Archives of Computational Methods in Engineering (2023) 30:4687–4709
https://doi.org/10.1007/s11831-023-09949-2
REVIEW ARTICLE
Numerical Approaches to Pedestrian Impact Simulation with Human
Body Models: A Review
Daniel Wdowicz1,2
· Mariusz Ptak1
Received: 19 March 2023 / Accepted: 25 May 2023 / Published online: 23 June 2023
© The Author(s) 2023
Abstract
The review paper discusses numerical human body models of pedestrians. The background of current trends in physical
and mathematical pedestrian research is presented. Development, validation and areas of application of pedestrian body
models are described. The differences between multibody models and finite element models are presented. Accident-based
and experimental approaches to validation of the models are discussed. As a novelty, this paper presents an overview of
multibody models used in forensic investigations, discusses their usefulness, and differences between their design and the
design of more advanced multibody and finite element models. Finally, the most recent trends in human body modelling are
discussed, including open-source approaches to model distribution and replacement of physical tests by digital simulations.
1 Introduction
Despite the available technology and engineering enhancements in fields of preventive, passive and acitve safety, the
problem of human lives being lost in pedestrian accidents
remains present. 4763 pedestrians died on the roads of the
European Union in the year 2018 and pedestrian fatalities in
Europe in general had consistently accounted for approximately 20% of all road fatalities in the 2010–2018 period
[114]. While UK data from the COVID-19 lockdown period
of 2020 showed a temporary decrease in the number of
pedestrian fatalities [27], the numbers after the lockdown
period remain high. In 2021, 343 pedestrians lost their lives
in Germany [159], 414 in France [116], and 527 in Poland
[70]. In the USA, there were 7485 pedestrian fatalities in
2021 [135]; this was a 54% increase in the span of a decade
from 2010 to 2020; meanwhile, in the same period, total
count of all other traffic deaths increased only by 13%.
Despite new legislative efforts and technical improvements,
* Daniel Wdowicz
daniel.wdowicz@pwr.edu.pl
Mariusz Ptak
mariusz.ptak@pwr.edu.pl
1
Faculty of Mechanical Engineering, Wroclaw University
of Science and Technology, Lukasiewicza 7/9,
50‑371 Wrocław, Poland
2
CYBID sp. z o.o. sp. k., Kuznicy Kollatajowskiej 15c/L2,
31‑234 Kraków, Poland
government, industry and research still face the problem of
pedestrian accidents taking a heavy toll on even the most
developed countries.
An approach to tackling this problem is by thorough
understanding the physical process of the vehicle-pedestrian
impact itself. There are multiple parties who have a need of
an in-depth analysis of the mechanics of vehicle–pedestrian
accidents:
• Individual forensic experts help courts and insurance
•
•
•
•
companies reconstruct pedestrian accidents to determine
the culpability in individual cases;
multidisciplinary teams reconstruct accidents for purposes of state initiatives such as GIDAS [118] or CIDAS
[17]; this knowledge is then shared with policymakers or
the industrial sector;
authorities and consumer organizations design test procedures for assessment of a vehicle’s passive safety, specifically in terms of pedestrian collisions;
automotive industry needs tools to determine whether its
products conform to requirements of normative acts and
consumer testing protocols;
researchers and engineers study factors influencing vehicle-pedestrian impact (crashworthiness studies), propose
changes to vehicles, or design additional safety equipment.
These applications require reliable, biofidelic (i.e. comparable in their mechanical response to the natural properties of
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D. Wdowicz, M. Ptak
biological organisms) mechanical surrogates of a pedestrian
body capable of being analysed in various configurations.
1.1 Mechanics of Pedestrian Impact
A study by [133] showed that, in accidents, 80% of pedestrians are impacted with the front-end or front corners of a
vehicle. For the frontal pedestrian impact configurations,
five patterns of post-impact pedestrian kinematics were recognized by [133]: wrap projection, forward projection, somersault, roof vault, fender vault (see: Table 1). An example of
a wrap projection impact, simulated using a CTS PRIMUS
dummy (175 cm, 78 kg) [144], is demonstrated in Fig. 1.
Other distinct configurations are: dragging of the pedestrian
under the vehicle’s chassis [34]; sideswipe collisions, when
an impact to the side of the vehicle causes a rotation of the
pedestrian’s body [156].
The mechanical process of vehicle-to-pedestrian impact
can be separated into three general phases:
• contact phase—between the first impact of the pedestrian
(usually of their lower extremities), and the separation of
the pedestrian from a vehicle
• flight phase—between the moment of separation from the
vehicle and the moment of the first ground contact
• sliding phase—between the first ground contact, through
bouncing, sliding or tumbling of the pedestrian, until
coming to full rest
Some researchers distinguish another phase, the transport
phase, between the contact and flight phases [1]. The transport phase takes into account the time when the pedestrian
has already impacted the vehicle, but remains carried on
the vehicle, and the duration of the phase depends on the
intensity of vehicle’s braking.
The throw distance, a.k.a. projection distance, is defined
as the distance between the initial vehicle-pedestrian impact
and the final rest position of the pedestrian [11]. The most
common subject of analysis is the longitudinal throw distance, i.e. measured along the longitudinal direction of the
vehicle motion. Multiple factors influence the throw distance, including vehicle shape, braking intensity or the position of the pedestrian relative to the front end of the vehicle.
During the contact phase, two major impacts of the
pedestrian with the vehicle are usually distinguished [198]:
primary impact, between the pedestrian’s lower extremities
Fig. 1 Time sequence in [ms] of a 50 km/h collision of a small family car with a pedestrian surrogate, a CTS Primus dummy—a wrap projection
configuration [25]
Table 1 Frequency of occurrence and description of pedestrian post-impact kinematics patterns
Configuration
Frequency
(%)a
Description
Wrap Projection
45.2
Impact below pedestrian’s center of mass; pedestrian’s body wraps around the bonnet; chest/shoulder impact
followed by head impact; after the head impact, the body separates from the vehicle
Impact with a flat surface (box-shaped vehicle body), above pedestrian’s center of mass; limited rotation of the
body, higher longitudinal velocity; final rest position is in front of the vehicle
Impact with the vehicle’s corner, body wraps around the fender; the final rest position of the body is to the side
or behind the vehicle
Usually occurs in higher impact speeds (> 60 km/h). Pedestrian slides or passes over the hood, the windshield,
and the roof; contact with the vehicle’s trunk is possible
Similar to wrap projection; More pronounced rotation, body flips in the air before contacting the road surface
Forward Projection 34.4
Fender Vault
13.3
Roof Vault
5.4
Somersault
1.7
a
Frequency relative to the total number of frontal accident configurations analysed by [133]
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Numerical Approaches to Pedestrian Impact Simulation with Human Body Models: A Review
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and the front of the vehicle, and secondary impact, when
the head strikes the bonnet or the windshield. Between the
primary and secondary impact, other impacts such as shoulder or chest impact may occur as well. Time offset between
primary and secondary impact (see: Fig. 1) is dependent on
the impact configuration, and it is shorter for forward projection cases than for wrap projection cases [34].
on mathematical modelling techniques, which can both
provide quantitative results and cover multiple scenarios
cost-effectively.
