Assessment of Integrated Pedestrian Protection Systems with Autonomous Emergency Braking (AEB) and Passive Safety Components

Abstract

Objective:
Autonomous emergency braking (AEB) systems fitted to cars for pedestrians have been predicted to offer substantial benefit. On this basis, consumer rating programs-for example, the European New Car Assessment Programme (Euro NCAP)-are developing rating schemes to encourage fitment of these systems. One of the questions that needs to be answered to do this fully is how the assessment of the speed reduction offered by the AEB is integrated with the current assessment of the passive safety for mitigation of pedestrian injury. Ideally, this should be done on a benefit-related basis. The objective of this research was to develop a benefit-based methodology for assessment of integrated pedestrian protection systems with AEB and passive safety components. The method should include weighting procedures to ensure that it represents injury patterns from accident data and replicates an independently estimated benefit of AEB.

Methods:
A methodology has been developed to calculate the expected societal cost of pedestrian injuries, assuming that all pedestrians in the target population (i.e., pedestrians impacted by the front of a passenger car) are impacted by the car being assessed, taking into account the impact speed reduction offered by the car's AEB (if fitted) and the passive safety protection offered by the car's frontal structure. For rating purposes, the cost for the assessed car is normalized by comparing it to the cost calculated for a reference car. The speed reductions measured in AEB tests are used to determine the speed at which each pedestrian in the target population will be impacted. Injury probabilities for each impact are then calculated using the results from Euro NCAP pedestrian impactor tests and injury risk curves. These injury probabilities are converted into cost using "harm"-type costs for the body regions tested. These costs are weighted and summed. Weighting factors were determined using accident data from Germany and Great Britain and an independently estimated AEB benefit. German and Great Britain versions of the methodology are available. The methodology was used to assess cars with good, average, and poor Euro NCAP pedestrian ratings, in combination with a current AEB system. The fitment of a hypothetical A-pillar airbag was also investigated.

Results:
It was found that the decrease in casualty injury cost achieved by fitting an AEB system was approximately equivalent to that achieved by increasing the passive safety rating from poor to average. Because the assessment was influenced strongly by the level of head protection offered in the scuttle and windscreen area, a hypothetical A-pillar airbag showed high potential to reduce overall casualty cost.

Conclusions:
A benefit-based methodology for assessment of integrated pedestrian protection systems with AEB has been developed and tested. It uses input from AEB tests and Euro NCAP passive safety tests to give an integrated assessment of the system performance, which includes consideration of effects such as the change in head impact location caused by the impact speed reduction given by the AEB.

Full text

This article was downloaded by: [TRL Ltd]
On: 08 June 2015, At: 07:09
Publisher: Taylor & Francis
Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,
37-41 Mortimer Street, London W1T 3JH, UK
Click for updates
Traffic Injury Prevention
Publication details, including instructions for authors and subscription information:
http://www.tandfonline.com/loi/gcpi20
Assessment of Integrated Pedestrian Protection
Systems with Autonomous Emergency Braking (AEB)
and Passive Safety Components
Mervyn Edwardsa, Andrew Nathansona, Jolyon Carrolla, Marcus Wischb, Oliver Zanderb & Nils
Lubbec
a Transport Research Laboratory, Berkshire, England
b Bundesanstalt für Straßenwesen (Federal Highway Research Institute), Bergisch Gladbach,
Germany
c Toyota Motor Europe, Zaventem, Belgium
Published online: 01 Jun 2015.
To cite this article: Mervyn Edwards, Andrew Nathanson, Jolyon Carroll, Marcus Wisch, Oliver Zander & Nils Lubbe (2015)
Assessment of Integrated Pedestrian Protection Systems with Autonomous Emergency Braking (AEB) and Passive Safety
Components, Traffic Injury Prevention, 16:sup1, S2-S11, DOI: 10.1080/15389588.2014.1003154
To link to this article: http://dx.doi.org/10.1080/15389588.2014.1003154
PLEASE SCROLL DOWN FOR ARTICLE
Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in
the publications on our platform. Taylor & Francis, our agents, and our licensors make no representations or
warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Versions
of published Taylor & Francis and Routledge Open articles and Taylor & Francis and Routledge Open Select
articles posted to institutional or subject repositories or any other third-party website are without warranty
from Taylor & Francis of any kind, either expressed or implied, including, but not limited to, warranties of
merchantability, fitness for a particular purpose, or non-infringement. Any opinions and views expressed in this
article are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The
accuracy of the Content should not be relied upon and should be independently verified with primary sources
of information. Taylor & Francis shall not be liable for any losses, actions, claims, proceedings, demands,
costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in
connection with, in relation to or arising out of the use of the Content.
This article may be used for research, teaching, and private study purposes. Terms & Conditions of access and
use can be found at http://www.tandfonline.com/page/terms-and-conditions
It is essential that you check the license status of any given Open and Open Select article to confirm
conditions of access and use.

