use of the technology would be in doubt. 
The importance of SSF to rollover resistance is supported by abundant 
real-world evidence, while there is no data on the effectiveness of 
ESC. Based on the relative data available, it would not be appropriate 
to abandon SSF. We encourage manufacturers to assist us in determining 
the effectiveness of ESC by identifying optional ESC systems in VIN 
codes and sharing available data. We will continually monitor data on 
the real-world effectiveness of ESC and make appropriate changes based 
on that data. We do not expect that manufacturers will abandon ESC, 
since they express so much confidence in its ultimate effectiveness.
    NHTSA wants to encourage technological applications that enhance 
vehicle stability, provide drivers with more control of their vehicle, 
and help prevent rollover and other crashes. For ESC in particular, it 
is reasonable to assume that it will help some drivers use the 
available traction to stay on the road in circumstances that would 
otherwise result in panic-driven errors and roadway departure. We have 
asked the National Academy of Sciences to recommend ways of combining 
the effect of ESC on exposure to single-vehicle crashes, with the 
effect of SSF on rollover resistance in a single-vehicle crash, as part 
of its Congressionally-mandated study of rollover consumer information. 
We do not expect that a recommendation can be implemented without some 
determination of ESC's real-world effectiveness, but in the meantime we 
will identify in our Buying a Safer Car brochure the vehicles for which 
ESC is available and provide an explanation of these systems. The 
identification of vehicles with ESC will start in the December 2000 
issue of Buying a Safer Car. The April 2001 issue of Buying a Safer Car 
will also present Rollover Resistance Ratings.
    The first presentation of Rollover Resistance Ratings will be on 
the NHTSA website. The website will also present Questions and Answers 
regarding rollover crashes including one discussing the effect of ESC 
and its relationship to the Rollover Resistance Ratings. Until the 
Rollover Resistance Ratings are integrated into Buying a Safer Car, the 
NHTSA website will provide a chart of rated vehicles which will include 
a column indicating the availability of ESC. The heading of that column 
will provide a link to the Q&A about ESC.
    The Q&A section will include the following discussion:

    Question: How does Electronic Stability Control affect rollover, 
and what is its relationship to the Rollover Resistance Ratings?
    Answer: Most rollovers occur when a vehicle runs off the road 
and strikes a curb, soft shoulder, guard rail or other object that 
``trips'' it. The Rollover Resistance Ratings estimate the risk of 
rollover in event of a single vehicle crash, usually when the 
vehicle runs off the road. Electronic Stability Control (which is 
offered under various trade names) is designed to assist drivers in 
maintaining control of their vehicles during extreme steering 
maneuvers. It senses when a vehicle is starting to spin out 
(oversteer) or plow out (understeer), and it turns the vehicle to 
the appropriate heading by automatically applying the brake at one 
or more wheels. Some systems also automatically slow the vehicle 
with further brake and throttle intervention. What makes Electronic 
Stability Control promising is the possibility that with its aid 
many drivers will avoid running off the road and having a single 
vehicle crash in first place. However, ESC cannot keep a vehicle on 
the road if its speed is simply too great for the available traction 
and the maneuver the driver is attempting or if road departure is a 
result of driver inattention. In these cases, a single vehicle crash 
will happen, and the Rollover Resistance Rating will apply as it 
does to all vehicles in the event of a single vehicle crash.

    A similar discussion will accompany the rollover resistance ratings 
in the April issue of Buying a Safer Car.

F. Alternative Programs for Rollover Consumer Information Suggested by 
Commenters

    Three commenters to the RFC presented ideas for consumer 
information programs to be used in place of the agency's proposal to 
use SSF to rate vehicles. The Alliance had four suggestions:
     Cause drivers to obey the speed limits, be alert and 
unimpaired, and use proper restraints, and provide driver training in 
off-road recovery and crash avoidance maneuvering.
     Improve the roadways with paved shoulders to eliminate 
road edge drop-offs and provide road edge rumble strips to help alert 
drivers.
     Promote Electronic Stability Control.
     Promote crashworthiness improvements including active 
restraint systems, tubular and side curtain air bags, new belt reminder 
systems, structural crashworthiness improvements, FMVSS 201 interior 
protection, new locks and latches and alternative glazings.
    Ford and Suzuki commented that SSF should be used only to rate 
vehicle classes and should not be used to show distinctions between 
make/models in the same class. These commenters also believe that the 
program should not present the risk of rollover quantitatively.
    The NADA recommended that NHTSA put more emphasis on the seat belt 
message in the context of rollover,

[[Page 3403]]

including child safety restraints and suggested that manufacturers 
include in their vehicles' owners manuals material about crash 
avoidance driving practices. The manufacturers' association, the 
Alliance, on the other hand, wanted to see seat belt information only 
in a general sense, not specifically referring to rollover.
    The major flaw with all of these suggestions is that they do not 
deliver what the consumer wants--definitive, comparative, information 
about the relative risk of rollover in specific vehicles. We have 
shown, in the previous sections of this notice and the notices that 
have preceded it, that we can link rollover risk to the SSF of specific 
make/models. Any rollover-specific consumer information product that 
NHTSA develops in the future will mention driving habits that 
contribute to rollover prevention and emphasize the importance of seat 
belt use. However, the focus of the present action is on allowing 
consumers to make an informed choice about the safety of the vehicles 
they purchase, both by class and by model.

G. Commenters Preference for a Minimum Standard Based on a Dynamic Test

    Tab Turner, a plantiff's attorney, and Insurance Institute for 
Highway Safety, Consumers Union, and Advocates for Highway and Auto 
Safety, stated in their comments that, while they had no objection to 
using SSF to provide consumer information, an information program was 
not sufficient to address the rollover problem. They believe a federal 
motor vehicle safety standard, based on a dynamic track test of 
vehicles, is needed.
    Notwithstanding the recent Transportation Recall Enhancement, 
Accountability, and Documentation Act (TREAD) \15\ which requires the 
agency to issue ratings based on a dynamic test within two years, we 
believe that consumer information based on SSF is an appropriate way to 
proceed at this time to address rollover. Two issues are involved here: 
the issue of a minimum standard versus consumer information, and the 
issue of dynamic testing versus a static metric. Both of these issues 
were addressed at length in the RFC.
---------------------------------------------------------------------------

    \15\ P.L. 106-414, November 1, 2000.
---------------------------------------------------------------------------

    We agree that it would be desirable to have a standard to address a 
safety issue as significant as rollover resistance. However, as 
explained in the RFC, NHTSA previously decided not to set a vehicle 
rollover standard at a level that would effectively force nearly all 
light trucks to be redesigned to be more like passenger cars.\16\ NHTSA 
also previously decided not to set a vehicle rollover standard at a 
level that would effectively force a redesign even of certain vehicle 
types like small pickups and small SUVs \17\ because it would not be 
appropriate to prohibit the manufacture and sale of those vehicles 
without some predictable benefit commensurate with the cost of that 
action. However, we can still provide accurate and meaningful 
information about rollover resistance to allow the public to make fully 
informed choices when selecting a new vehicle.
---------------------------------------------------------------------------

    \16\ Denial of the Wirth petition, 52 FR 49033 (December 29, 
1987).
    \17\ Termination to establish a minimum vehicle standard for 
rollover resistance based on TTR or CSV, 59 FR 33254 (June 28, 
1994.)
---------------------------------------------------------------------------