There are different approaches to the mathematical estimation of pedestrian kinematics:
1.2 Pedestrian Accident Reconstruction
• empirical models, described by single-argument regres-
The aim of pedestrian-vehicle accident reconstruction is usually to assess the velocity of the vehicle impact, determine
the location of the impact and to determine the pedestrian’s
position in relation to the vehicle [33]. The analysis of evidence (e.g., pedestrian injuries, bloodstains, vehicle damage,
paint flakes in clothes) yields general qualitative information on the course of an accident [113, 169]. However, in
practise of accident reconstruction, there is often a need for
more quantitative conclusions, e.g. regarding the vehicle’s
collision velocity. Generally, it is not recommended to assess
collision velocity directly from vehicle damage or severity
of pedestrian injuries [42]. Event data recorders (EDR) pose
to be a promising source of objective information on vehicle
collision velocity. However, the accuracy of reconstruction
of vehicle’s trajectory and velocity using various EDR models has been shown to sometimes be unsatisfactory [40].
Moreover, while the recently approved Regulation 160 of
UN ECE, with the 01 series of amendments, imposes certification of EDR present on board of new vehicles, events
with vulnerable road users (pedestrians in particular) are not
mandatory to be recorded by every vehicle’s EDR. From a
physical point of view, the disproportion of mass between a
pedestrian and a vehicle results in a low change in vehicle
velocity in the case of an impact [35], and thus pedestrian
impacts may sometimes not be registered at all. Also, as
stated in [88], only 15–20% of vehicles on European roads
are equipped with EDR.
In view of the above, recreating pedestrian accidents
by using mechanical simulations presents to be a valid
approach to reconstruction. Mechanical surrogates for a
pedestrian body are required, but it is naturally impossible to recreate accidents using the actual bodies of the
participants. Thus, for instance, pedestrian accidents can
be simulated by crash tests with dummies. Pedestrian test
dummies, particularly the recent designs [144], have a
kinematic response comparable to that of a human pedestrian in terms of pedestrian final rest position and location
of damage on the vehicle. The caveat is, crash tests are
costly and a typical forensic investigation rarely economically warrants more than a couple of crash tests, if any
at all. This limits the number of crash test scenarios that
can be analyzed for each investigated case. Thus, pedestrian accident reconstruction is currently largely based
• projectile models, where a pedestrian is assumed to be
a point mass [146];
sion equations, which are based on statistical approximation of results of dummy experiments or real accident analyses [42, 132, 160];
• two- and three-segment 2D analytical models, which
assume the pedestrian body to be made of stiff segments connected with a hinge joint [156, 197];
• numerical models of an entire human body.
Projectile and empirical models are the easiest to use, as
they are described by easily solvable, one-variable equations. The equations usually describe the relationship
between impact velocity and pedestrian throw distance.
Projectile and empirical models provide no detailed
information on the course of the impact (e.g. location of
head-to-windscreen impact) or the pedestrian’s injuries.
Application of projectile and empirical models is limited
to central collisions. These models cannot be applied to
the analysis of cases where a pedestrian is impacted by a
vehicle’s corner, i.e. when pedestrian exhibits fender vault
type of movement [42]. Furthermore, most empirical models were developed in the last century, and further study of
their applicability in collisions with newer vehicle models
may be required [21]. Two- and three-segment models,
while they correspond well with experimental data [156],
are limited to 2D cases and have found limited application
in the practice of accident reconstruction.
Given the limitations of other mathematical techniques,
numerical human body modelling has found widespread
use in pedestrian accident reconstruction. Numerical models can be used to study impacts in various conditions and,
from a practical point of view, they are a visually attractive
way of presenting the results of a forensic investigation.
General description, validation and application of human
body models, created using multibody techniques and dedicated specifically for pedestrian accident reconstruction,
are described further in Chapters 2, 4, 5.
1.3 Vehicle Certification
Years of research in the area of vehicle passive safety have
led to the introduction of normative acts and regulations
defining requirements for the protection of vulnerable road
users (VRUs), including pedestrians as the most susceptible
group.
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Many advances in this field have been made by the International Harmonized Research Activities (IHRA) Pedestrian
Safety Working Group. Through worldwide research—
including accident data analysis and the development of
numerical pedestrian models—unified test procedures for
pedestrian safety have been created.
Nowadays, there is Regulation (EU) 2019/2144 of the
European Parliament and of the Council in force which sets
type-approval requirements for motor vehicles and their
trailers, and systems, components, and separate technical
units intended for such vehicles, as regards their general
safety and the protection of vehicle occupants and vulnerable
road users, amending Regulation (EC) 78/2009. Currently,
UN Regulation No 127, also applies in the European Union
in respect to type-approval of motor vehicles concerning the
approval of motor vehicles with regard to their pedestrian
safety performance.
Remarkably, unlike occupant safety tests, vulnerable road
user safety certification procedures of the above-mentioned
Regulations do not involve usage of full-scale test dummies.
The vehicle front-end is tested by means of impactor tests
( Fig. 2) at a fixed velocity and a fixed impact angle. This
allows the tests to be normalised and reduces the costs of the
certification procedure, but at the expense of not being able
to recreate the complexity of full-scale pedestrian body kinematics (see: Fig. 1). The vehicle manufacturers, however,
tend to aim above the basic requirements of Regulations
and use (or even develop themselves) numerical mechanical surrogates of full-scale pedestrians. This allows them to
test their vehicles in a vast array of configurations, without
a need for costly physical crash testing.
1.4 Motivation
While the current state of the art in relation to human body
models (HBMs) in the field of biomechanics of impact
Fig. 2 Visualisation of a vehicle-to-pedestrian accident and the use of
numerical impactors that represent critical parts of the human body:
A a headform, B upper legform C lower legform
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is often studied [191, 208], there have been few review
attempts focusing strictly on validation and application of
pedestrian models [60, 79, 99, 156]. Interestingly, to the
knowledge of the authors of this paper, there has been no
review of HBMs implemented in dedicated commercial road
accident reconstruction programmes yet.
The aim of this review study is to present historical and
current numerical models used for simulating pedestrian
impact. Additional focus of the study is similarities and
differences in areas of validation and application between
models designed primarily for accident reconstruction and
advanced models which are used in detailed studies and
industrial applications.
2 Multibody Models
The most common approach to creating computational
pedestrian models is multibody (MB) modelling. The principle behind this approach is to model mechanical systems
as kinematic chains consisting of rigid, undeformable bodies connected by kinematic pairs (also known as kinematic
constraints or joints). The motion of such bodies is computed
using the Newton-Euler equations.
2.1 Historical Models
The history of MB solvers utilized for vehicle impact simulation dates as far back as the 1960s [94], when the first
numerical vehicle occupant simulations were created. One
of the first reviews of these packages and models was done
by [64]. Multiple iterations of programming packages, often
referred to as Crash Victim Simulation (CVS) packages,
were developed in the 1960s and 1970s, including CAL-2D
[94, 95], MVMA-2D by HRSI/UMTRI [137], or their 3D
iterations [7]. Nowadays, access to many reports and user
manuals from those times is limited, yet it is clear that those
early programmes laid the foundation for modern MB solvers used for impact modelling, e.g. MADYMO [89]. The
primary programming language used for most of these solvers was FORTRAN.
An overview of the earliest MB pedestrian models was
provided by [196]. The models had been developed by
many parties using different programmes. The examples
are: PRAKIMOD model by Renault/Peugeot [74, 75] and a
pedestrian model by Citroen [24]; both models were based
on the MVMA program. Some of the early pedestrian models were 2D models. The earliest representations of vehicles
used for pedestrian impact included either 2D-straight line
segments, 3D planes or 3D hyperellipsoids.
Numerical Approaches to Pedestrian Impact Simulation with Human Body Models: A Review
2.2 Commercial Multibody Models
There are multiple commercial packages equipped with predefined MB pedestrian models. Visualisation and a short
description of a selection of those models are provided,
respectively, in Tables 2 and 3.