Traffic Injury Prevention (2015) 16, S2–S11
Published with license by Taylor & Francis
ISSN: 1538-9588 print / 1538-957X online
DOI: 10.1080/15389588.2014.1003154
Assessment of Integrated Pedestrian Protection Systems with
Autonomous Emergency Braking (AEB) and Passive Safety
Components
MERVYN EDWARDS1, ANDREW NATHANSON1, JOLYON CARROLL1, MARCUS WISCH2, OLIVER ZANDER2,
and NILS LUBBE3
1Transport Research Laboratory, Berkshire, England
2Bundesanstalt f¨
ur Straßenwesen (Federal Highway Research Institute), Bergisch Gladbach, Germany
3Toyota Motor Europe, Zaventem, Belgium
Received 12 November 2014, Accepted 24 December 2014
Objective: Autonomous emergency braking (AEB) systems fitted to cars for pedestrians have been predicted to offer substantial
benefit. On this basis, consumer rating programs—for example, the European New Car Assessment Programme (Euro NCAP)—are
developing rating schemes to encourage fitment of these systems. One of the questions that needs to be answered to do this fully is
how the assessment of the speed reduction offered by the AEB is integrated with the current assessment of the passive safety for
mitigation of pedestrian injury. Ideally, this should be done on a benefit-related basis.
The objective of this research was to develop a benefit-based methodology for assessment of integrated pedestrian protection
systems with AEB and passive safety components. The method should include weighting procedures to ensure that it represents
injury patterns from accident data and replicates an independently estimated benefit of AEB.
Methods: A methodology has been developed to calculate the expected societal cost of pedestrian injuries, assuming that all
pedestrians in the target population (i.e., pedestrians impacted by the front of a passenger car) are impacted by the car being assessed,
taking into account the impact speed reduction offered by the car’s AEB (if fitted) and the passive safety protection offered by the
car’s frontal structure. For rating purposes, the cost for the assessed car is normalized by comparing it to the cost calculated for a
reference car.
The speed reductions measured in AEB tests are used to determine the speed at which each pedestrian in the target population will
be impacted. Injury probabilities for each impact are then calculated using the results from Euro NCAP pedestrian impactor tests
and injury risk curves. These injury probabilities are converted into cost using “harm”-type costs for the body regions tested. These
costs are weighted and summed. Weighting factors were determined using accident data from Germany and Great Britain and an
independently estimated AEB benefit. German and Great Britain versions of the methodology are available. The methodology was
used to assess cars with good, average, and poor Euro NCAP pedestrian ratings, in combination with a current AEB system. The
fitment of a hypothetical A-pillar airbag was also investigated.
Results: It was found that the decrease in casualty injury cost achieved by fitting an AEB system was approximately equivalent to
that achieved by increasing the passive safety rating from poor to average. Because the assessment was influenced strongly by the level
of head protection offered in the scuttle and windscreen area, a hypothetical A-pillar airbag showed high potential to reduce overall
casualty cost.
Conclusions: A benefit-based methodology for assessment of integrated pedestrian protection systems with AEB has been developed
and tested. It uses input from AEB tests and Euro NCAP passive safety tests to give an integrated assessment of the system
performance, which includes consideration of effects such as the change in head impact location caused by the impact speed reduction
given by the AEB.
Keywords: pedestrian, active safety, crash avoidance, brake assist, AEB
©Mervyn Edwards, Andrew Nathanson, Jolyon Carroll, Marcus
Wisch, Oliver Zander, and Nils Lubbe
Managing Editor David Viano oversaw the review of this article.
Address correspondence to Mervyn Edwards, Transport Re-
search Laboratory, Crowthorne House, Nine Mile Ride, Work-
ingham, Berkshire, England RG40 3GA. E-mail: medwards@trl.
co.uk
Introduction
Annually in the European Union (EU) about 6,500 pedes-
trians are killed and 156,000 are injured in road traffic
accidents (European Commission 2013). Around half of
these are hit by the front of a car. This article reports
and expands on work performed as part of a European
Downloaded by [TRL Ltd] at 07:09 08 June 2015

Passive Safety Components 3
Commission 7th framework project, called Assessment
methodologies for forward looking integrated Pedestrian
and further extension to Cyclists Safety Systems (AsPeCSS)
(ASPECSS 2014). The main aim of this project was to con-
tribute toward improving the protection of vulnerable road
users by developing harmonized test and assessment proce-
dures for forward-looking integrated pedestrian safety sys-
tems that incorporate both autonomous emergency braking
(AEB) and passive safety systems.
AEB systems for pedestrians have been predicted to offer
substantial benefit (Edwards et al. 2014; Ros´
en et al. 2010;
Searson et al. 2014). Ros´
en et al. (2010) estimated an effective-
ness of 44 and 33% for prevention of fatal and severe injuries,
respectively, for pedestrian casualties impacted by the front of
a car. An AEB system that activates the brakes one second
prior to a predicted impact with a maximum deceleration of
6m/s
2was assumed. Searson et al. (2014) estimated that the
fitment of a pedestrian AEB system would improve a car’s
European New Car Assessment Programme (Euro NCAP)
performance rating by approximately one color band, which
corresponds to an increase in the score of 25% of the maxi-
mum. Edwards et al. (2014) estimated an annual benefit for
the EU from about €1 billion to €3.5 billion depending on the
type of AEB and assuming that it was fitted to all cars.
On this basis, consumer rating programs—for example,
Euro NCAP—are developing rating schemes to encourage
fitment of these systems (Euro NCAP 2014). One of the ques-
tions that needs to be answered to do this fully is how the
assessment of the speed reduction offered by the AEB can be
integrated with the current assessment of the passive safety for
further mitigation of pedestrian injury or accident avoidance.
Ideally, this should be done on a benefit-related basis.
Lubbe et al. (2012) discussed the elements of existing safety
assessment methods relevant for a new integrated assessment,
such as Hamacher et al. (2011) and Hutchinson et al. (2012).
Lubbe et al. (2012) recommended that an integrated method-
ology should take into account the effect that the active system
has on the boundary conditions for the passive system; for ex-
ample, the change in head impact location resulting from the
speed reduction given by the AEB. They also recommended
validation and calibration against real-world data.
The aim of the work reported was to develop a methodology
to give an overall assessment of a car’s pedestrian protection
on a benefit basis, using the results of pedestrian AEB tests
and the standard impactor tests within Euro NCAP. Weighting
procedures were developed to ensure accurate representation
of injury patterns observed in accident data and the indepen-
dently estimated AEB benefit for a car representative of the
average fleet in the accident data.
Methodology
Concept
In principle, the cost of expected injury was calculated, assum-
ing that all casualties in the target population were impacted
by the car being assessed and taking into account the impact
speed reduction offered by the car’s AEB (if fitted) and the
passive safety protection offered by the car’s frontal structure.
This cost can be normalized by comparing it to the cost calcu-
lated for selected cars. It should be noted that different versions
of the methodology were developed for Germany and Great
Britain because of links of the methodology to the accident
data and differences between the German and Great Britain
accident data.
The methodology consists of five main steps as described
below and illustrated in Figure A1 (see online supplement).
Step 1: Active safety testing: Exposure–impact velocity curve
shift. The exposure–impact velocity curve for the pedestrian
casualty target population (i.e., pedestrians impacted by a
car front) was adjusted to account for the impact speed
reduction provided by the AEB system.
Step 2: Passive safety testing: Impactor measurement and ex-
trapolation. Following the Euro NCAP protocols, standard
impactor tests and simulations were conducted at the nomi-
nal speed, which approximately represents a 40 km/h pedes-
trian impact. Injury criteria values recorded were extrapo-
lated to all other speeds experienced by the pedestrian target
population.
Step 3: Calculation of injury frequency. Impact probabili-
ties for each area of the car’s front were calculated for
each impactor. Using these probabilities, the injury crite-
ria measurements from step 2, injury risk curves relating
these measurements to the probability of injury, and the
velocity–exposure data from step 1, injury risks for each Ab-
breviated Injury Scale (AIS) level were summed for tested
body regions for all casualties in the target population to
give injury frequency for tested body regions.
Step 4: Calculation of socioeconomic cost. Injury frequen-
cies for tested body regions were converted into costs using
“harm” cost information for the injuries considered; that is,
those related to the impactor injury criteria.
Step 5: Vehicle assessment: Weighting and summing. The body
region costs were weighted using calibration factors and
summed to give the total cost of injury assuming that all
pedestrians in the target population were involved in an
accident with the car being assessed. This cost was also
weighted using a calibration factor to account for factors
such as injuries to body regions not assessed by the im-
pactors and injuries caused by ground impact. This cost
can be compared with the cost calculated for other selected
cars to give a relative assessment of the car.
Step 1: Active Safety Testing: Exposure—Impact Velocity
Curve Shift
As described in Edwards et al. (2014), baseline
exposure–impact velocity curves were developed for
Great Britain and Germany (Figures 1 and 2) using detailed
and national accident data appropriate for each country.
For Great Britain, On the Spot (OTS) data were weighted
using STATS19 national data on the basis of the number of
fatal, serious, and slight casualties. For Germany, German
In Depth Accident Study (GIDAS) data were weighted using
the German national data.
Accident scenarios describe the typical situations in which
pedestrians are struck in real-world accidents. Test scenar-
Downloaded by [TRL Ltd] at 07:09 08 June 2015