IV. Rollover Information Dissemination using SSF in NCAP

    The agency has decided to go forward with a pilot consumer 
information program on vehicle rollover resistance, using the SSF as a 
basis for the rating system. This program will be part of NCAP, which 
currently gives consumers information on frontal and side-impact 
crashworthiness. Today we are announcing the 2001 model year vehicles 
to be tested and how the information will be disseminated to the 
public.
    There are two activities ongoing in NHTSA that may change this 
pilot program: the study by the National Academy of Science mandated by 
Congress in the Department's Fiscal Year 2001 appropriations bill \18\ 
and the Congressional requirement contained in the TREAD Act that the 
agency develop a dynamic test for consumer information on rollover, 
conduct the tests, and determine how best to disseminate the test 
results to the public by November 1, 2002. Changes or additions to this 
program will be developed if necessary to conform to the requirements 
of these two statutes.
---------------------------------------------------------------------------

    \18\ P.L. 106-346, October 23, 2000.
---------------------------------------------------------------------------

    The rollover information program will operate just as the current 
frontal and side NCAP does. New models are selected for testing before 
the beginning of the model year. Selection is based primarily on 
production levels predicted by the manufacturers and submitted to the 
agency confidentially. Consideration is given also to vehicles 
scheduled for major changes, or new models with specific features that 
may affect their SSF's. The vehicles chosen for NCAP testing will be 
obtained and measured by NHTSA, as the vehicles become available. 
Vehicles are obtained with popular equipment, typical of a rental 
fleet, and the equipment with possible influence on SSF will be 
included in the vehicle description when the rating is reported. Two-
wheel drive and four-wheel drive versions of a vehicle are treated as 
separate models, because a four-wheel drive option can have a 
significant effect on SSF. As provided for in the frontal and side 
NCAP, manufacturers can, at their option, pay for tests of vehicles, 
models, or configurations not included in NHTSA's test plan, if they 
wish to inform consumers about those vehicles through the program.\19\ 
The SSF will be converted to a star rating according to the curve 
presented in Section III and Appendix I at the intervals specified in 
Section III. The rollover rating information will be available on the 
agency's website, and will be included in all NHTSA publications and 
press releases which use NCAP data. The brochures and the website 
presentation will explain the basis of the ratings, present the SSF 
measurements, and discuss the magnitude of rollover harm prevention 
provided by seat belt use.
---------------------------------------------------------------------------

    \19\ The manufacturer pays for the vehicle and the test, 
however, actual vehicle leasing and testing is done by a testing 
laboratory under contract to NHTSA.
---------------------------------------------------------------------------

    As part of the presentation on rollover the following explanatory 
text will be used:

Description of Rollover Resistance Rating

     Most rollover crashes occur when a vehicle runs off the 
road and is tripped by a ditch, curb, soft soil, or other object 
causing it to roll over. These crashes are usually caused by driver 
behavior such as speeding or inattention. These are called single-
vehicle crashes because the crash did not involve a collision with 
another vehicle. More than 10,000 people die each year in all rollover 
crashes.
     The Rollover Resistance Rating is an estimate of your risk 
of rolling over if you have a single-vehicle crash. It does not predict 
the likelihood of that crash. The Rollover Resistance Rating 
essentially measures vehicle characteristics of center of gravity and 
track width to determine how ``top-heavy'' a vehicle is. The more 
``top-heavy'' the vehicle, the more likely it is to roll over. The 
lowest rated vehicles (1-star) are at least 3 times more likely to roll 
over than the highest rated vehicles (5-stars).
     The Rollover Resistance Ratings of vehicles were compared 
to 220,000 actual single-vehicle crashes, and the ratings were found to 
relate very closely to the real-world rollover experience of vehicles.

[[Page 3404]]

     While the Rollover Resistance Rating does not directly 
predict the risk of injury or death, keep in mind that rollovers have a 
higher fatality rate than other kinds of crashes.

Remember: Even the highest rated vehicle can roll over, but you can 
reduce your chance of being killed in a rollover by about 75% just 
by wearing your seat belt.

     Here are the Rollover Resistance Ratings:

In A Single-Vehicle Crash, a vehicle with a rating of:

Five Stars  
Has a risk of rollover of less than 10%
Four Stars  
Has a risk of rollover between 10% and 20%
Three Stars  
Has a risk of rollover between 20% and 30%
Two Stars  
Has a risk of rollover between 30% and 40%
One Star  
Has a risk of rollover greater than 40%

As part of these ratings, the agency also has decided to note vehicles 
that are equipped with ``electronic stability control'' technology, 
which may reduce the risk of a vehicle getting into an incipient 
rollover situation.
    Appendix II contains a preliminary list of vehicles we will measure 
and for which we will report SSF and star ratings. The vehicles will be 
tested as they become available to the test facility. As of today 24 
vehicles have been tested; the results are available from the Auto 
Safety Hotline (888-327-4236) or on the NHTSA website at 
www.nhtsa.dot.gov. The remainder of the test results and star ratings 
for the 2001 model year will be available by April 30, 2001.

V. Rulemaking Analyses and Notices

Executive Order 12866

    This notice was not reviewed under Executive Order 12866 
(Regulatory Planning and Review). NHTSA has analyzed the impact of this 
decision and determined that it is not a ``significant regulatory 
action'' within the meaning of Executive Order 12866. The agency 
anticipates that providing information on rollover risk under NHTSA's 
New Car Assessment Program would impose no regulatory costs on the 
industry.

    Authority: 49 U.S.C. 322, 30117, and 32302; delegation of 
authority is at 49 CFR 1.50 and 49 CFR 501.8.

    Issued on: January 8, 2001.
Stephen R. Kratzke,
Associate Administrator for Safety Performance Standards.

Appendix I: Statistical Analysis in Response to Comments

    Response to Comments of the Alliance of Automotive Manufacturers 
based on a Study by Exponent Failure Analysis Associates, Inc. 
titled: The Relative Importance of Factors Related to the Risk of 
Rollover Among Passenger Vehicles

Background

    The agency has proposed expanding the New Car Assessment Program 
(NCAP), which tests vehicle performance in front and side crashes, 
to include information on rollover resistance. We proposed a 
rollover metric for consumer information based on the Static 
Stability Factor (SSF) and described the approach in a Request for 
Comments, Notice for Rollover NCAP (``the Notice,'' docket NHTSA 
2000-6859, item 1, June 1, 2000). The Appendix to the Notice 
described a statistical analysis of four years of data (1994 to 
1997) from six states (Florida, Maryland, Missouri, North Carolina, 
Pennsylvania, and Utah), and we provided more details of the 
analysis (definitions, programming statements, and computer output) 
in another submission to the Rollover NCAP docket (item 4). The 
Alliance of Automobile Manufacturers (``the Alliance'') reviewed the 
Notice and the supplemental material and submitted their comments to 
that docket (item 25).
    Appendix 4 of their comments is a paper prepared for the 
Alliance by Exponent Failure Analysis Associates, Inc. (``the 
Exponent report'') on The Relative Importance of Factors Related to 
the Risk of Rollover Among Passenger Vehicles (Alan C. Donelson, 
Farshid Forouhar, and Rose M. Ray, in a paper dated August 30, 
2000). The Exponent report critiqued our linear regression analysis 
of the summarized crash data and suggested an alternative approach 
based on logistic regression analysis of individual crash events. 
This paper is a comparison of the two approaches (the linear model 
from summarized data and the logistic model of individual crash 
events) in response to those comments.