MADYMO (currently branded as Simcenter MADYMO)
is a popular multibody solver and software package used
mainly in the automotive industry. Since its introduction by
the Research Institute for Road Vehicles TNO, Netherlands
[89, 178], it has been continuously developed, in chronological order, by TNO, Tass International B.V. and from 2017 by
Siemens Industry Software and Services B.V.
Two main types of MB pedestrian models are available
in the commercial MADYMO distribution. One is the ellipsoidal model [167, 177]. The first version of this model was
developed by TNO in partial cooperation with Chalmers
Table 2 Examples of multibody pedestrian models available in commercial software
MADYMO
[177]
PC-CRASH Virtual CRASH
[107]
[98]
CYBID V-SIM
[13]
Source [13, 107, 167, 183]
Table 3 Description of
a selection of multibody
pedestrian models available in
commercial software
No. of Segments
No. of Joints
Scalable
Type of Joint Motion
Restraint Characteristic
Geometries per Body
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University of Technology [73]. The family of models by
[177] represents adult specimens of the Western European
population as of 1984 between 8 and 70 years of age: 5th
female, 50th male, 95th male. A family of 3- and 6-year-old
ellipsoid child pedestrian models is also available, with their
dimensions based on the Q-Series child dummy. In addition to the models of a predefined size, a native MADYMO
Scaler [41] can scale the default 50th percentile adult male
model to a given sex, stature or weight, including that of a
child.
MADYMO model library also has a second type of
pedestrian model, a 50th percentile male facet model [167].
Similarly to the ellipsoidal model, the facet model has MB
rigid bodies, but its geometry is represented by a rigid mesh
of facet surfaces covering rigid bodies, with contact forces
calculated using the standard MB solver. The facet models
share an identical geometry to MADYMO active human
model [96, 167] but are not equipped with any controllers.
The facet model is not scalable.
MADYMO active human multibody model [167] can also
technically be used for pedestrian simulations. It is equipped
with controllers that allow modelling of active behaviour of
human muscles. However, while one study reported the use
of the active model in the pedestrian context [3], the controllers were deactivated, and therefore its response could
be deemed equivalent to a standard facet pedestrian model.
Aside from MADYMO, multiple commercial vehicle
dynamics/traffic accident reconstruction programmes are
equipped with multibody human body models, furthermore
referred to as MB forensic models: HVE [38], PC-CRASH
[107], Virtual CRASH [97, 98], CYBID V-SIM [193]. The
models of PC-CRASH, Virtual CRASH and CYBID V-SIM
were originally designed for pedestrian applications. The
HVE human body model was originally a vehicle occupant,
MADYMO
PC-CRASH
Virtual CRASH
CYBID V-SIM
52
51
Yes
Any characteristic
24
15
Yes
Limit ­anglesa
14
13
Yes
Limit ­anglesa
15
14
Yes
Any characteristic
Multiple
Single
Single
Single
Segments rigid bodies with individually defined mass properties. Joints a kinematic constraint connection
between two bodies which defines the number of degrees of freedom of motion between the two bodies.
Rigid bodies and kinematic constraints, a.k.a. segments and joints, constitute a multibody kinematic chain.
Scalable determines whether the dimensions and mass properties of a model can be scaled to any height
or weight. Joint motion restraint characteristic type of angle/torque characteristic that defines the passive
resistance of a joint to angular displacement from a default position. Geometries per Body how many geometric objects (hyperellipsoides, cylinders, mesh grids) can be attached to a single rigid body
a
Limit angles—restraint characteristic defined by two values of limit angles. Any angular position of a joint
between those two values causes little or no resistance to occur; beyond the limit values, the resistance
moment of force increases linearly with angular displacement. Note: The formulations of the Limit angle
restraint types may differ between software.
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but can also be used successfully for pedestrian impact
simulation. Notably, PC-CRASH, Virtual CRASH and
HVE multibody solvers utilise a similar contact algorithm
to MADYMO, i.e. an algorithm based on calculation of
the depth of penetration (expressed in [ m]). The pedestrian
model in CYBID V-SIM utilises an algorithm based on the
calculation of the intersection volume, expressed in [ m3], of
two bodies in contact.
Multibody forensic models are notably simpler than
MADYMO models as they have fewer body segments and
more simplified contact characteristics. Their major advantage is that they can be easily scaled to any given stature or
mass. An additional advantage of these models is that they
are part of robust software packages. Their user can integrate data collected on-scene during an investigation into
the software, create digital environment of an accident site
and simulate vehicle motion dynamics using mathematical
models of vehicle subsystems. This substantially streamlines
the process of pedestrian accident reconstruction.
2.3 Noncommercial Models
In addition to the models available in commercial simulation packages, many researchers created their in-house MB
pedestrian models. As evidenced by the history of vehicle
impact simulation, the development of a multibody solver
poses to be a challenge in itself. Thus, many researchers
decided to use the established MADYMO solver for their
multibody simulations investigation. The solver was used
to create numerous non-commercial pedestrian models [2,
36, 49, 82, 111, 203]. Some of the models based on the
MADYMO solver were also known in the literature under
different names: Chalmers pedestrian model (Chalmers
University of Technology, Sweden) [203, 205], RARU
model (Road Accident Research Unit of Adelaide University, Australia) model [36], JARI model (Japan Automobile
Research Institute) [111]. Except for the facet child pedestrian model by [206], most multibody models created with
the MADYMO solver were made using ellipsoidal surface
representation.
There are other commercial MB solvers that show
some potential in human body modelling in the area of
the automotive industry, i.e. ADAMS [54]. Nevertheless,
at the moment, few in-house multibody pedestrian models
have been reported to be created with solvers other than
MADYMO. The pedestrian model by [69] is one of the
exceptions, as it was created using the PC-CRASH multibody solver. Another in-house model created with a solver
different than MADYMO is VIRTHUMAN [184]. It is a
hybrid MB model (for definition of hybrid models, see:
Sect. 5.5) developed for ESI Virtual Performance Solution (VPS) software. The model combines MB functionalities with FE representation of the outer body surface. The
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formulation of contact surfaces is different than MADYMO
facet models. The outer FE surface of each segment is made
up of parts connected to the MB structure with one-dimensional springs and dampers, which properties were tuned to
assure the model’s biofidelity. The authors also developed
their own procedure to scale the model to various sizes and
mass properties [47]. The VIRTHUMAN model can be used
both as an occupant and as a pedestrian [185].
2.4 Customization of Multibody Models
Due to the popularity of MADYMO pedestrian models,
there have been attempts to create modifications to their
parts. This was done either by modifying a model’s characteristics, or by using more detailed MB segment models, or
by replacing MB segments with FE models. For instance,
detailed FE head models (FEHMs) [195] are frequently utilised for simulations with MB pedestrian models [34, 152,
187, 199].
[134] optimised joint and contact characteristics of the
upper body of the MADYMO facet model by performing
a validation against cadaveric data. The customised facet
model was later supplied with a new model of a lower
extremity and used as a pedestrian model [84].
Several alternative lower extremity models were proposed
[51, 62, 202, 204]. Lower extremity MB models were often
designed to be equipped with: (a) a detailed knee joint; (b)
frangible joints to simulate leg fractures.
While the frequency of life-threatening upper extremity
injuries in pedestrian accidents is low [105], differences in
the shoulder complex model may influence the kinematics of
the model and, consequently, the response of more sensitive
organs, for example the head [111]. In line with this, [112]
proposed a more biofidelic structure of the shoulder complex of an IHRA pedestrian model. [16] proposed a modified contact characteristic for the shoulder complex of the
MADYMO facet pedestrian model. Outside of the scope of
pedestrian impact modelling, [101] developed an MB upper
arm model as an augmentation to the MADYMO ellipsoidal
pedestrian model and they used it for the analysis of workplace accidents.