4Edwards et al.
Fig. 1. Impact speed distribution curves developed for Great
Britain.
ios were used to assess the performance of an AEB system
in a laboratory (test track) environment and were designed
to reproduce the relevant parameters of the accident scenar-
ios that had a major effect on system performance. Mapping
accident to test scenarios was necessary to estimate what pro-
portion of the casualties in the target population (i.e., in the
exposure–velocity curves) would experience the impact speed
reductions measured in the test scenarios.
The 5 test scenarios used in the method were those de-
veloped within AsPeCSS described in Table A1 (see online
Fig. 2. Impact speed distribution curves developed for Germany.
supplement; Seiniger et al. 2014). The mapping shown in Ta-
ble 1 was used to determine the speed reduction for the casu-
alties in the target population. The 7 accident scenarios and
the proportion of casualties in each were defined in an early
part of the project and have been reported previously (Wisch
et al. 2013). A summary of the accident scenarios and the pro-
portions of casualties in them is shown in Table A2 (see online
supplement). It should be noted that there was a difference in
the mapping for the German and Great Britain versions of the
methodology for accident scenario 6, because in the German
data, these accidents usually involved the pedestrian crossing
one carriageway of the road before being hit by the car and
therefore from the AEB system point of view, the pedestrian
was not obstructed.
Step 2: Passive Safety Testing: Impactor Measurement
and Extrapolation
Euro NCAP impactor test data and the manufacturer input
data were used to give impactor injury criteria values for most
of the car’s frontal area that is likely to be hit by a pedestrian,
apart from areas with a wrap-around distance (WAD) beyond
2,100 mm, such as high on the windscreen and the windscreen
header rail for the headform impactor. These injury criteria
values, which represent impacts at nominally 40 km/h, were
extrapolated to other speeds using simple relationships found
in the literature or developed empirically from simulations per-
formed in the project. For example, for head injury criterion
(HIC) the relationship shown in Eq. (1) developed by Searson
et al. (2009) was used.
HIC1
HIC2
=v1
v25
2
,(1)
where vis speed.
One of the assumptions made was not to consider the effect
of an impactor “bottoming out” on stiff structures below the
struck structure.
Step 3: Calculation of Injury Frequency
As mentioned above, impact probabilities for each area of the
car’s front were calculated and with the velocity–exposure data
from step 1 used to calculate injury frequency for tested body
regions.
Impact Probabilities
These were calculated, both laterally and longitudinally, for
all impactors. Laterally it was assumed that the impact prob-
ability was uniform for all impactors. This assumption was
supported by an analysis of GIDAS data, which showed that
the weighted average distribution of impact location across the
car was approximately even with a slight bias to the right side
(nearside)—left 31%, center 30%, right 39%. Other work was
also found to support this assumption (Barrow et al. 2014).
Longitudinal impact probabilities were only considered to be
relevant for the headform impactor. For this impactor, the
following speed-dependent relationship between pedestrian
Downloaded by [TRL Ltd] at 07:09 08 June 2015

Passive Safety Components 5
Table 1. Mapping of accident to test scenarios
Test scenario
TS1 Walking (slow) adult, TS2 Running adult, TS3 Walking adult, TS4 Walking adult, TS5 Walking child obstructed,
Accident scenario center impact center impact near-side impact farside impact center impact
1: Crossing straight road, near-side, no
obstruction
16.5% 16.5% 48% 19%
2: Crossing straight road, farside, no
obstruction
15% 15% 45% 25%
3 and 4: Crossing at junction, near- or
farside, vehicle turning or not across
traffic
17.75% 17.75% 38% 27%
5: Crossing straight road, near-side, with
obstruction
100%
6: Crossing straight road, farside, with
obstruction
15% 15% 45% 25% 100%
7: Along carriageway on straight road, no
obstruction
100%
8: Not classified into scenarios 1 to 7 No speed reduction
DE =Germany, GB =Great Britain.
height and the longitudinal position of head impact in terms
of WAD was derived from the results of simulations with the
pedestrian human body THUMS model (Mottola et al. 2013):
WAD(log(v),Pedestrian Height) =−2227 +335 log(v)
+1.8 Pedestrian Height,
(2)
where Pedestrian Height is in millimeters and speed vis in
kilometers per hour.
Using Eq. (2) and population height distributions measured
for the UK (Department of Trade and Industry, UK 1996),
longitudinal impact probabilities in terms of WAD were de-
rived for the headform impactor.
Injury Risk
Injury risk curves describe the probability that a certain load
will cause a specific injury (Schmitt et al. 2004). The following
functions relating risk of human injury in terms of AIS to
impactor injury risk criteria were selected from the literature
for the appropriate impactors.
For the headform impactor, 2 sets of injury risk curves were
considered, namely, curves developed by Matsui (2004) using a
logistic regression type called “modified maximum likelihood
method” (MMLM) and those developed by NHTSA (1995),
which used data from Prasad and Mertz (1985). The Matsui
MMLM curves were selected for use in the model based mainly
on pragmatic reasons (Figure 3). These were that the devel-
opment of Matsui injury risk curves was based on pedestrian-
to-car head impact data as opposed to head-to–car interior
impact data for the NHTSA curves. In addition, the Matsui
curves gave head injury costs slightly closer to those calculated
from accident data during calibration when both sets of curves
were tried in the methodology.
For the upper legform impactor, an injury risk curve at
the AIS 2 level for femur and pelvis injuries as the average
of the 2 risk curves developed by EEVC WG17 (2002) were
adopted (Figure A2, see online supplement). These curves
were developed by Rodmell and Lawrence (1998) and included
12 reconstructed accidents reported in Matsui et al. (1998).
For the EEVC WG17 legform impactor, injury risk curves
from Matsui (2003) at AIS 2 level were used (Figure A3, see
online supplement). These were identified by Lawrence et al.
(2006) to represent the best current data.
For the flexible pedestrian legform impactor (Flex PLI),
injury risk curves from Takahashi et al. (2012) were used (Fig-
ures A4 and A5, see online supplement). An alternative injury
Fig. 3. Pedestrian headform injury risk curves (created with equa-
tions from Matsui 2004).
Downloaded by [TRL Ltd] at 07:09 08 June 2015