Overview

    The Exponent report listed four goals for their study (page 4 of 
that report), and we will address their conclusions in our response. 
The four goals were as follows:
    (1) ``To evaluate the statistical study offered by NHTSA as a 
basis for comparative 'ratings' [emphasis in original] of rollover 
risk,''
    (2) ``To gauge the strength of SSF as a predictor of rollover 
relative to the influence of non-vehicular factors,''
    (3) ``To quantify the relationship between SSF and risk of 
rollover after adjusting for the influence of non-vehicular 
factors,'' and
    (4) ``To estimate the magnitude and reliability of apparent 
changes in rollover risk with changes in SSF.''
    The Exponent report offered three corrections to our vehicle 
group definitions, questioned the use of linear models of summarized 
data, and recommended logistic models of individual crash events as 
an improvement (their goals 1 and 2). In response, we have made the 
suggested corrections, used updated VIN-decoded data, added a year 
of data (the 1998 calendar year data are now available for all six 
states used in our original analysis), and refit the model. Details 
on the data definitions are included below in ``Available Data,'' 
and the results of are described in ``Refitting the Linear Model.'' 
We have also used our data to fit logistic regression models, and 
these results are described in ``Fitting Logistic Models.'' A 
comparison of the two approaches is provided in ``Comparing the 
Models.''
    Our logistic models produced results that were similar to those 
produced by our linear model of summarized data and to the logistic 
models described in the Exponent report (which were based on a 
slightly different group of states, calendar years, and explanatory 
variables). That is, the choice of model form and data source do not 
affect our essential conclusion: the SSF is strongly related (both 
in terms of statistical significance and magnitude of effect) to 
rollover risk. However, there are some differences among the models 
in the estimated sensitivity of rollover risk to changes in the SSF.
    Where we disagree most with the Exponent report is in the 
interpretation of the results. The authors of the Exponent report 
argue that the SSF plays a smaller role in rollover causation than 
do driver and other road-use factors (their goals 2 and 4). Goal 2 
(gauging the relative strength of the SSF and non-vehicle factors) 
is so important to the authors that they used it as the title of 
their report. We believe that our analysis indicates that the SSF is 
very important in describing rollover risk, as measured by the fit 
of each model, the significance of the coefficient of the SSF term, 
and the magnitude of the coefficient of the SSF term. We do 
recognize that driver and other road-use variables are also 
important. Federal, state, and local education and enforcement 
programs are all aimed at the vulnerability of road users to human 
error, and we recognize that the driver plays a large role in 
causing or avoiding crashes. However, what we set out to address in 
the Notice is whether the SSF provides information that is useful to 
consumers--information they can use in selecting a vehicle, deciding 
whether to use seat belts and child seats, and adapting their 
driving style to a new vehicle. We describe this point in more 
detail below, in ``Interpreting the Analytical Results,'' using an 
example based on the relationship between crash severity, belt use, 
and injury severity.
    In summary, we believe that our statistical models (both the 
linear model of summarized data and the logistic models of 
individual crash events) and the statistical models offered in the 
Exponent report support our conclusion that the SSF is a useful 
measure of rollover risk that will help the consumer choose a new 
vehicle and use it wisely.

Available Data

    The analysis described in the Notice was based on single-vehicle 
crashes, which we

[[Page 3405]]

defined to exclude crashes with another motor vehicle in transport 
or with a nonmotorist (such as a pedestrian or pedalcyclist), 
animal, or train. We eliminated vehicles without a driver and all 
vehicles that were parked, pulling a trailer, designed for certain 
special or emergency uses (ambulance, fire, police, or military), or 
on an emergency run at the time of the crash. Our only criterion for 
including a vehicle model in the analysis was a reliable measure of 
the SSF. The 100 vehicle groups we identified were described in the 
Notice, and the definitions for these groups were included in 
another submission to the same docket (item 4).
    Exponent reviewed this information and pointed out three errors 
in the specifications of the vehicle groups (page 37). First, 
vehicle group 65 should have been defined as model years 1990-1995 
(not 1988-1996). Second, vehicle group 66 should have been defined 
as model years 1996-1998 (not 1997-1998). And third, vehicle group 
91 should have included model code ``SKI'' (not ``SCI''), as defined 
by the output from The Polk Company's PC VINA software 
(PC VINA for Windows User's Manual, October 20, 1998). 
We also found a typographical error in the specification of vehicle 
group 79: the number of drive wheels should have been specified as 
``not equal to 4'' (rather than ``equal to 4''). We corrected these 
mistakes in the list and computer programs, and the corrected list 
of vehicles is included here as Tables 1 through 4.
    Our understanding of some important differences in state crash 
reporting are included in Table 5. The Notice described our criteria 
for including a state in the analysis, which were as follows:
    (1) Data availability (the state must participate in the 
agency's State Data System (SDS) and have provided the 1997 data),
    (2) VIN reporting (the vehicle identification number (VIN) must 
be coded on the electronic file), and
    (3) Rollover identification (we must be able to determine 
whether a rollover occurred, regardless of whether it was a first or 
subsequent event in the crash).
Six states (Florida, Maryland, Missouri, North Carolina, 
Pennsylvania, and Utah) met all three criteria. Two states (New 
Mexico and Ohio) met two of the three criteria; these states 
participate in the SDS and the VIN is available on the electronic 
file, but rollovers are identified only if they are reported as the 
first harmful event in the crash. We have made some use of all eight 
states in this updated analysis, but most of the analysis is based 
on the six states with the best rollover reporting. These are the 
six states that were the basis for the analysis described in the 
Notice.
    For this analysis, we used the SDS data and the VIN-decoded data 
available on NHTSA's Research and Development Local Area Network ( 
LAN). The National Center for Statistics and Analysis (NCSA, an 
office in R&D) recently rebuilt the 1997 VIN files for Maryland and 
Missouri, and the numbers of relevant cases differ slightly from 
those reported in the Notice. The major changes were a slightly 
more-conservative approach to dealing with mistakes in VIN 
transcription and some additional vehicle-make codes. We also 
expanded somewhat our definition of ``rollover'' in North Carolina 
(adding information from the four impact-type variables), which 
increased the number of rollovers in that state over what was 
reported in the Notice. The number of relevant vehicles identified 
for each state and calendar year are shown as Table 6. Note that 
Ohio reported a relatively small percentage of VINs in 1998 (about 
29 percent of vehicles had a VIN on the electronic file), so case 
counts for the vehicles relevant to this study are low. Our analysis 
is not too sensitive to missing VIN information because it is based 
on internal comparisons of the crash data (specifically, on 
rollovers per single-vehicle crash); this would not be the case if 
we were basing our analysis on comparisons with an external source, 
such as rollovers per registered vehicle.
    We added a calendar year of data (1998) for the six states used 
in the analysis described in the Notice. However, Pennsylvania no 
longer includes on the electronic file some environmental variables 
that we need for this analysis (specifically, CURVE and GRADE), so 
we could not use the 1998 Pennsylvania data in the analysis. The 
variables available for this analysis are shown as Table 7. We 
calculated the SSF to two decimal places (with observed values 
between 1.00 and 1.53), we defined NUMOCC as the count of occupants 
in each vehicle, and we defined all the other road-use factors as 
dichotomous variables (with ``0'' coded for ``no,'' and ``1'' coded 
for ``yes'').
    All eight states reported the following data: ROLL, SSF, DARK, 
STORM, FAST, HILL, CURVE, BADSURF, MALE, YOUNG, OLD, and DRINK. 
Speed limit is not reported in New Mexico, so we defined FAST based 
on the roadway function class after reviewing the relationship 
between these two variables among New Mexico cases in the 1994-1998 
Fatality Analysis Reporting System (FARS) data. We assumed, based on 
our review of the FARS data, that (1) interstate and rural arterial 
roads had a speed limit of at least 55 mph, (2) local roads and 
urban arterial roads, collectors, and ramps had a speed limit of no 
more than 50 mph, and (3) the speed limit was unknown for all other 
roads. RURAL was unavailable for two states (Maryland and Missouri), 
BADROAD was unavailable for two states (Missouri and Pennsylvania), 
NOINSURE was unavailable for three states (Maryland, North Carolina, 
and Utah), and NUMOCC was unavailable for Missouri (where uninjured 
passengers need not be reported).