Children bodies have notably different material properties
from adults. To develop a family of 3, 6, 9 and 15-year-old
child pedestrian models, [82] calculated and applied scaling factors to modify the joint and contact characteristics
obtained originally from a 50th percentile adult model of
[205].
Numerical Approaches to Pedestrian Impact Simulation with Human Body Models: A Review
3 Finite Element Models
When there is a need for a more detailed study concerning not only kinematics but also the stress–strain behaviour of a pedestrian body, finite element (FE) models are
a suitable solution. By using full-scale FE surrogates of
a human body, researchers can not only study the wholebody kinematics of a vehicle-pedestrian interaction, but
also gain detailed insight into severity of injuries. This
yields data for the structural design and functional development of vehicle front-ends. Over the past decades, many
attempts have been made to develop biofidelic, finite element models of a pedestrian. There were attempts to create
full families of pedestrian FE models of various sizes and
ages. Examples of such models are presented in Fig. 4.
3.1 Families of Finite Element Pedestrian Models
Total Human Model for Safety (THUMS) [52] is a human
finite element model jointly developed by Toyota Motor
Corporation and Toyota Central R &D Labs. THUMS
models can be used as both a pedestrian [87] and as an
occupant [52]. The current THUMS system includes the
following pedestrian models: child models, 3-, 6-, 10-yearold [50]; the average size male model (AM50) [87]; the
small size female model (AF05); the large size male model
(AM95). The average-size male model (AM50), which
consists of nearly 2 million finite elements, is described
in Table 4. Developers of the THUMS family of models
in the LS-DYNA solver format made it freely available in
January 2021.
Global Human Body Models Consortium (GHBMC)
models are full-scale human body models developed
jointly by NHTSA and various automotive companies.
GHBMC system includes a family of occupant and pedestrian models. GHBMC pedestrian models come in two versions: a detailed pedestrian (P) and a simplified pedestrian
(PS). Currently, GHBMC pedestrian models are: a 6-yearold child model (6YO-PS) [100]; an average-sized male
model (M50-P/ M50-PS) [174, 175]; a small-sized female
model (F05-P/ F05-PS) [121]; a large-size male model
(M95-P/ M95-PS) [120]. The geometry of the M50-P
model was obtained by upright magnetic resonance imaging (MRI) of a specimen in standing posture.
[57] presented a family of VIVA+ FE human body
models. For VRU simulations, two standing-posture models are available: an average male (50M) and, remarkably,
an average woman (50F). It is worth noting that this is one
of the first efforts to develop a model of an average-sized
female, and that the female, not male, model was selected
as the base model during the development process. The
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geometry of the female model was largely based on various statistical shape models of body parts and medical
images that had previously been used to develop an occupant model [117]. VIVA+ models are distributed in the
open source model, in line with Open Science practise.
3.2 Other FE Models
Various FE models were developed by researchers associated with Honda automotive brand. [209] developed an ellipsoidal pedestrian model and used it for simulations with an
FE vehicle body. This model led further to development of
four pedestrian models by [115]: a large male, an average
male, a small female and a 6-year-old child. The geometry
of models of both [115, 209] was originally based on the
MB model by [49] and on GEBOD antrhopometry data [19].
The final models consisted of 15 segments and 14 joints,
and were translated into a PAM CRASH FE solver format.
Another FE model was developed by [63], with a special
focus on modelling the pelvis and lower extremities.
As part of the HUMOS2 project [181], an original
HUMOS (Human Model for Safety) FE occupant model
[138] was scaled to match dimensions of various specimens:
5th percentile female, 50th percentile male and 95th percentile male. Additionally, a tool was developed to position the
models to use them as pedestrians.
In the early 2000’s, JAMA (Japan Automobile Manufacturers Association, Inc.) [161] decided to consolidate efforts
of Japanese automotive manufacturers and jointly develop
a human body model. The results of these efforts led to
the development of models of human body models models, including pedestrians [165], which, at various stages of
the model’s development process, were used in a variety of
pedestrian safety studies [48, 122].
[56] developed a CHARM-70F model of a 70-yearold female pedestrian. The mesh of the body was created
through computer tomography of an elderly female cadaver,
which weighed 62 kg and was 1.58 m tall.
CHARM-10 FE model of a 10-year-old child occupant
and pedestrian was developed by [153]. The geometry was
primarily based on the collected CT and MRI imaging data
[90].
[126] developed a pedestrian version of a PIPER 6-yearold child occupant model. The original child occupant model
was developed as part of PIPER project [10], with its geometry approximated from CT scans of children of different
ages, lying in supine position. The occupant models were
capable of being scaled using GEBOD database [19]. Parallel to the development of the model, a PIPER software
framework, including a dedicated joint positioning tool, was
developed. The PIPER model is available under open source
licence.
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[78] describe the development of a 6-year-old pedestrian model (TUST 6YO). The geometry of the model was
obtained from CT images of paediatric standing posture.
4 Validation
4.1 Validation Sources
There are various sources for the validation of human-body
pedestrian models, the primary being results of in-depth
road accident investigations. If an accident is well documented, particularly with regard to vehicle and pedestrian
kinematics at the time of impact, then such data can be used
to simulate the event and assess the model’s accuracy. However, even the most in-depth accident data capture usually
only the state after the impact, and general information on
the initial conditions, and no detailed time-response data
on the mechanical properties of individual segments of the
human body are available.
Another solution is the usage of pedestrian crash test
dummies, as the experiments can be conducted in a known
environment and registered accurately. However, the low
biofidelity of dummies is often a downside to this approach,
and human body models by their nature should not aim to
recreate an artificial device such as a dummy.
The validation of advanced pedestrian body models is
currently largely reliant on results of Post Mortem Human
Subject (PMHS) experiments, which involve using cadavers of voluntary donors as specimens for testing [140]. The
PMHS-based validation is usually carried out on two levels:
component-level and full-body-level. The numerical model
validation procedure uses either individual signals from the
experiments or corridors derived from multiple specimens.
D. Wdowicz, M. Ptak
the surface of or inside of the cadaver specimen [12, 182].
Similar signals can easily be obtained in numerical simulations with pedestrian models, and direct comparisons can
be made.
Types of signals which are most often analysed as part of
the component-level validation process are: force–deflection,
contact force-time, pressure–time, moment-bending angle
(for joints), values of peak forces. The presence of direct
injuries is also observed, such as the location and number
of rib fractures per configuration or rupture of ligaments.
4.1.2 Full‑Scale Validation Using Cadaver Data
The full-body kinematics of advanced pedestrian models
are also validated using PMHS full-scale tests. During the
preparatory phase of PMHS full-scale experiments, cadavers before the crash are suspended and maintain an upright,
pedestrian-like position. Then, they are released, usually just
before the impact with a vehicle. The signals studied are
most often kinematic data obtained from high-speed video
analysis of markers/photo targets attached to the specimens
before the test. The common registered signals are: displacement–time and relative velocity–time relationships, 2D-trajectories of segments (vertical vs horizontal displacement)
[49, 61]. Accelerometers mounted to body segments were
used, too [49, 150].
The results of two full-scale pedestrian PMHS experiments were extensively used for the validation of pedestrian models: [49, 61]. Recently, a new PMHS study by
[150] has been published, but so far only the response of
the MADYMO MB pedestrian model has been compared
with these results [151].
4.2 Validation: Multibody Models
4.1.1 Component‑Level Validation Using Cadaver Data
On the component level of validation, a.k.a. sub-model validation [77], responses of individual body segments are studied under static, quasi-static and dynamic loads. Reference
data is mostly obtained from PMHS tests using impactors of
different sizes (rectangular, cylindrical, spherical), masses
(e.g. 23.4 kg in [12]; 5.6 kg in [110]) and impact velocities (e.g. average of 10.9 m/s in [166]). Non-impact experiments with cadaver specimens can also provide insight on
the behaviour of the body [162].