6Edwards et al.
risk curve for tibia fracture based on tibia bending moments
has been derived by Zander et al. (2011) alongside discus-
sions within the Informal Group on UN GTR No. 9, Phase 2
(Zander 2012). This could be used as an alternative in future
analyses.
Step 4: Calculation of Socioeconomic Cost
Injury frequencies for tested body regions were converted into
costs for tested body regions using harm-type information
(total monetary costs) from Zaloshnja et al. (2004) for the
injuries considered; that is, those related to the impactor injury
criteria.
Step 5: Vehicle Assessment: Weighting and Summing
The body region costs calculated in step 4 above were weighted
and summed to give the total societal cost of injury if it was
assumed that all pedestrians in the target population were in-
volved in an accident with the car being assessed. The follow-
ing calibration (weighting) factors were derived by comparing
the costs calculated with those known from accident data:
•A body region calibration factor to correct the relative cost
of injury calculated for the tested body regions—that is,
head, upper leg, and lower leg—to represent injury cost of
body regions observed in accident data.
•An overall calibration factor to correct the total cost of
injury was calculated. This should help take into account
injury to body regions not tested, injury caused by contacts
with parts of the car not tested currently, and injury caused
by ground impacts and align with an independently esti-
mated AEB benefit for a car representative of the average
fleet in the accident data. However, it does assume that the
cost of these injuries is proportional to the cost of injuries
to the tested body regions.
To calculate the calibration factors, impactor test results
were required for a car with passive safety protection levels
representative of those of the cars in the accident data sample.
The median registration date for cars in the accident data
samples was 1997 with a range of about 1987 to 2010. On this
basis, impactor data for a car with a registration year of about
1997 were developed to be broadly representative of those
in the accident data sample. This was not a straightforward
exercise because it was not possible to obtain impactor test
data directly for cars of this era in Euro NCAP pedestrian
assessment protocol version 6.0 or higher format (i.e., color
codes for all grid points for headform impact) because it did
not exist, because this protocol was only introduced in 2012.
It could not be generated easily either, because simulation
is required to do this and simulation models of these vehicles
were not available. Therefore, the following approach was used
to derive the necessary impactor data:
•Available worst-case impactor test data in a different for-
mat for a car of similar age was transformed into the format
required. A Volkswagen Golf V tested in 2003 was chosen
for this because much research work had been performed
with this car that provided information to enable this
transformation. In addition, this vehicle was a popular fam-
ily car well represented in the vehicle fleet.
•The transformed Golf V impactor data was scaled to be
representative of good, average, and poor Euro NCAP-
rated cars registered circa 1997.
Body Region Calibration Factors
The cost of injury for the head, upper leg, and lower leg body
regions seen in the accident was calculated using the GIDAS
(German) and OTS (Great Britain) accident databases. For
casualties in the target population (pedestrians impacted by
the front of a car), the AIS injuries to these body regions
were converted into costs using total monetary costs from Za-
loshnja et al. (2004) and summed. The distribution of these
costs was compared with that calculated by the methodology
described above to derive calibration factors for the German
and Great Britain versions of the methodology (see Table A3,
online supplement). This process was repeated using the im-
pactor data for the good and poor performing representative
cars. It was found that the calibration factors derived were
not that different to those derived for the average representa-
tive car. On this basis, it was decided to use the body region
calibration factors derived for the average representative car.
Overall Calibration Factor
Following application of the body region calibration factor,
the overall factor was calculated from a comparison of the cost
predicted by the methodology and the cost of injury estimated
from the accident data. The specific data used were injury
costs estimated from a benefit analysis reported in Edwards
et al. (2014). In this analysis, injury costs were calculated for
the following 4 potential situations:
•No AEB system fitted to cars.
•Current generation AEB pedestrian systems (2013+) fitted
to all cars.
•Second-generation AEB pedestrian systems (2018+) fitted
to all cars.
•Reference limit AEB pedestrian system (2023+) fitted to all
cars.
Specifications of the AEB systems are described in Edwards
et al. (2014). Both the Great Britain and German methodology
versions were run using input data to represent the 4 situations
described above. Impactor test data representative of an av-
erage car in the accident data sample and exposure–impact
velocity curves from the benefit for each of the 4 situations de-
scribed above were used as input data. The exposure–impact
velocity curves used took into account the effect of the AEB
system on the accident impact speed. The costs calculated
from the benefit analysis (Edwards et al. 2014) were compared
with those predicted by the methodology for each of the AEB
systems to derive calibration factors for the Great Britain and
German methodology versions, shown in Tables A4 and A5
(see online supplement), respectively. The calibration factors
for each AEB system variant were averaged to give overall
calibration factors.
Downloaded by [TRL Ltd] at 07:09 08 June 2015