Refitting the Linear Model

    We refit the linear model using the approach described in the 
Notice. There were 241,036 single-vehicle crashes available for this 
analysis (that is, involving a vehicle in one of the 100 vehicle 
groups, occurring between 1994 and 1998, and occurring in the six 
states we studied in preparing the Notice (Florida, Maryland, 
Missouri, North Carolina, Pennsylvania, and Utah), and 48,996 of 
these (20.33 percent) involved rollover. We eliminated the 1998 
Pennsylvania data because CURVE and GRADE are not available on the 
electronic file, and this left 227,194 single-vehicle crashes, of 
which 45,880 (20.19 percent) involved rollover.
    We summarized the data for each vehicle group in each state, 
which produced 599 summary records (there were no reported single-
vehicle crashes involving vehicle group 54 in Utah). As with the 
earlier analysis, we eliminated any summary record that was based on 
fewer than 25 cases because we thought estimates based on smaller 
samples were too unreliable. This left us with 518 summary records, 
representing the experiences of 226,117 single-vehicle crashes, 
including 45,574 (20.16 percent) rollovers. Figure 1 shows the 
rollover rate (rollovers per single-vehicle crash) as a function of 
the SSF plotted for each of the 100 vehicle groups. These data have 
not been adjusted for differences in vehicle use or state reporting 
practices, but they do show a strong tendency for lower rollover 
rates with higher values of the SSF.

[[Page 3406]]

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BILLING CODE 4910-59-C
    We used the 1994-1998 General Estimates System (GES) for a 
comparison with the six-state rollover rate for the study vehicles 
as a group. The five years of GES data include 9,910 sampled 
vehicles that we identified as being in one of the 100 vehicle 
groups (based on decoding the VIN with the PC VINA 
software for those states that include the VIN on their police 
reports) involved in a single-vehicle crash, and 2,377 of these 
rolled over. Weighting the GES data to reflect the sample scheme 
(but not adjusting for missing VIN data) produces estimates of 
1,185,474 single-vehicle crashes per year, of which 236,335 (19.94 
percent) involved rollover. That is, the six states in our study 
have a rollover rate for police-reported crashes that is essentially 
the same as the national estimate produced from the GES data (with 
the qualification that the GES estimate is based on data from just 
those states that include the VIN on the police report).
    We defined the dependent variable ROLL as the fraction of 
single-vehicle crashes that involved rollover. The independent 
(explanatory) variables in the six-state combined model were those 
available in all six states. They were expressed as the fraction of 
single-vehicle crashes that involved each of the following ten 
situations: DARK, STORM, FAST, HILL, CURVE, BADSURF, MALE, YOUNG, 
OLD, and DRINK. We also defined dummy variables for five states 
(DUMMY__FL, DUMMY__MD, DUMMY__NC, DUMMY__PA, and DUMMY__UT, with 
Missouri used as the baseline case) to capture state-to-state 
differences in reporting thresholds and definitions. These variables 
have the value ``1'' if the crash occurred in that state (for 
example DUMMY__MD = 1 for all Maryland crashes), and they have the 
value ``0'' otherwise (for example, DUMMY__MD = 0 for all crashes in 
Florida, Missouri, North Carolina, Pennsylvania, and Utah). These 
are the fourteen variables we used in the earlier analysis 
(described in the Notice), plus the variable OLD.
    We ran the stepwise linear regression analysis against these 518 
summary records to describe the natural logarithm of rollovers per 
single-vehicle crash, which we call LOGROLL, as a function of a 
linear combination of the explanatory variables. (To avoid losing 
information on vehicle models with a low risk of rollover, we set 
ROLL to 0.0001 if there were no rollovers represented by the summary 
record.) We used the option that gives more weight to data points 
that are based on more observations, so vehicle groups with more 
crashes count for more in the analysis. Each data point was weighted 
by the number of single-vehicle crashes it represented, but the 
weighting was capped at 250. That is, data points based on more than 
250 observations were weighted by 250. Our rationale was that we 
wanted the model to fit well across the full range of SSF values, so 
we did not want to over-weight the data for the most-common models 
on the road.
    We ran a preliminary model using the SSF and the five state 
dummies to estimate LOGROLL. The model had an R2 of 0.73, 
and the coefficient of the SSF term (-2.8634) was highly significant 
(the t-statistic indicates that the probability that the coefficient 
is really zero is less than 0.0001); the details are included as 
Table 8a. Thus, it appears that the SSF is very useful in 
understanding rollover risk. We then performed a stepwise linear 
regression (using forward variable selection and a significance 
level of 0.15 for entry and removal from the model) on the six-state 
data; this is the same approach we used for the analysis described 
in the Notice. The stepwise regression procedure with the SSF chose 
three variables that describe the driving situation (DARK, FAST, and 
CURVE), three variables that describe the driver (MALE, YOUNG, and 
DRINK), and all five state dummy variables. The F-statistic for the 
model as a whole was 311, and the probability of a value this high 
by chance alone is less than 0.0001. The model had an R2 
of 0.88 and the coefficient of the SSF term (-3.3760) was highly 
significant; more details on the fit of the model are included as 
Table 8b. Note that adding the road-use variables increased both the 
model R2 (from 0.73 to 0.88) and the absolute value of 
the coefficient of the SSF term (from -2.8634 to -3.3760). That is, 
the effect of the SSF on rollover risk is estimated to be even 
greater after adjusting for differences in road use.
    We used the results of the model to adjust the observed number 
of rollovers per single-vehicle crash to account for differences 
among vehicle groups in their road-use characteristics in single-
vehicle crashes. For each of the 518 summary records, we used the 
regression results and the typical road use to estimate what LOGROLL 
would have been if road use for that vehicle group had been the 
typical road use observed for all the vehicles in the study. The 
approach is the one used in the Notice. We used an intermediate step 
to account for differences in road use and adjust the data towards 
the average experience for the study vehicles:

    ADJ__LOGROLLi
    =LOGROLLi
    BETA__DARK  x  (DARKi - MEAN__DARK)
    -BETA__FAST  x  (FASTi - MEAN__FAST)
    -BETA__CURVE  x  (CURVEi - MEAN__CURVE)
    -BETA__MALE  x  (MALEi - MEAN__MALE)
    -BETA__YOUNG  x  (YOUNGi - MEAN__YOUNG)

[[Page 3407]]