Different measurements can be obtained from PMHS
experiments. For instance, when an impactor strikes a
cadaver specimen, contact force can be obtained directly
from the impact hammer sensors [12], the displacement
of the impactor can be measured by means of high speed
video analysis [182], and accelerations acting on the human
body can be measured from accelerometers mounted on
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The earliest pedestrian models were validated mainly using
results from dummy tests [196]. The dummy models were
ONSER 50 dummy [24], Part 572 dummy, SIERRA 292/050
dummy. Validation of the models using cadaver test results
was also attempted [119].
The validation of the MADYMO pedestrian ellipsoidal
model has been documented by [177] and in the model
manual [167]. The validation process consisted of two parts.
First, dynamic response of individual body segments, i.e.
shoulder, thorax, abdomen, pelvis was validated based on
results of PMHS impactor tests in different configurations
(lateral, oblique), see Fig. 3 [12, 166, 182]. A good agreement with reference corridors was acheived. Then, full-scale
pedestrian impact simulations were carried out, with multiple PMHS tests as a reference (e.g. [49, 205]). Linear position and velocity of the head impact, and maximum linear
acceleration/bumper force were analysed.
Numerical Approaches to Pedestrian Impact Simulation with Human Body Models: A Review
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Fig. 3 Impactor dynamic tests:
a PMHS test [166]; b MB
simulation, MADYMO [177]; c
FE simulation, THUMS model
[171]
Table 4 Description of example pedestrian FE models
THUMS Version 4 for Euro
NCAP TB024 Pedestrian
AM50
Elements 1,975,471
Solid
1,527,480
hell
442,273
Beam
204
Nodes
772,172
Parts
1285
Fig. 4 Examples of FE pedestrian models. a THUMS Version 4 for
Euro NCAP TB024 Pedestrian AM50. b GHBMC Pedestrian Model
Simplified Version (PS)
GHBMC Pedestrian
Model Simplified Version
(PS)
834,228
646,785
184,847
2552
542,921
590
[23] scaled the MADYMO adult pedestrian model and
compared its response during the initial impact phase of
the vehicle against data from a PMHS experiment with a
female specimen [49]. Body segment trajectories (head,
pelvis, knee, ankle), resultant head velocity and head
accelerations were comparable to the reference values.
The newest efforts concerning improvement of the
MADYMO ellipsoid model were reported by [3]. Contact
and joint characteristics were optimised to achieve a better
response comparing to reference impactor and full-scale
PMHS tests. In addition to this, the modified model’s compliance to the requirements of EuroNCAP TB024 pedestrian model certification protocol [66] was confirmed.
As documented in [167], the MADYMO facet model
was validated using results of cadaver tests. Additionally,
[73] compared the kinematic response of the MADYMO
facet model with the cadaver tests by [49].
VIRTHUMAN model was validated in a series of
component-level validation simulations for multiple body
segments including the head, neck, torso, lower and upper
extremities [184]. Also, a full body validation was carried
out, too [184], and it was based on the PMHS pedestrian
impact test by [61], while using the deformation characteristics of the vehicle parts from [157]. The analysed
criteria for the full-body simulation were: trajectory of
head, thorax and pelvis; location of the head impact (wrap
around distance) and the corresponding time of the head
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strike. The results of the simulation were comparable to
the results of the PMHS experiment.
MB forensic models available in reconstruction software were often validated by means of comparison of body
motion with video data either from dummy crash tests [9,
29, 107–109] or CCTV (closed-circuit television) recordings [4, 13]. The main focus in those studies was on the
model’s ability to recreate locations where pedestrians or
dummies impacted the vehicle. Another often-studied criterion was pedestrian throw distance. Forensic experts often
estimate collision velocities through established relationships between throw distance and collision velocity (see:
Sect. 1.2). TTEhe Validation of some pedestrian models
reflects this approach to collision reconstruction. Simulated
pedestrian throw results are often compared against available
throw distance data from dummy tests [9, 98, 108, 109] or
from real accidents [4, 9, 13]. Analytical projectile methods
are also used as a reference [108, 136].
As part of the verification of pedestrian models, a realistic sensitivity of the kinematics of pedestrian models to the
geometry of impact vehicles has been shown for most MB
forensic models [97, 136]. Notably, throw distances of many
pedestrian models are higher than reference data, especially
in impact with higher velocity or with box-shaped vehicles
[98, 108]. In addition, the results show that throw distance
of pedestrian models tends to be highly sensitive to initial
impact location across the vehicle front [136].
MB forensic models were generally not validated against
PMHS data. [69] compared the motion response of their
custom MB model based on the PC-CRASH solver with
video frames from cadaveric and dummy tests. The research
involved tuning the physical parameters (coefficients of restitution and friction) of the initially proposed model individually for each collision configuration to obtain a response
comparable to the reference tests. [193] visually compared
the response of their lower leg model in one simulation with
cadaver tests by [91], but no quantitative measure of the
simulation accuracy was given.
4.3 Validation: Finite Element Models
Historically, there were FE pedestrian models for which
body segments were assumed to be rigid and for which there
were no validation data [115]. Currently, as researchers aim
to model the properties of a human body in the most minute details, robust validation remains a prerequisite for any
pedestrian impact simulations.
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D. Wdowicz, M. Ptak
4.3.1 Component‑Level Validation of Finite Element
Models
Multiple teams of researchers reported component-level
validation simulations of their FE pedestrian models [56,
57, 87, 100, 120, 175, 192]. Below is a selection of component-level validation configurations, based on [87, 192],
is given:
• lower extremity model: quasi-static three-point femur
bending, dynamic toe impact experiments, shearing and
bending of the knee joint, four-point bending of the
knee joint;
• head model: frontal skull impact, lateral head impact,
head rotation/brain kinematics test, head rotation/brain
injury test;
• chest, abdomen, pelvis models: lateral chest impact
with impactors, location and number of rib fractures,
pelvic lateral impact, pelvic fracture location;
The validation of individual body segments tends to be a
subject of separate studies by itself [86, 145]; individual
validated body segments are assembled into a complete
pedestrian body. Some developers first report componentlevel validation of models in occupant postures, and then
reposition and remesh the models to a pedestrian posture [87, 126]. However, it is also common to report the
component-level validation procedure for a model already
in the pedestrian posture [121]. When the authors report
validation of their full-body pedestrian models, individual
component tests are either described in detail as part of the
main article [87, 192], or an abridged tabular matrix of all
validation cases is provided [50, 57, 153].
Validation for a special subset, such as elderly or child
specimens, often requires narrowing the subset of available results of cadaver component-level experiments to
only the most relevant cases [10, 50, 56].
4.3.2 Full‑Body Validation Of Finite Element Pedestrian
Models
Several developers compared the responses of their FE
pedestrian models with full-body PMHS [87, 120, 121,
165, 175, 192, 209]. The validation criteria were mostly
based on the trajectory corridors of the body segments.
Comparison of injuries sustained by cadaver specimens
and pedestrian models is also used as a criterion for validation [120, 121, 175].
While it cannot be considered a validation per se, [153]
verified the full-body response of their FE model impacted
by a vehicle against results from a similar simulation
with a MADYMO MB pedestrian model scaled to the
Numerical Approaches to Pedestrian Impact Simulation with Human Body Models: A Review
dimensions of a 10-year-old child. The scaled MADYMO
model was not validated either, but a generally good comparison of kinematic responses between the two models
was noted.