Passive Safety Components 7
Results
Input Data Used to Test Methodology
Both versions of the assessment methodology (German and
Great Britain) were tested with the following input data:
•AEB input test data:
•No AEB system; that is, zero impact speed reduction
against the baseline exposure–impact velocity curves.
•Current AEB system; that is, speed reductions measured
for the 5 test scenarios described in Table A1 for a vehicle
tested in Seiniger et al. (2014; vehicle C).
•Passive safety impactor test data
•Two sets of impactor tests results representative of cur-
rent good, average, and poor performing vehicles. The
first set (referred to as composite vehicles) contained ve-
hicles with different windscreen areas, whereas the sec-
ond set (referred to as composite vehicles with constant
windscreen area) contained vehicles with identical wind-
screen areas to remove this variable.
•Because no overall good, average, and poor performing
Euro NCAP-rated vehicles existed, (generally vehicles
scored maximum points for legform, average for head-
form, and poor for upper legform), impactor tests results
representative of good, average, and poor performing ve-
hicles were developed by selecting results from a number
of vehicles on a component level (i.e., headform, upper
legform, and legform) and combining them into com-
posite vehicles. Because it was found that the assessment
methodology was sensitive to changes in the amount
of windscreen area in the head impact assessment zone
and this changed between the good, average, and poor
composite vehicles, good, average, and poor composite
vehicles with constant windscreen area were developed.
This was achieved by scaling the head impactor results
for the average composite vehicle to give poor and good
constant windscreen area composite vehicles with similar
Euro NCAP scores as for the poor and good composite
vehicles with variable windscreen area.
•Hypothetical A-pillar airbag:
•Impactor test results were modified to represent a hy-
pothetical airbag fitted to the vehicle, which covered the
A-pillars completely but did not cover the scuttle. This
airbag was assumed to offer protection at impact veloci-
ties between 21 and 51 km/h, reducing HIC at 40 km/h
from 6,000 to 400 (Fredriksson and Ros´
en 2014) and to
follow the same HIC–velocity relation as for other ve-
hicle structures given in Eq. (1). For this hypothetical
A-pillar airbag, Euro NCAP scores were estimated by
changing the rating from red (zero score) to green (full
score) for test points on the A-pillar.
The test data described above were input into the German
and Great Britain versions of the assessment methodology.
Results are shown in terms of total casualty costs for Germany
and Great Britain for the composite vehicles with no AEB
system, an AEB system representative of current systems, an
A-pillar airbag, and both an AEB system and an A-pillar
airbag (Table 2). Costs are nominally in euros for the German
Table 2. Assessment results in terms of total casualty cost for
variable windscreen area composite cars with good, average,
and poor Euro NCAP passive safety rating with no system,
with AEB system, with airbag, and with both AEB and airbag
fitted. Percentages show costs normalized to average passive
safety performance with no AEB system fitted. Note that Euro
NCAP scores increase for better protection, whereas methodol-
ogy assessment costs decrease
Passive safety level
Additional safety
system Good Average Poor
Euro NCAP passive safety score rating
No system 32.2(142%) 22.6(100%) 12.2(54%)
A-pillar airbag 33.4(148%) 24.4(108%) 13.3(59%)
Methodology (German version) integrated score rating (million euros)
No system 662(99%) 667(100.0%) 943(141%)
AEB system
representative of
current systems
559(84%) 563(84%) 791(119%)
A-pillar airbag 375(56%) 338(58%) 661(99%)
AEB system and
A-pillar airbag
324(49%) 333(50%) 560(84%)
Methodology (Great Britain version) integrated score rating (million pounds
sterling)
No system 889(99%) 895(100%) £1,255(140%)
AEB system
representative of
current systems
753(84%) 758(85%) £1,060(118%)
A-pillar airbag 471(53%) 482(54%) 845(94%)
AEB system and
A-pillar airbag
405(45%) 413(46%) 718(80%)
version and Great Britain pounds for the Great Britain version
because the models were calibrated using the results of the
benefit analyses for the respective countries (see Methodology
section). At the time of writing, there were approximately 1.28
euros to the pound.
The results for the composite vehicles with constant wind-
screen area are shown in Table 3.
Discussion
Approach
In this study, a methodology was developed to assess a car’s
pedestrian protection based on harm estimated from AIS.
However, other injury and cost metrics exist that could pos-
sibly have been used. For example, threat to life measured by
the Injury Severity Scale, quality of life year losses, long-term
consequences measured by permanent medical impairment,
or socioeconomic cost, or combinations of them. There was
no obvious choice, so the harm method was chosen on the
basis that it was one of the simplest and data to implement
it were readily available. It should be noted that there is little
doubt that how the injury outcome is measured will influence
the result. Tingvall et al. (2013) have shown that the group of
road users suffering most injuries depends to a great extent
on the injury measure used. For example, passenger car occu-
pants are the majority of Maximum Abbreviated Injury Score
Downloaded by [TRL Ltd] at 07:09 08 June 2015