    -BETA__DRINK  x  (DRINKi - MEAN__DRINK)
    -BETA__DUMMY--FL  x  DUMMY__FLi
    -BETA__DUMMY__MD  x  DUMMY__MDi
    -BETA__DUMMY__NC  x  DUMMY__NCi
    -BETA__DUMMY__PA  x  DUMMY__PAi
    -BETA__DUMMY__UT  x  DUMMY__UTi
    + MEAN__DUMMIES,

where:

    ADJ__LOGROLLi is the estimate of what LOGROLL would 
have been for each summary record if all vehicles were used the same 
way,
    LOGROLLi is the value of LOGROLL observed for each 
summary record,
    BETA__DARK through BETA__DRINK are the coefficients (Beta-
values) of the road-use variables, DARK through DRINK, that were 
produced by the model (as shown in Table 8b),
    BETA__DUMMY__FL through BETA__DUMMY__UT are the coefficients of 
the state dummy variables, DUMMY__FL through DUMMY__UT, that were 
produced by the model,
    DARKi through DRINKi are the values of the 
road-use variables observed for each summary record,
    DUMMY__FLi through DUMMY__UTi are the 
values of the state dummy variables for each summary record (with no 
more than one of these equal to ``1,'' and all the rest equal to 
``0''),
    MEAN__DARK through MEAN__DRINK are the average values of the 
road-use variables observed in the study data (with 
MEAN__DARK=0.4314,
    MEAN__FAST=0.4807, MEAN__CURVE=0.3315, MEAN__MALE = 0.6276,
    MEAN__YOUNG=0.3987, and MEAN__DRINK=0.1509), and
    MEAN__DUMMIES is the average state adjustment in the study data.

    MEAN__DUMMIES was calculated for these 226,117 single-vehicle 
crashes from the coefficient of the state dummy variables and the 
number of cases in each state as follows:

    (1.2253  x  number of Florida cases
    +0.6933  x  number of Maryland cases
    +0.0000  x  number of Missouri
    +0.6969  x  number of North Carolina cases
    +1.2449  x  number of Pennsylvania cases
    +0.8622  x  number of Utah cases)
    /Total number of cases
    =0.8019,

    The adjusted rollover rate for each vehicle group is then 
estimated by:

    ADJ__ROLL=e(ADJ__\\\)

This is our estimate of what the rollover rate would have been if 
all vehicle groups were used in the same way, and it reflects the 
average use patterns of all vehicles in the study. The adjusted 
rollover rates are shown in Figure 2.
    The average adjusted number of rollovers per single-vehicle 
crash for all the study vehicles in the six states is 0.1982, which 
is essentially the same as the rollover rate in the original study 
data (0.2016) and the rollover rate estimated from the GES data 
(0.1994) for these 100 vehicle groups. A linear model fit through 
the adjusted data is described by the equation:

    LOGROLL = 2.5861--3.3760  x  SSF.

    The model has an R2 of 0.85, and the coefficient of 
the SSF term was highly significant. Details on the fit of the model 
through the adjusted rollover rates are included as Table 8c.
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BILLING CODE 4910-59-C
Exponentiating both sides of the equation produces an estimate that 
the number of rollovers per single-vehicle crash is approximated by 
the curve:

ROLL = 13.28  x  e(-3.3760  x  SSF).

The estimated rollover rates for the SSF values between 0.95 and 
1.55 are shown in Table 19 in the column labeled ``Model 1,'' and 
the estimates for the observed range (SSF values from 1.00 to 1.53) 
are shown as Figure 2. This model form has very useful properties. 
The increase in the SSF that is associated with halving the number 
of rollovers per single-vehicle crash is estimated as 0.21. For 
example, the number of rollovers per single-vehicle crash under 
average conditions is estimated as:

0.44 for a SSF of 1.01
0.23 for a SSF of 1.22, and

[[Page 3408]]

0.11 for a SSF of 1.43.

Thus, rollover risk drops by a half when the SSF increases from 1.01 
to 1.22, and it drops in half again when the SSF increases from 1.22 
to 1.43.
    The SSF is both highly significant in the model and very 
important in describing rollover risk (the estimated rollover risk 
increases by a factor of 6.0 over the observed range of the data, 
from a SSF of 1.00 to 1.53). This means that changes in the SSF (or 
changes in how vehicles with low SSF values are used) has the 
potential for large reductions in rollover risk.

Fitting Logistic Models

    The Exponent report questioned the validity of using a linear 
regression analysis of summarized data, though they noted the 
advantages of this approach for describing the data. They suggested 
using a logistic regression analysis with the SSF and road-use 
variables, and they also suggested (as a way of dealing with 
potential cross-correlations) an approach that uses crash-risk 
scenarios in place of the road-use variables. They provided results 
from the states they used in their analysis, and we did a similar 
analysis of the eight states available to us. The data for two 
states, New Mexico and Ohio, were not combined with the data from 
the other six states because a rollover is reported in New Mexico or 
Ohio only if it is considered to have been the first harmful event 
in the crash. However, we did look briefly at these data because we 
were curious about how the rollover definition affects the analysis. 
We wanted to see how the risk of a rollover occurring as the first 
harmful event in a single-vehicle crash varies as a function of the 
SSF as reported in these two states.
    We ran a logistic regression analysis for each state to model 
rollover as a function of the SSF and the road-use variables. For 
each state, we used the explanatory variables available for the 
linear regression analysis plus other variables that were available 
in each state, as described in Table 7. The fits of the models are 
summarized in Tables 9a through 16a. Each model seems to fit the 
data well. The coefficient of the SSF term varies from (-3.0800) in 
North Carolina to (-4.3908) in Florida. The values for New Mexico 
(-3.0809) and Ohio (-4.3642) fall in this range, which suggests that 
the choice between ``all rollovers'' and ``first harmful event 
rollovers'' may not be critical for a basic understanding of the 
sensitivity of rollover risk to the SSF (though the choice is 
important in determining the absolute level of rollover risk). In 
all cases, the coefficient of the SSF term was highly significant; 
the probability of a chi-square this large by chance alone (the 
smallest chi-square values were 209 for New Mexico and 416 for Utah) 
was estimated as less than 0.0001.
    We then combined the data from the six states that have the best 
rollover reporting (that is, data that were not limited to first-
harmful-event rollovers) and used them together in a logistic model, 
using the explanatory variables they have in common. We used the 
approach Charles Kahane described in his study of the safety effects 
of vehicle size. He used dummy variables to capture reporting 
differences in a logistic model of state data, and the results are 
included in Relationships between Vehicle Size and Fatality Risk in 
Model Year 1985-93 Passenger Cars and Light Trucks (Charles J. 
Kahane, Evaluation Division, Office of Plans and Policy, National 
Highway Traffic Safety Administration, DOT HS 808 570, January 
1997). The results of the six-state combined model are shown as 
Table 17a. The model fits the data well, and the SSF is highly 
significant in the model (with a chi-square value of 7,230).
    The coefficient of the SSF term in the logistic model for each 
state and for the six-state combined model describes the 
relationship between the rollover rates for any two values of the 
SSF, and we can use this relationship to estimate the rollover rate 
under average road-use conditions for each value of the SSF. We used 
the method that Ellen Hertz described in her study of the safety 
effects of vehicle weight. She estimated injury risk based on a 
logistic model of state data, and the results are included in A 
Collection of Recent Analyses of Vehicle Weight and Safety (T.M. 
Klein, E. Hertz, and S. Borener, Mathematical Analysis Division, 
National Center for Statistics and Analysis, Research and 
Development, National Highway Traffic Safety Administration, DOT HS 
807 677, May 1991). We defined:

BETASSF = the coefficient of the SSF term in the logistic model for 
a state,
ROLLSSF = the rollover rate at a specific value of the 
SSF, and
ODDSSSF = the odds of rollover at a specific value of the 
SSF.