5 Application of Pedestrian Models
Pedestrian models are primarily used in two areas of application: road accident reconstruction and pedestrian safety
analyses.
5.1 Road Accident Reconstruction
It is no surprise that there are numerous examples of reconstruction of pedestrian accident cases using MB forensic
models, as it is their primary area of application [29, 44–46,
55, 71, 127]. MADYMO pedestrian model has been used for
reconstruction purposes as well [23, 152, 173, 190, 199]. In
line with current best-practise recommendations [33], analysis of the uncertainty of accident reconstruction [85] has
also been applied to simulations of pedestrian impact with
MB models [213]. FE models are used much more rarely for
accident reconstruction. [72, 190]
An optimisation approach to accident reconstruction
using pedestrian models is discussed in Sect. 5.6.
5.2 Industrial Applications
The RARU model, the JARI model and the original TNO
MADYMO pedestrian model [177] played a role in developing subsystem testing procedure by IHRA [105, 111]. Simulations with model by [2] were used to derive a proposal for
joint torques properties for a Polar I dummy.
Pedestrian models are used to evaluate the performance
of new means of protecting vulnerable road users, including
adaptive bumpers [154].
5.3 EuroNCAP TB024
Simulations with pedestrian human body models have been
suggested as means of replacing some physical tests. An
example of bringing this idea into practise is the assessment of deployable systems for pedestrian protection (DS) in
line with the EuroNCAP VRU Testing Protocol [32]. There
are three areas of the Protocol where pedestrian models are
used:
• detection of pedestrians, i.e. determination of hardest to
detect pedestrian size
• timing of system deployment
• bonnet deflection due to body loading
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Details on each of these areas have been described, e.g. in
[32, 67]. In summary, the Testing Protocol determines that
certain procedures either can be or must be (depending on
the procedure) carried out using CAE simulations, as long
as the pedestrian models used for the simulations have been
certified according to Technical Bulletin 024 [66]. Otherwise, if no satisfactory test data is provided, the vehicle is
assessed with its active systems disabled, and this can naturally be detrimental to the vehicle’s final safety rating.
The development of the TB024 certification procedure as
part of the CoHerent project was described by [67]. Generic
vehicle FE models were developed as part of the project and
are used for TB024 certification. In practise, this means that
only FE pedestrian models or MB pedestrian models that
support MB-FE coupling can be validated in line with this
certification.
Assessment of compliance to the requirements of the
EuroNCAP TB024 pedestrian model certification protocol
has recently become the subject of numerous studies. The
study by [68] presented anonymised results for different
human body models, and it was shown that certain models
at their then-state did not satisfy the acceptable tolerance
corridors for head impact time or body segment’s centre
of gravity trajectories. Since then, publications reporting
improvements and compliance of various models have been
published: MADYMO [3], GHBMC Simplified Pedestrian
AM50 [26], TUST 6yo [78]. TB024 in its initial version
provided kinematic corridors for the 50th percentile male
model only. However, the current version (v. 3.0.1.) requires
validation of 6YO and 5th percentile female models as well.
5.4 Crashworthiness Studies
Due to fast computation times, MB pedestrian models can
be used to simulate a whole Design of Experiment (DoE)
matrix in a relatively short period of time. Therefore, the
DoE approach was used to study influence of vehicle shape
[141] or vehicle kinematics [214] on pedestrian injury
and kinematics. The simulation results obtained with a
MADYMO-based custom pedestrian model were used by
[84] to create and train a predictive model capable of assessing injuries only from the front design of the vehicle. The
influence of the position of the pedestrian in relation to vehicle was also studied. [155] studied the influence of rotation
angles of pedestrian on injury risk, while [215] analysed the
influence of gait type on injury. [43] investigated the influence of non-struck leg on pedestrian pelvis kinematics using
a modified FE pedestrian model by [63].
MB forensic models were also used in crashworthiness
studies, for example, to assess the values of biomechanical
injury criteria under different conditions [4, 20, 127, 210,
215]. However, since forensic models have not been reported
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to be validated for crashworthiness analyses, their ability for
accurate injury prediction is doubtful.
When applying MB pedestrian models, either FE [34,
130] or MB [83] vehicle models have been used.
5.5 Coupling
It is a common practise to first use MB models to determine impact conditions and then carry out a further detailed
analysis. By the same token, results from reconstructions
with MB forensic models provided boundary conditions for
simulations with MADYMO [80, 125] or FE [201] models.
MADYMO MB models were also used as a tool to determine the boundary conditions for subsequent FE simulations
[34, 123, 129, 207]. The coupling of MB and FE simulations
is also a valid approach in both accident reconstruction [34,
152] and crashworthiness analyses [81, 130]. In most coupling cases, the coupling configuration is FE vehicle—MB
pedestrian, but FE parts (skull/brain models in particular) for
an MB model are also utilized [34, 152, 187, 199]. Human
body models which combine MB and FE functionalities are
known in the literature as hybrid models [139]. The main
advantage of the hybrid approach is that it allows for the
simulation of complex systems that have both rigid and
deformable components, which cannot be efficiently modeled using either MB methods or FE methods alone. By
combining these two techniques, the hybrid approach can
provide a more accurate and comprehensive representation
of the system dynamics.
5.6 Optimization
Optimisation methods are often used in pedestrian impact
analysis. The principle behind those methods is to carry out
numerous simulations to find one set of input parameters
which would minimise the value of target function.
For safety analysis, optimisation is usually employed to
create car front shapes with the best pedestrian crashworthiness response [76]. However, there are examples where optimisation was used to assist the development of pedestrian
models themselves, i.e. to develop a body segment model
with a desired contact response [62, 134].
Moreover, optimisation methods are an often-used technique for reconstruction of road accidents [33], used to recreate various vehicular impact configurations [83]. In the
case of pedestrian accident reconstruction, the target function would often be a difference in vehicle and/or pedestrian rest position between simulation and accident, and the
sought-after parameters are usually collision velocity and
the initial impact configuration (linear and angular position
of vehicles and pedestrians).
Examples of the application of optimisation techniques
in relation to accident reconstruction with MADYMO
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D. Wdowicz, M. Ptak
pedestrian models have been carried out [152, 173, 190,
199]. It is also a standard approach to reconstructions with
PC-CRASH, Virtual CRASH or V-SIM programmes, too.
For optimisation, many safety analysis or forensic software packages are equipped with their own optimisation
modules [22, 33, 168]. External commercial packages are
also used for optimisation in pedestrian simulations [62, 143,
200], with MATLAB being a notable example [76, 77, 142,
211].
Attempts have also been made to run MATLAB optimisation with FE solvers for purposes of pedestrian modelling
[77].
5.7 Other Applications
MB forensic models are used in a variety of applications
outside of the typical reconstruction. Examples are: supporting statistical analyses of accident configurations [6, 17,
18], preparation of pedestrian detection staged experiments
[180], supporting study of influence of driver behaviour on
pedestrian injuries [210], being a reference point to determine the feasibility of formulas describing pedestrian afterimpact motion [212].
Obviously, pedestrian models have originally been
validated for pedestrian impact applications, and so initial
attempts required creating modifications to apply the models
for other VRU impact purposes, e.g. a bespoke knee model
for a cyclist model [14] However, now it remains common
practise to use them for a multitude of impact scenarios:
cyclist [124, 158, 176, 194], solowheel or doublewheel electric self-balancing scooters [149, 188] or powered standing
scooters [92, 128, 131].
Other applications include sports biomechanics [170],
fall from heights [30, 102, 186] and workplace accidents
[101, 103].