8Edwards et al.
Table 3. Assessment results in terms of total casualty cost for
constant windscreen area composite cars with good, average,
and poor Euro NCAP passive safety rating with no system,
with AEB system, with airbag, and with both AEB and airbag
fitted. Percentages show costs normalized to average passive
safety performance with no AEB system fitted. Note that Euro
NCAP scores increase for better protection, whereas methodol-
ogy assessment costs decrease
Passive safety level
Additional safety
system Good Average Poor
Euro NCAP passive safety score rating
No system 32.2(142%) 22.6(100%) 12.2(54%)
A-pillar airbag 34.0(150%) 24.4(108%) 14.0(62%)
Methodology (German version) integrated score rating (million euros)
No system 545(82%) 667(100%) 884(132%)
AEB system
representative of
current systems
461(69%) 563(84%) 742(111%)
A-pillar airbag 265(40%) 388(58%) 604(90%)
AEB system and
A-pillar airbag
231(35%) 333(50%) 512(77%)
Methodology (Great Britain version) integrated score rating (million pounds
sterling)
No system 736(82%) 895(100%) 1,181(132%)
AEB system
representative of
current systems
625(70%) 758(85%) 997(111%)
A-pillar airbag 322(36%) 481(54%) 767(86%)
AEB system and
A-pillar airbag
280(31%) 413(46%) 653(73%)
(MAIS) 3+injured road users, whereas bicyclists dominate
MAIS 2+injured road users.
To illustrate what different approaches other than harm
may give, the relative rating of injury outcome for the harm
approach used and an approach using risk of permanent med-
ical impairment (RPMI) were compared for single AIS in-
juries to the head and leg body regions. RPMI is a measure
for long-term consequences of injury that has been used in
Swedish studies of pedestrian protection (Fredriksson et al.
2007; Strandroth et al. 2011). It is based on Swedish insurance
data available for Swedish car occupants (Malm et al. 2008).
It was found that using RPMI affects the relative ratings
of AIS level injuries substantially, in particular for the head
(Table A6, see online supplement). It increased the rating
(cost/impairment) of an AIS 4 injury compared to an AIS
5 injury and decreased the rating of an AIS 2 injury com-
pared to an AIS 3 injury. Which rating is best is unknown,
although both have shortcomings. However, the harm cost
rating was chosen because sufficient information was avail-
able for harm to differentiate between the upper and lower leg
body regions and to calibrate against an independent benefit
estimate, whereas for RPMI it was not. Further work is rec-
ommended to investigate different injury and cost metrics in
the future.
Assessment of Passive Safety
Examination of the assessment results for the composite vehi-
cles for “no AEB system” (Table 2) shows that the AsPeCSS
assessment of the good, average, and poor performing vehi-
cles’ passive safety performance aligns in terms of order with
the Euro NCAP score rating but does not align in terms of
scale. Specifically, the AsPeCSS assessment shows a large dif-
ference in rating between the poor and average vehicles and a
small difference between average and good vehicles, whereas
the Euro NCAP scores show large differences between both
the poor and average vehicles and the average and good vehi-
cles.
However, examination of the assessment results for the
composite vehicles with constant windscreen area for no AEB
system (Table 3) shows that the AsPeCSS assessment of the
good, average, and poor performing vehicles’ passive safety
performance aligns in terms of order with the Euro NCAP
score rating and better in terms of scale; that is, there is a
closer difference in the assessment (cost) between poor and
average and average and good vehicles.
Head injury costs were the main cause of the different as-
sessment results for the composite vehicles, both with variable
and constant windscreen area, because they form about 80%
of the total costs. Further investigation found that the main
reasons for the differences between the head impact assess-
ments for the poor, average, and good composite vehicles with
variable and constant windscreen area were as follows:
•A different portion of windscreen (default green) and A-
pillar (default red) in the assessment area that varied be-
tween the poor, average, and good composite vehicles with
variable windscreen area but was the same for the compos-
ite vehicles with constant windscreen area, which can be
seen by examining Figure A6 (see online supplement).
•The difference between the Euro NCAP and AsPeCSS as-
sessments for severe head injury and the effective weighting
of this area in the AsPeCSS assessment to account for the
probability of the head strike occurring there, compared to
no weighting for the Euro NCAP assessment.
The difference between the Euro NCAP and AsPeCSS as-
sessments for severe head injury is illustrated in Table 4. This
table shows head assessment results for hypothetical average
composite vehicles with a constant HIC value over all the as-
sessment area with values representing green (good), yellow,
orange (average), brown, red (poor), and default red vehicles.
It can be seen that the relative differences between colors are
similar for the Euro NCAP and AsPeCSS assessments for
green to red rated vehicles but are quite different between red
and default red for values of high HIC where the Euro NCAP
assessment score is constant but the AsPeCSS assessment cost
increases substantially. This is caused by the injury risk curves
for head injury (Figure 3), which show a substantial increase
in risk for severe head injury (AIS 4+,AIS5+) at HIC values
above 1,800 and a large change from red (assumed to be HIC
1,800) to default red (assumed to be HIC 6,000). The result of
this is that variations in the amount of A-pillar in the assess-
ment area changes the AsPeCSS assessment much more than
the Euro NCAP assessment.
Due to the importance of the A-pillar area, it comes as no
surprise that a hypothetical airbag reduced overall casualty
cost (Tables 2 and 3). The percentage effect of an A-pillar
Downloaded by [TRL Ltd] at 07:09 08 June 2015

Passive Safety Components 9
Table 4. Comparison of Euro NCAP and AsPeCSS assessments
of head injury
Head Good Average Poor Default
Assessment (green) Yellow (orange) Brown (red) red
HIC 600 900 1,200 1,500 1,800 6,000a
Euro NCAP score 24 18 12 6 0 0
AsPeCSS assessment injury
cost (million euros)
106 203 318 448 599 2,379
Percentage of avearge
AsPeCSS cost
33 64 100 141 188 748
aA value of 6,000 was chosen based on A-pillar test results in Fredriksson
and Ros´
en (2014).
airbag (approximately 45% casualty cost reduction) is larger
in the proposed AsPeCSS assessment compared to the Euro
NCAP assessment (5 to 8% score increase for the cars in Ta-
bles 2 and 3), depending on base score and number of A-pillar
test points in the rating area. One should keep in mind, though,
that the hypothetical airbag was assumed to deploy in all col-
lisions in the specified speed range and that side effects such as
restricted visibility for the driver after deployment may reduce
the actual safety benefit in the field. The benefit of a hypothet-
ical airbag given in Tables 2 and 3 is rather more a potential
than field benefit but nevertheless illustrates how the AsPeCSS
method can be used to assess such systems.
The differences caused by changes in the amount of wind-
screen and A-pillar in the assessment area were emphasized
by the difference in the weighting of this area between the
Euro NCAP and AsPeCSS assessments. In the Euro NCAP
assessment there is no weighting of this area (i.e., all head-
form test points are equally important). However, in the As-
PeCSS assessment this area is effectively weighted because of
the impact probability distribution used (Figure 4). If the As-
PeCSS methodology impact probability distribution curves
are considered in conjunction with the impact speed distri-
bution curves (Figures 1 and 2), it can be seen that a large
proportion of fatal and serious injuries occur at speeds greater
than 30 km/h, and that at these speeds areas with a WAD dis-
tance of 1,800 mm or greater are more likely to be impacted.
The outcome of this is that a change in the protection offered
in these areas influences the AsPeCSS assessment considerably
more than the Euro NCAP assessment.
Assessment of Active Safety and Combined Effects
If the assessment results in Tables 2 and 3 for the composite
vehicles with variable and constant windscreen areas are ex-
amined it can be seen that the addition of an AEB system that
has a performance representative of current systems, in terms
of the assessment, is broadly equivalent to increasing passive
safety from poor to average or average to good. This is an
increase in the Euro NCAP rating of 2 color bands and is in
alignment with the previous work of Ros´
en et al. (2010), who
predicted a high effectiveness for pedestrian AEB systems.
However, it is greater than the benefit predicted by Searson
et al. (2014), who estimated that fitment of a pedestrian AEB
system would improve a car’s Euro NCAP performance rating
by approximately one color band only.
Fig. 4. Impact probability distribution with WAD and impact
speed used in assessment methodology.
The estimated benefits of the hypothetical A-pillar airbag
(40 to 50% casualty cost reduction) exceeded the benefits of
an AEB system (10 to 20% casualty cost reduction) but re-
mained in a similar order of magnitude. The estimated benefit
of A-pillar airbags seems rather high and the benefit of AEB
rather low compared to Fredriksson and Ros´
en (2014), who
estimated 20 to 30% reduction of severe AIS 3+head injuries
for A-pillar and windscreen base airbags and 10 to 50% for
AEB systems. Possible explanations for these differences are
as follows:
•Fredriksson and Ros´
en express the benefit in terms of re-
duction of AIS 3+head injuries, whereas the AsPeCSS
methodology expresses the benefit in terms of cost reduc-
tion. Cost reduction takes into account the higher costs
of AIS 4 and AIS 5 injuries compared to AIS 3 injuries,
whereas quantifying AIS 3+injury does not. A-pillar
airbags are likely to reduce these higher severity injuries
to a greater extent than AEB systems, therefore helping to
cause the differences in the results seen.
•Compared to Fredriksson and Ros´
en, for the AsPeCSS as-
sessment, the rather optimistic assumption for the A-pillar
airbag was always for deployment in the specified speed
range, whereas the AEB system was rather pessimistically
assumed to give no benefit in some unclassified accident
scenarios (20% of all cases).
•The AsPeCSS assessment calculates the impact probability
for the head for each WAD and divides this probability
by the number of lateral test points for each individual
WAD to calculate the impact probability for each test point.
As seen in Figure A6, the highest WAD has only few test
points because of the shape of the car and the marking out
procedure. This overemphasizes the effect of the A-pillar
at this WAD in the AsPeCSS assessment. This is because
Downloaded by [TRL Ltd] at 07:09 08 June 2015