We choose a SSF of 1.00 as the basis for the calculations. The 
relationship between ROLL1.00 and any other 
ROLLSSF can be calculated for each state as follows:

ROLLSSF = ODDSSSF / (1 + ODDSSSF)

where

ODDSSSF = e ((SSF - 1.00)  x  BETASSF)  x  
ROLL1.00 / (1 - ROLL1.00).

The results of the logistic analysis of the Florida data are shown 
in Table 9a, including an estimate that:

BETASSF = (-4.3908),

so all we need for rollover rate estimates across the range of the 
SSF is an estimate of ROLL1.00 in Florida. We estimated 
ROLL1.00 using the following approach. For each state, we defined:

ODDSALL = odds of rollover for the study vehicles as a 
group,
LOGODDSALL = the natural logarithm of ODDSALL, 
and
MEANSSF = the average SSF for the study vehicles.

The model says that:

LOGODDS = T + (BETASSF  x  SSF),

where

T = a linear function of the explanatory variables,

and we solved for the ``average'' value of T such that:

LOGODDSALL = T + (BETASSF  x  MEANSSF).

That is, we assumed that the results of the logistic model apply to 
the average rollover rate and SSF value for the vehicles as a group, 
and this means that:

T = LOGODDSALL - (BETASSF  x  MEANSSF).

The rollover rate for all the vehicles included in the Florida study 
was 0.2044 and their average SSF was 1.2894, which means that:

T = loge(0.2044/0.7956) - (-4.3908  x  1.2894) and
T = 4.3025 at the average rollover odds and SSF values.

We call this specific value of the function T, ``T0.'' Then, after 
controlling for other factors, LOGODDSSSF is estimated 
as:

LOGODDSSSF = T0 + (BETASSF  x  SSF),

and at SSF=1.00 in Florida, this is calculated as:

LOGODDS1.00 = 4.3025 - (4.3908  x  1.00),

so

LOGODDS1.00 = (-0.0883).

ROLL1.00 is estimated from the LOGODDS1.00 as:

ex/(1 + e x),

where x is the LOGODDS1.00, so the rollover rate at a SSF 
value of 1.00 is estimated as 0.4778 rollovers per single-vehicle 
crash. The rollover rate for all other values of the SSF can be 
estimated using:

ODDSSSF = e((SSF - 1.00)  x  BETASSF)  x  
ROLL1.00/(1 - ROLL1.00)

and

ROLLSSF = ODDSSSF/(1 + ODDSSSF).

    We used this approach for each state and for the six-state 
combined model. The average rollover rate and SSF for each state and 
for the six-state combined data are shown in Table 18, along with 
the estimated rollover rates for a SSF of 1.00. For example, the 
rollover risk for the six-states combined is estimated as 0.4031 at 
an SSF of 1.00, and it is shown in the column for the results of the 
models based on ``individual variables.'' (The results of the models 
based on ``crash scenarios'' are described below.) The results for 
each value of the SSF are shown in the column labeled ``Model 2'' in 
Table 19.
    As a check of the six-state combined model, we calculated the 
average rollover risk for each value of the SSF based on the 
individual state models. For example, we calculated the average 
rollover rate for a vehicle with a SSF of 1.00 by taking the average 
of the estimates for these six states (that is, Florida, Maryland, 
Missouri, North Carolina, Pennsylvania, and Utah), weighted by the 
size of each state (as measured by the number of single-vehicle 
crashes involving any study vehicle in each state). The result is an 
estimated risk of 0.4101 rollovers per single-vehicle crash for an 
SSF of 1.00, and the same procedure was applied to each value of the 
SSF from 0.95 to 1.55. The results are shown as the column labeled 
``Model 3'' in Table 19.
    The Exponent report also suggested using an approach they called 
a ``crash scenario analysis'' to address possible interactions among 
the explanatory variables. This idea is interesting and conceptually 
simple. The single-vehicle crashes from each state are categorized 
into cells defined by the possible combinations of the road-use 
variables. For example, the Florida logistic analysis used 14 road-
use variables: DARK, STORM, RURAL, FAST, HILL, CURVE, BADROAD, 
BADSURF,

[[Page 3409]]

MALE, YOUNG, OLD, NOINSURE, DRINK, and NUMOCC. NUMOCC is the count 
of occupants in each vehicle, and the other 13 variables take on the 
value ``0'' or ``1'' (indicating ``no'' or ``yes''). This produces a 
large number of possible combinations of the variable values:

    213  x  the number of levels of NUMOCC.

    Converting NUMOCC into a dichotomous variable (for example, one 
that identifies vehicles with at least three occupants) yields 14 
dichotomous variables, which means 214 combinations of these 
variables, or 16,384 cells for the various crash scenarios. In 
practice, not all combinations will occur (there were 2,034 non-zero 
cells in the Florida data), and some non-zero cells have very low 
counts (there were 267 cells in the Florida data with at least 25 
observations). The rollover rate for each cell can be calculated 
from these data, and this is a measure of the risk associated with 
that scenario. This rate can be used in place of all the road-use 
explanatory variables (for example, in place of the 14 original 
road-use variables in the Florida analysis). The Exponent report 
recommends a refinement to this calculation so that the scenario-
risk variable for each specific vehicle reflects the rollover rate 
for all other vehicles in its cell. For example, in a cell with 100 
vehicles and 20 rollovers, the scenario-risk variable (SCENRISK) 
will be calculated as:

20/(100 - 1) for each nonrollover vehicle

and as

(20 - 1)/(100 - 1) for each rollover vehicle.

    Using a crash-scenario variable is an interesting idea, even 
though the analytical results in the Exponent report seem to show 
that the individual-variable and crash-scenario logistic models 
produced very similar results. The standardized estimates for the 
coefficients of the SSF term produced by the two approaches (and our 
own results) are shown in Table 20. We attempted to duplicate the 
crash-scenario analysis based on the description provided in the 
Exponent report. The concept seems clear and logical, and we made 
the following decisions in implementing it for this analysis. First, 
we reviewed the output from the logistic regression on individual 
variables for each state and selected those for which the 
probability of a greater chi-square value was less than 0.20. We 
reasoned that using a large number of variables to define the crash 
scenarios would tend to produce many cells with small sample sizes, 
and that the variables with smaller chi-square values would be 
missed less. A review of Tables 9a through 16a shows that this 
eliminated only one variable in Florida (DARK), but it eliminated 
five variables in Utah (STORM, HILL, MALE, YOUNG, and OLD). Second, 
we converted NUMOCC into MANYOCC (with value ``1'' meaning three or 
more occupants, and ``0'' meaning one or two occupants). Again, the 
purpose of this was to reduce the number of cells with small sample 
counts, while retaining the essential information.
    Third, we tabulated the number of single-vehicle crashes 
(SVACCS) and the number of rollovers (ROLLACCS) for each combination 
of DARK, STORM, RURAL, FAST, HILL, CURVE, BADROAD, BADSURF, MALE, 
YOUNG, OLD, NOINSURE, DRINK, and MANYOCC that had been selected for 
inclusion in each state. We eliminated any combination (that is, any 
crash scenario) with fewer than 25 observations. The results are 
summary data describing the experience of all vehicles in each crash 
scenario. Fourth, we merged the crash-scenario summary data for each 
state back onto the original data (that is, the data for each 
individual single-vehicle crash), so that each crash was linked to a 
count of the total number of single-vehicle crashes and the total 
number of rollovers that occurred in its crash scenario (its cell). 
We defined the scenario-risk variable, SCENRISK, as the rollover 
rate for all other vehicles in that crash scenario in that state. 
The calculation was as follows:

SCENRISK = (ROLLACCS - ROLL)/(SVAACCS - 1).