6 Discussion
6.1 Quality of Reference Data Used for Validation
Many studies used results of accident investigations as initial
conditions for simulations with advanced pedestrian models
[23, 65, 80]. One of the most important factors in pedestrian accidents is vehicle-pedestrian collision velocity, which
in most cases is unknown and can only be estimated. The
estimations of impact velocity are usually based on throw
distance formulas or skid marks [113], and due to variability
of many parameters (vehicle model, road surface properties, unknown braking intensity) should always be estimated
after an uncertainty study. It is not always the case, and thus
quality of velocity estimations is sometimes debatable. In
addition to that, accidents are often initially reconstructed
Numerical Approaches to Pedestrian Impact Simulation with Human Body Models: A Review
using pedestrian models (e.g. as in [80]), so it could be
argued that in such studies results from one model influence results of simulations that use other, more advanced
models. Currently, CCTV video recordings of accidents can
be considered to be one of the most reliable objective means
for collision velocity estimation; thus, accident-based validation of models should primarily use these new sources of
information [4].
While being a valuable source of information, PMHS
tests are not without flaws. Experiments with cadaver specimens are (usually) one-time, hardly repeatable tests with
inherent uncertainties such as material properties of a particular human specimen. There is also a risk of overtuning
the model when validation is based only on results from a
narrow group of specimens.
6.2 Validation vs Phases of Pedestrian Impact
Many kinematic analyses, including frame-by-frame analyses, focus only on the model behaviour up to the moment
of head-windscreen impact [49, 69, 107] or, at most, up to
the moment of separation of the body from the vehicle [108,
109]. Motion of the model during the flight phase, i.e. in-air
rotation or flight height, has received some attention [11,
104, 109, 127, 151], but more research is still required. In
the practise of accident reconstruction, the in-air trajectory
of the pedestrian registered on a CCTV or dash camera is
often one of the few reliable pieces of evidence available
to the forensic expert. Juxtaposition of simulation against
video recordings is a standard approach to assessing impact
velocity, and so the pedestrian’s flight phase should receive
more attention in terms of the models’ validation.
Ground contact after the flight phase is relatively rarely
studied using advanced pedestrian body models. On that
note, simulations with the MADYMO pedestrian model by
[151], which recreated the PMHS full body experiments by
[150], showed that the accurate prediction of ground contact
injuries is generally limited by the accuracy of pedestrianvehicle contact response.
Remarkably, no simulation has been reported in which the
pedestrian throw distance for the FE pedestrian models was
measured. FE simulation of pedestrian impacts usually end
at 0.1s after the impact with the vehicle [72, 175], while in
reality a pedestrian comes to a full stop only at about 1.5–2.0
s after the initial impact.
6.3 Criticism of Validation Procedures
Validation of many forensic models has been done by means
of full-scale dummy experimental tests. It could be argued
that this approach is inherently flawed, as the purpose of
most software developers was to provide its users with computational human models, and not digital equivalents of a
4699
particular dummy model. Also, many validation papers do
not provide any detailed description of the dummies used
[9, 108], and thus biofidelity of those devices cannot be
ascertained.
The above comments notwithstanding, most pedestrian
models that were actually validated using dummy test results
are those dedicated to forensic accident reconstruction. Inaccuracy of vehicle models used, uncertainty of initial conditions, and sometimes even inaccuracy of measured evidence
(such as final rest position of the pedestrian) is inherent in
the practise of reconstruction. No numerical reconstruction
with a pedestrian model, or any other type of road accident
reconstruction for that matter, should be treated as a definitive recreation of a particular event. Thus, an assumption
that it is enough for the digital model response to be equivalent to a dummy’s (at least in terms of global kinematic
parameters such as throw distance or impact area location)
could be considered valid. However, remarkably, most of
the MB forensic models implemented in commercial reconstruction packages have not yet been validated by means of
component-level or full-body simulations of PMHS tests.
Efforts were made to validate the response of the model
using video recordings of dummy impact tests. However,
some of the older studies lacked objective assessment (e.g.
comparison of surface marker trajectory). [107–109]. Future
investigators should address this if dummy experiments are
to be used for validation of pedestrian models. On that note,
PMHS studies, where the trajectory of body segments is
often registered in detail [49, 61], could be considered more
useful for validation.
Contemporary FE pedestrian models of THUMS,
GHBMC and JAMA are reported to be validated using both
component-level and full-body-level reference data. This is
however not always the case, and often full-body-level validation of pedestrian models is not reported, for instance in
studies by [56, 57]. In a study by [153], the developers conducted only a full-body verification, not validation, by comparing their simulation results with results from a not validated, 10-year-old pedestrian model scaled from MADYMO
MB pedestrian model as a reference. This omission of validation for full-body pedestrian applications could be coming
from the fact that many pedestrian models are morphed from
occupant models, and it is occupant-wise validation that has
the most priority when the models are initially developed.
The quality of the human body model model is often
quantified using objective rating methods, for instance ISO/
TS 18571 standard used in the work of [56]. The model
rating scores are based on comparison of similarity of two
curves, which are results of a numerical simulation and
a reference experiment; a higher rating score is meant to
show better biofidelity. [175] argued that such approach is
not entirely correct; HBMs are originally meshed from different body specimens than cadaver specimens used in most
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validation experiments, and so the experimental and simulation configurations are hardly comparable. In addition, the
researchers indicated that the simple linear scaling of the
reference results has significant limitations. Due to these
considerations, [175] in their study decided to keep only the
qualitative level of model validation.
6.4 Reconstruction
In practise of accident reconstruction, advanced pedestrian
body models are rarely used by individual witness experts.
As noted previously, FE simulations are generally too time
consuming for a standard accident reconstruction.
MADYMO MB model was suggested as a means of vehicle accident reconstruction, especially in conjunction with
optimisation techniques [152, 173, 190, 199]. However,
[152] noted that optimisation techniques for MADYMO
pedestrian model are best used for rest-position based accident reconstruction (due to lower non-linearity of displacement parameters compared to pedestrian injuries). Recent
studies [5, 151] suggest that inability to reliably model
ground impact limits the applicability of the MADYMO
pedestrian model in the area of individual vehicle reconstruction. By extension, care should be exercised when using
MB forensic models, too, especially for more detailed injury
analyses.
6.5 Practical Problems of Running Simulations
with Pedestrian Models
The history of development of pedestrian models often
showed the ingenuity of researchers when faced with limitations of numerical solvers. For instance, a decision to
create a facet child pedestrian model by [206] was actually
rooted in initial problems with the magnitude of ellipsoid
penetration. Severity of penetration precluded the use of the
standard contact algorithm, and thus the authors decided
to discretise ellipsoidal surfaces with a facet surface mesh.
Another example is a model of [69] and their decision to
split each spherical joint into three revolute joints. This
allowed the author to bypass limitation of the solver and
to declare separate motion restraints on each of rotation
directions.
MADYMO model of [177], in order to achieve a good
correlation of the contact response in different impact directions, was supplied with additional ellipsoidal surfaces on
the lateral and rear part of the pelvis to model the volume
of flesh in these areas. Such an approach is rarely used in
MB forensic models, as their solvers usually allow only one
contact surface per rigid body.
Numerous publications exist which attempted to recreate
the geometry and contact properties of a car body [23, 49,
177, 205], usually modelled using ellipsoidal surfaces. Facet
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D. Wdowicz, M. Ptak
models of pedestrian-impacting vehicle parts have been
modelled, too. [106]. These vehicle models were used in the
analyses of the influence of vehicle properties on pedestrian
injury criteria. In the case of dedicated reconstruction software such as PC-CRASH, Virtual CRASH, CYBID V-SIM
or HVE, the programs are usually supplied with databases
of thousands of vehicle models (dimensions, body shapes)
as well as default values of contact stiffness. The latter,
however, are usually assumed to be the same for all vehicle
models, so recreating the exact properties of a particular
model needs to involve manual tuning. Also, reconstruction
software often uses generalised stiffness for the entirety of
the vehicle body, thus not taking into account differences in
stiffness between, e.g., windscreen and A-pillars.