10 Edwards et al.
effectively the few test points are taken to be representative
of the full width of the car and the windscreen area between
these points, which would likely be default green, is not
taken into account.
Limitations
The major limitation within the methodology is the assump-
tion used implicitly during calibration because a simple mul-
tiplication factor is used. This is that the cost of casualty
injuries to body areas, such as the thorax, not assessed by the
impactors (headform, legform, and upper legform), and other
casualty injuries, such as those caused by ground impact, are
related linearly to casualty injuries assessed by the impactors;
that is, head, upper leg, and lower leg injuries.
However, examination of the calibration results indicates
that this assumption may be valid or may not overly affect
the assessments made with the methodology. Specifically, the
calibration factors developed using the different AEB systems
are very similar with a maximum variation of less than 2%
from the average for Great Britain and Germany. This is an
indication that relationships are approximately linear because
if they were not the calibration factor would likely change
more.
Other limitations include the following:
•Accuracy of impactor criteria to speed scaling relationships
and disregarding of bottoming out.
•Validity and accuracy of injury risk curves.
•Validity and accuracy of WAD relationship with speed and
pedestrian height for head impact. The vehicles simulated
by Mottola et al. (2013) were deemed representative of the
current EU fleet but not necessarily of the future fleet.
The relation between pedestrian height, impact speed, and
WAD is deterministic for the proposed method, and non-
negligible variation was observed in simulation (Mottola
et al. 2013) and accident data (Fredriksson and Ros´
en 2012;
Kiuchi et al. 2014).
•Validity and accuracy of Euro NCAP assessment results
for a car representative of cars within accident data used
for calibration. This is because of the large number of as-
sumptions made to derive these data.
•There was no account taken of the effect of vehicle pitching
when braking.
•How well the test scenarios represent the accident scenarios
that are mapped to them. For example, at present because
only basic test scenarios have been developed, accident sce-
narios such as “crossing a straight road from near-side, no
obstruction” that occur in daylight and night street condi-
tions is mapped as a whole to a test scenario that is con-
ducted in daylight conditions. If the performance of the
AEB system is dependent on the lighting conditions, the
current methodology will not show these differences.
A method to estimate the overall benefit of active and pas-
sive safety pedestrian protection was developed and success-
fully tested. Using 2 calibration steps, it was ensured that
the assessment reflects the body region injury distribution
observed in the accident data and that the indicated AEB bene-
fit equals an independent estimate of this benefit. The method
has thereby advanced integrated assessment in order to en-
courage and spread best possible overall pedestrian protection
and is ready for use in further assessments. The indication that
benefits for safety systems are of the same order of magnitude
as predicted by previous research is encouraging. However,
limitations exist and it remains to be seen in retrospective ac-
cident studies whether the proposed method correlates better
with observed injury outcome than other assessment schemes.
Acknowledgments
The authors acknowledge the UK Department for Transport
for permitting the use of the OTS (On-The-Spot) accident
study. The OTS data forms part of the Road Accident In
Depth Studies database; further information can be found at
https://www.gov.uk/government/publications/road-accid-
ent-investigation-road-accident-in-depth-studies.
Funding
The authors gratefully acknowledge the support of the Euro-
pean Commission and the German Federal Ministry of Trans-
port, Building and Urban Development and thank other AS-
PECSS project partners for their input into this work.
Supplemental Materials
Supplemental data for this article can be accessed on the
publisher’s website.
References
ASPECSS FP7 project website 2014. Available at: http://www.aspecss-
project.eu. Accessed May 2014.
Barrow A, Reeves C, Carroll J, et al. Analysis of pedestrian accident leg
contacts and distribution of contact points across the vehicle front.
Paper presented at: 6th International Expert Symposium on Accident
Research (ESAR); June 20–21, 2014; Hannover, Germany.
Department of Trade and Industry, UK. The Handbook of Adult Anthro-
pometric and Strength Measurements—Data for Design Safety. Not-
tingham, UK: Institute for Occupational Ergonomics, University of
Nottingham; 1996. Available at: http://www.openerg.com. Accessed
July 2014.
Edwards M, Nathanson A, Wisch M. Estimate of potential benefit for
Europe of fitting autonomous emergency braking (AEB) systems for
pedestrian protection to cars. Traffic Inj Prev. 2014;15:173–182.
EEVC WG17. Improved test methods to evaluate pedestrian protection
afforded by passenger cars. 2002. Available at: http://www.unece.
org/fileadmin/DAM/trans/doc/2006/wp29grsp/ps-187r1e.pdf. Ac-
cessed October 2014.
Euro NCAP. 2020 ROADMAP, Euro NCAP. 2014. Available at: http://
www.euroncap.com/files/Euro-NCAP-2020-Roadmap—June-2014-
2—0-e11c0984-af94-420e-9d63-63edc8538745.pdf. Accessed July
2014.
Fredriksson R, Rosen E. Integrated pedestrian countermeasures—
potential of head injury reduction combining passive and active coun-
termeasures. Saf Sci. 2012;50:400–407.
Fredriksson R, Ros´
en E. Head injury reduction potential of integrated
pedestrian protection systems based on accident and experimental
Downloaded by [TRL Ltd] at 07:09 08 June 2015