Recall that ROLL is coded as ``1'' if the vehicle rolled over and 
``0'' if it did not, so this equation produces an estimate of the 
rollover rate for all vehicles in the crash scenario except for the 
one case under study; this was the method recommended by the 
Exponent report. This scenario-specific rollover rate is calculated 
for each vehicle on the file and is then available as an explanatory 
variable for a logistic model.
    We ran a logistic regression analysis against the data for each 
state and for the six-state combined data to model rollover risk as 
a function of two variables: the SSF and SCENRISK. The fits of the 
models are summarized in Tables 9b through 17b. Each table shows the 
number of crash scenarios with at least 25 observations and the 
total number of crashes in these more-frequent scenarios. Each model 
seems to fit the data well. The coefficient of the SSF term in the 
crash-scenario logistic model for each state describes the 
relationship between any two values of the SSF. We applied the 
approach we used for the individual-variable logistic model to 
estimate the rollover risk for each value of the SSF and to combine 
the values across states. The rollover rates at a SSF of 1.00 are 
shown in Table 18, and the estimated rollover rates as a function of 
the SSF are shown in Table 19. The column labeled ``Model 4'' shows 
the results for the six-state model, and the column labeled ``Model 
5'' shows the average of the individual models for the six states. 
Note that the individual-variable and the crash-scenario approaches 
produce very similar numbers. This is consistent with the results 
reported in the Exponent report (and summarized in Table 20, using 
the standardized estimates of the coefficients).

Comparing the Models

    The rollover rates estimated across the range of SSF values for 
the six states combined are shown in Table 19 for all five 
statistical models (the linear model of summarized data and the four 
versions of the logistic model), and the estimates for the observed 
values of the SSF are plotted in Figure 3. The five models are as 
follows:

Model 1: Linear model of the summarized data,
Model 2: Logistic model of the six-state combined data, based on 
individual variables,
Model 3: Average of the logistic models for the six states, based on 
individual variables,
Model 4: Logistic model of the six-state combined data, based on 
crash scenarios, and
Model 5: Average of the logistic models for the six states, based on 
crash scenarios.

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There are important similarities between the estimates produced by 
the two approaches: both the linear model of summarized data and the 
logistic models suggest a strong relationship (in terms of 
statistical significance and in terms of the magnitude of the 
effect) between the SSF and rollover risk. The average slope across 
the range of the observed SSF values (from 1.00 to 1.53) shown in 
Figure 3 is -0.713 for the linear model; the logistic models produce 
estimates of a slightly smaller effect, with average slopes between 
-0.598 and -0.555. Both types of models agree in estimating a large 
increased risk for vehicles with a low SSF. The four logistic models 
produce very similar results, and each suggests that rollover risk 
is very sensitive to the SSF (only slightly less so than estimated 
from the results of the linear model of the summarized data).
    Figure 3 shows that the greatest absolute differences in the 
rollover rate estimates are at the lowest values of the SSF. The 
values of the rollover rate estimated for a SSF of 1.00 were as 
follows:

Model 1 = 0.4551 (linear model of the summarized data),
Model 2 = 0.4101 (logistic model of the six-state combined data with 
individual variables),
Model 3 = 0.4031 (average of the logistic models for the six states 
with individual variables),
Model 4 = 0.3999 (logistic model of the six-state combined data with 
crash scenarios), and
Model 5 = 0.3929 (average of the logistic models for the six states 
with crash scenarios),

The results of the four logistic models are almost indistinguishable 
in Figure 3: the crash-scenario approach produces results that are 
only slightly different from the individual-variable approach (the 
former are a little lower at a SSF of 1.00 and little higher at an 
SSF of 1.53), and the average of the logistic models for the six 
states produces results that are only slightly different from the 
logistic model of the six-state combined data (the former are a 
little lower at a SSF of 1.00 and little higher at an SSF of 1.53).
    The results of our logistic analyses seem to differ only 
slightly from those described in the Exponent report, and much of 
the difference may be the result of our decision to omit wheelbase 
from the models. We did not include wheelbase as an explanatory 
variable because we could not identify any physical reason for an 
effect on rollover risk. However, we reran each analysis with the 
addition of wheelbase to test the sensitivity of the results to this 
decision. In every case, adding wheelbase to the model produced a 
higher estimate of the effect of the SSF on rollover risk and a 
higher estimate of rollover risk for the lowest values of the SSF. 
This occurred for all 18 models (those estimated using both the 
individual-variable and crash-scenario approaches for each of the 
eight states and for the six-state combined data), despite a 
negative value for the coefficient of the wheelbase term in each 
model. That is, the coefficient of the SSF term was negative in each 
of the original models, it became more negative in the presence of 
wheelbase, and wheelbase itself had a negative coefficient in each 
model in which it was included.
    Adding wheelbase seemed to produce results closer to those in 
the Exponent report. That report does not include the estimates of 
the variable coefficients, but it does include the standardized 
coefficients. These are shown in our Table 20, along with the 
corresponding values from our analysis. For example, when we ran the 
logistic regression analysis on the Florida data and used wheelbase 
as one of the explanatory variables, we obtained values of (-0.392) 
and (-0.374) for the standardized coefficients from the individual-
variable and crash-scenario models, respectively. These are higher 
than the values we obtained without wheelbase, (-0.349) and 
(-0.327), and they are very close to the values in the Exponent 
report, (-0.383) and (-0.381). Adding wheelbase to our models 
produced higher estimates of the coefficient for the SSF term and 
higher estimated rollover rates for vehicles with lower SSF values. 
For example, the six-state models that included wheelbase produced 
estimates that the coefficients of the SSF term are (-3.9525) and 
(-3.7918) and the estimated rollover rates for a SSF of 1.00 are 
0.4338 and 0.4228 for the individual-variable and crash-scenario 
approaches, respectively.
    There is also one important difference between the linear 
analysis of summary data and the logistic analysis of individual 
crashes. We limited the summary data to those based on at least 25 
observations and

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we capped the weighting at 250 to avoid over-emphasizing the more-
popular vehicles. However, the logistic regression analysis on 
individual crashes uses all observations equally. When we removed 
the two thresholds from the linear analysis, we obtained slightly 
lower estimates of the effect of the SSF on rollover risk, and the 
relationship between the adjusted rollover rates and the SSF is 
described by:

ROLL = 10.99e (- 3.2356  x  SSF)

This model produces an estimate of 0.4323 rollovers per single-
vehicle crash at an SSF of 1.00, which is closer to the estimates 
from our logistic models (and essentially the same as the estimates 
from the logistic models that include wheelbase as an explanatory 
variable).

Interpreting the Analytical Results

    Many of the comments in the Exponent report reflect an interest 
in evaluating the relative strength of the driver and vehicle 
contributions to rollover risk. We agree that this is an interesting 
question, but it is not the one we set out to address. Our 
perspective is that of a person choosing a new vehicle who wants to 
know how his choice of vehicle will affect his risk of being 
involved in a rollover. We are interested in eliminating the 
confounding effects of road use so we can isolate the effect of the 
vehicle on rollover risk. The importance of road-use factors does 
not preclude a role for vehicle-specific information.
    Also, a factor can be important without suggesting an easy 
remedy. Consider two factors that increase the risk of rollover 
given a single-vehicle crash: driver age (specifically, the effect 
of young, inexperienced drivers) and curved roads. We do have some 
influence over their effect on rollover risk: better driver training 
and better road design can help reduce rollovers even among young 
drivers on curved roads. However, some additional risk is a given 
for people who are still gaining on-road experience, and curved 
roads are a necessity in many places. So, while driver and other 
road-use factors are important to understanding rollover risk, this 
is not the same as saying that all rollovers can be prevented by 
driver and other road-use remedies. Vehicle design plays an 
important role in understanding and mitigating rollover risk even 
among young drivers on curved roads by making vehicles more-
forgiving of driver and road limitations, and our analysis describes 
the magnitude of that effect.
    Another comparison may help clarify why we believe that the SSF 
can be useful even though driver and other road-use factors are such 
valuable predictors of rollover risk. Using the same approach 
Exponent used for SSF and other factors involved in rollover, one 
can statistically demonstrate that seat belt use is insignificant in 
preventing injuries from a crash. The 1998-1999 National Automotive 
Sampling System (NASS) data include 7,631 investigated unbelted 
drivers of light passenger vehicles that were towed from a frontal 
nonrollover crash (Table 22), and weighting these data to reflect 
the sampling plan produces an annual average estimate of 171,284 
drivers involved each year. An estimated 11,569 of these were 
seriously injured (that is, they died or received an injury rated as 
three or higher on the Abbreviated Injury Scale). The overall risk 
of serious injury was 6.75 percent, but the risk varied greatly as a 
function of the change in vehicle velocity during the impact (that 
is, the delta V). For delta V less than 10 mph, the risk of serious 
injury was 0.76 percent.
    If all 171,284 drivers in these towaway crashes had been injured 
at the same rate as those in the lowest delta V range, we would have 
seen:

0.0076  x  171,284 = 1,302 serious injuries

among unbelted drivers in frontal crashes. Half of these (601 
serious injuries) could have been prevented if the drivers had used 
a lap-and-shoulder belt. Thus, we have the following:

171,284 serious injuries among unbelted drivers, of which 1,202 
would have occurred if delta V was low, of which 601 would have 
occurred if belts were used.

    According to the logic proposed by Exponent, we would interpret 
the results as follows:

99.30 percent of serious injuries are attributable to high crash 
speeds, and 0.35 percent are attributable to neglecting to use 
belts.

    Clearly this is nonsense. Belt use will prevent serious injury 
even among those in higher-speed crashes (half of the 11,569 serious 
injuries that did occur among unbelted drivers at any crash speed 
could have been prevented by belt use, for a reduction of 5,784 
serious injuries from belt use). More importantly, belts offer a 
practical solution, while there is no practical way to reduce all 
crash speeds to less than 10 mph.
    Note that this is comparable to the approach that the Exponent 
report used in arguing that the value of the SSF in understanding 
rollover risk was in the range of 3-8 percent. They estimated the 
relative risk of the lowest-risk scenario, estimated how many 
rollovers could be prevented if all single-vehicle crashes occurred 
with the risk of the lowest-risk scenario, and relegated the 
importance of the SSF to a fraction of the small amount of risk that 
remained. The lowest-risk scenario that they use as their standard 
appears to be (based on the table on page 31 of their report) 
crashes that did not involve a vehicle defect and that did involve a 
mature driver who had not been drinking or engaged in risky driving, 
on a straight, urban road with a speed limit of 50 mph or less, and 
for which the first harmful event was a collision with a traffic 
unit in a single-vehicle crash; the bulk of these crashes may be 
collisions with pedestrians and pedalcyclists, which would tend to 
be reported because of the injuries to the non-motorists.
    These are crashes with almost no chance of rollover, and so they 
are essentially irrelevant to a rollover-prevention program. Also 
note that some of these factors can be addressed by the driver 
(driving more carefully and when fully sober), but others are beyond 
the control of the driver (roads are curved, through rural areas, 
and with speed limits of 55 mph so traffic can move efficiently 
through all parts of the country). Young drivers gain experience 
through driving, and they eventually become mature drivers; in the 
meantime, they also benefit from more-stable vehicles. It is 
difficult to see how Exponent's the low-risk scenario could be used 
as an alternative to the SSF as the basis for a rollover safety 
program.
    The approach described in the Exponent report (comparing the 
risk associated with the SSF to all the risks associated with road-
use factors) would suggest, in our example based on NASS data, that 
reducing delta V should be a higher safety priority than increasing 
belt use. (To use an extreme example to make a point, using the 
approach described in the Exponent report for a study of air crashes 
would suggest that preventing gravity is more important than regular 
maintenance of the airplane.) However, belt use programs have been 
successful because the remedy is simple and cost-effective and 
because the importance of delta V does not reduce the importance of 
belt use in preventing injury. We believe a similar argument can be 
made for focusing on the SSF, while agreeing that driver and other 
road-use variables may be the basis for other safety improvements.

Conclusion

    The Exponent report acknowledged the potential advantages of 
multiple linear analysis, and their recommendation is relevant here:
Multiple regression analysis can have some value as an explanatory 
tool for describing factors related to vehicle rollover. Linear 
regression analysis, however, must only be used in this heuristic 
way and only when prior research has demonstrated that linear 
regression produced essentially the same results as did a rigorous 
and valid statistical analysis. [page 28]

Table 19, Figure 3, and the sensitivity analyses described above 
suggest that the linear and logistic regression approaches produce 
essentially the same results. The Exponent report recommended a 
logistic approach and concluded that the linear approach based on 
summarized data overstated the value of the SSF in understanding 
rollover risk. This does not seem to be the case. The linear 
approach produces estimates of rollover risk that are a little more 
conservative (in the sense that they are lower) than those from the 
logistic models for most observed values of the SSF and for most 
vehicles on the road today. The Exponent report included much lower 
estimates for rollover risk across the range of SSF values, but this 
was not a result of the logistic approach. Rather, it was the result 
of tying the estimates to the low-risk scenario (where rollover is 
unlikely).

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Appendix II: List of Test Vehicles for MY2001 Rollover Resistance 
Ratings

    NHTSA expects to measure the Static Stability Factor and provide 
rollover resistance ratings for each of the following model year 
2001 vehicles. For pickups and SUVs, the agency plans to measure and 
report separately on both two-wheel-drive and four-or all-wheel-
drive variants of each model, where applicable. In no case will a 
two-wheel-drive measurement be applied to a four-or all-wheel-drive 
variant, or vice versa. The agency may need to make substitutions 
for some of the models listed depending on availability. The list is 
arranged largely alphabetically within each vehicle category, and 
passenger cars are sorted by class according to the classifications 
used in the NHTSA NCAP frontal and side crash test programs. The 
order in which vehicles will be tested will be determined by the 
test laboratory and will depend primarily on model availability.
    The following class abbreviations are used:

LPC = light passenger car
CPC = compact passenger car
MPC = medium passenger car
HPC = heavy passenger car
SUV = sport utility vehicle
LT = light truck

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[FR Doc. 01-973 Filed 1-9-01; 2:33 pm]
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