Passive Safety Components 11
data—benefit of combining passive and active systems. Paper pre-
sented at: IRCOBI 2014 Conference; September 2014. Available at:
http://www.ircobi.org/downloads/irc14/default.htm. Accessed Oc-
tober 2014.
Fredriksson R, Zhang L, Bostr¨
om O, Yang K. Influence of im-
pact speed on head and brain injury outcome in vulnerable
road user impacts to the car hood. Stapp Car Crash J. 2007;51:
155–167.
Hamacher M, Eckstein L, Kuhn M, Hummel T. Assessment of active
and passive technical measures for pedestrian protection at the vehicle
front. Paper presented at: 22nd ESV Conference; 2011; Washington,
DC.
Hutchinson T, Anderson R, Searson D. Pedestrian headform testing:
inferring performance at impact speeds and for headform masses not
tested, and estimating average performance in a range of real-world
conditions. Traffic Inj Prev. 2012;13:402–411.
Kiuchi T, Lubbe N, Otte D, Chen Q. Comparative study of VRU head
impact locations. Paper presented at: 6th International Expert Sym-
posium on Accident Research (ESAR); June 20–21, 2014; Hannover,
Germany.
Lawrence GJL, Hardy BJ, Carroll JA, Donaldson WMS, Visvikis C,
Peel DA. A Study on the Feasibility of Measures Relating to the Pro-
tection of Pedestrians and Other Vulnerable Road Users —Final 2006.
TRL Limited Unpublished Project Report UPR/VE/045/06
under Contract No. ENTR/05/17.01 to the European
Commission. Retrieved from: http://ec.europa.eu/enterprise/sectors
/automotive/files/projects/report pedestrian trl 2006 en.pdf. Acce-
ssed: July 2014.
Lubbe N, Edwards M, Wisch M. Towards an integrated pedestrian
safety assessment method. Paper presented at: IRCOBI 2012 Con-
ference; 2012. Available at: http://www.ircobi.org/downloads/irc12/
default.htm. Accessed July 2014.
Malm S, Krafft M, Kullgren A, Ydenius A, Tingvall C. Risk of perma-
nent medical impairment (RPMI) in road traffic accidents. Ann Adv
Automot Med. 2008;52:293–100.
Matsui Y. New injury reference values determined for TRL legform
impactor from accident reconstruction test. Int J Crashworthiness.
2003;8(2):179–188.
Matsui Y. Proposal of injury risk curves for evaluating pedestrian head
injury risk using headform impactor based on accident reconstruc-
tion [in Japanese with English abstract]. JSAE Trans. 2004;35(4):
221–228.
Matsui Y, Ishikawa H, Sasaki A. Validation of pedestrian upper legform
impact tests—reconstruction of pedestrian accidents. Paper presented
at: 16th Enhanced Safety of Vehicles Conference; 1998.
Mottola E, Rodarius C, Schaub S. Pedestrian kinematics and specifi-
cations of new impact conditions for head- and leg-form impactors.
2013. Available at: http://www.aspecss-project.eu/downloads./ Ac-
cessed July 2014.
NHTSA. Final Economic Assessment, FMVSS No. 201, Upper Inte-
rior Head Protection. Docket No. NHTSA-1996-1762. 1995. Avail-
able at: http://www.regulations.gov/#!documentDetail;D=NHTSA-
1996-1762-0003. Accessed July 2014.
Prasad P, Mertz HJ. The Position of the US Delegation to the ISO Working
Group 6 on the Use of HIC in the Automotive Environment. Warrington,
PA: SAE; 1985. SAE Paper No. 851246.
Rodmell C, Lawrence GJL. Comparison Between Dose–Response and
Cumulative Methods of Injury Risk Analysis and Implications on the
JARI Injury Risk Analysis. Workingham, UK: TRL Limited; 1998.
Ros ´
en E, Kallhammer J, Eriksson D, Nentwich M, Fedriksson R, Smith
K. Pedestrian injury mitigation by autonomous braking. Accid Anal
Prev. 2010;42:1949–1957.
Schmitt KU, Niederer P, Walz F. Trauma Biomechanics Introduction to
Accidental Injury. Berlin, Germany: Springer-Verlag; 2004.
Searson, D, Anderson, R, Hutchinson T. Integrated assessment of pedes-
trian head impact protection in testing secondary safety and au-
tonomous emergency braking. Accid Anal Prev. 2014;63:1–8.
Searson D, Anderson R, Ponte G, van den Berg A. Headform im-
pact test performance of vehicles under the GTR on pedestrian
safety. 2009. Available at: http://casr.adelaide.edu.au/publications/
researchreports/casr072.pdf. Accessed July 2014.
Seiniger P, Bartels O, Kunert M, Schaller T. Preventive Pedes-
trian Protection Test Procedures and Results. 2014. Available at:
http://www.aspecss-project.eu/downloads/. Accessed July 2014.
Strandroth J, Rizzi M, Sternlund S, Lie A, Tingvall C. The correlation
between pedestrian injury severity in real-life crashes and Euro NCAP
pedestrian test results. Traffic Inj Prev. 2011;12:604–613.
Takahashi Y, Matsuoka F, Okuyama H, Imaizumi I. Development of in-
jury probability functions for the flexible pedestrian legform impactor.
SAE Int J Passenger Cars Mech Syst. 2012;5(1):242–252.
Tingvall C, Ifver J, Krafft M, et al. The consequences of adopting
a MAIS 3 injury target for road safety in the EU: a compari-
son with targets based on fatalities and long-term consequences.
Paper presented at: IRCOBI 2013 Conference; 2013. Available at:
http://www.ircobi.org/downloads/irc13/default.htm. Accessed De-
cember 2014.
Wisch M, Seiniger P, Pastor C, Edwards M, Visvikis C, Reeves C. Sce-
narios and weighting factors for pre-crash assessment of integrated
pedestrian safety systems. 2013. Available at: http://www.aspecss-
project.eu/downloads./ Accessed July 2014.
Zaloshnja E, Miller T, Romano E, Spicer R. Crash costs by body part
injured, fracture involvement, and threat to life severity, United States,
2000. Accid Anal Prev. 2004;36:415–427.
Zander O. Derivation of draft FlePLI prototype impactor limits. 6th
Meeting of Informal Group GTR9 Phase 2, Document GTR9-06-
08r1, March 2012. Retrieved from: https://www2.unece.org/wiki/
download/attachments/5801858/GTR9-6-08-Rev1e.pdf?api=v2.
Accessed July 2014.
Zander O, Gehring D, Leßmann P. Improved assessment methods of
lower extremity injuries in vehicle-to-pedestrian accidents using im-
pactor tests and full scale dummy tests. Paper presented at: 22nd ESV
Conference; 2011; Washington, DC.
Downloaded by [TRL Ltd] at 07:09 08 June 2015