A major issue of some FE pedestrian models is their
lack of robustness in longer simulations due to the explicit
time-integration error accumulation. Moreover, the complexity of the material model, the element formulation and
thus the number of integration points in finite elements
directly influence the computation time. [57] noted that,
for instance, VIVA model has a runtime of 90 min on 32
cores for a simulation time of 50 ms (timestep of 0.33 μs);
in the same paper, it is said that the occupant version of
GHBMC detailed model runs a simulation of 60 ms on 48
cores in 16 min (timestep of 28 μs), and the detailed occupant GHBMC model runs a simulation of 60 ms on 48 cores
in 538 min (timestep of 0.1 μs). In contrast, the authors of
the current paper ran a basic pedestrian impact simulation
with a V-SIM 5.0.62 forensic pedestrian MB model. The
runtime was approximately 4.5 s using an 8-core processor
for a simulation time of 4 s (timestep of 200 μs). This can
be attributed to the model’s simplified geometry and low
number of degrees of freedom of the whole system (see:
Table 3).
The above could be the reasons for a relative paucity of
practical application of FE models in accident reconstruction, in which applications all phases of pedestrian kinematics often need to be simulated (see: Sect. 6.2). Due to robustness and numerical stability, MB models are also good for
large-scale studies. This is showcased by a research by [214]
who conducted a Design of Experiment matrix of 10,000
simulations with MADYMO pedestrian MB model in various impact configurations.
All MB and FE pedestrian models currently used for
impact simulations recreate only passive properties of a
human body, i.e. the behaviour during impact is equivalent to
an unconscious person’s. However, the motion of the pedestrian lateral to the vehicle’s front plays and important role
on the outcomes of an impact. As a compromise, for most
simulations, the motion of the pedestrian at the moment of
impact is usually simulated by rigid whole-body transformation (usually linear translation with a fixed linear velocity),
with joints in a fixed position to recreate the pedestrian’s
Numerical Approaches to Pedestrian Impact Simulation with Human Body Models: A Review
posture at the time of impact [152, 173]. Additionally, while
much research has recently been conducted in the area of
modelling active muscle behaviour of an occupant [164], the
subject of modelling active muscles for pedestrian models
has yet to be explored. An analysis of 305 accident recordings uploaded to YouTube [179] has shown that only 43%
of pedestrians exhibit no reaction before the impact, while
the rest exhibit behaviours ranging from stopping, slowing
down, accelerating, or even jumping away. Future studies
with numerical mechanical models of pedestrians should
aim to define pedestrians’ pre-crash movement and behaviour more accurately.
6.6 Geometry Sources, Scalability, Morphing
Pedestrian models use different sources for geometry and
inertial data. A common approach, especially when creating multibody models, is to use statistical data. GEBOD
(Generator of Body Data) [8, 19] is a software/database
of anthropometric dimensions of U.S. and German Air
Force specimens [39] and it was a source of geometry and
mass properties of multiple pedestrian models [36, 38, 49,
203, 206]. RAMSIS software data [147, 148] of a Western
European specimen were used to create the default model
of [177], but scaling of that model is still by default done
using GEBOD database. While the dimensions of the default
model by [108] were based on dimensions of a Hybrid III
dummy, its scaling is based on [59] and an undefined Slovakian research [31]. [193] based their pedestrian model generator on two sets of data: a U.S. Air Force population [39]
and a Polish average population [37].
Another approach is to directly use a geometry of an
individual specimen obtained from CT or MRI data. This
approach was used by developers of multiple FE models
[56, 175, 192].
Geometry of a model can also be generated via morphing. Morphing can generally be described as the process
of modyfing the shape of a model’s geometry based on a
target set of mesaurements. Regarding the development of
pedestrian models, it is common to morph previously developed FE models to a different size or sex. In most cases,
the target geometry is obtained from taking anthropometric
measurements by means of 3D scanning techniques [120,
121]. Different morphing approaches are used: for instance,
radial basis interpolation approach was used for pedestrian
models [120, 121], but in the area of human body modelling
kriging and moving least squares methods are also used [58].
The influence of pedestrian stature on impact results
has long been known [161]. However, while there were
attempts to investigate the influence of different anthropometries on results of numerical simulations [189], it is
still unclear if minor differences between specimens of
4701
different populations, but of identical standing height, have
any major impact on results of crashworthinness research
or forensic investigation. Also, influence of the manner of
scaling of dimensions (which is sometimes done simplistically by linear functions) remains unknown.
Scaling for FE models often involves morphing previously developed FE models to a different size or sex. In
most cases, the target geometry is obtained from taking
anthropometric measurements by means of 3D scanning
techniques [120, 121]. Different morphing approaches are
used: for instance, radial basis interpolation approach was
used for pedestrian models [120, 121]. Scaling in general
tends to be a problem for FE pedestrian models, and developers need to employ additional tools of verifying stability
(“runability”) of their models.
Those issues are much more rarely encountered when
using MB models, naturally at the cost of the accuracy of
the generated model’s geometry.
Scalability of pedestrian models, while being a vital
feature used in many studies, poses a problem of the resultant model being not biofidielic. Indeed, many validation
simulations have been carried out for the 50th percentile
adult model only [108, 177], and no direct data for validation of the other model sizes is available.
Currently, much research in the field of vehicle crashworthiness addresses the problem of equal representation in terms of the passive safety of various size and age
groups. These groups include: an average female [117], so
far largely underrepresented in the crash test protocols, as
well as children [93] or disabled road users [163]. Modelling bodies of children [50, 78, 100, 126, 153] or the
elderly pedestrians [56] remain in line with the efforts for
introducing equity to the field of road safety.
6.7 Repositioning Tools
Pedestrian postures vary to a large degree depending on
the walking or running speed, or the gait cycle phase
[173]. Thus, pedestrian models need to be capable of modelling a variety of postures. Additionally, as pointed out
previously in this review, pedestrian HBMs were often
based on occupant models. As a result, there was a need
for repositioning the model’s joints from an occupant to
any pedestrian posture. Positioning is rarely an issue for
the ellipsoidal MB models, but it is a fundamental problem for models which geometry is based on mesh grids,
FE models in particular. Stretching and compression of
elements may pose numerical problems. Thus, various
approaches to positioning of finite element models to
pedestrian postures were proposed [28, 53, 57, 181].
13
4702
D. Wdowicz, M. Ptak
6.8 New Approaches to Model Distribution
It is becoming popular to employ knowledge from different
fields, including software engineering, and introduce it to the
practice of mechanical engineering. Version control systems
(e.g. Git [15]), usually employed for maintenance of programming code, are used to store the model source files, and
dedicated pre- and post-processing tools are developed with
programming languages such as Python [57]. In the case of
models by [57], users can participate in the development of
the models by committing their changes to a common repository, which changes are later approved by the core development team before finally introducing them to the model.
Additionally, families of THUMS, VIVA+, GHBMC models
are distributed free-of-charge, or under terms of academic
licences, which can lead to their widespread use amongst the
academic community.
On this note, it is worth noting that usage of some human
body models for purposes of litigation or investigation is
generally limited and regulated by their User Policy, eg.
THUMS [172]. Meanwhile, specialized reconstruction software with their human body models is usually distributed
under the “as it is” principle, and as is often said explicitly
in their User Policy, the responsibility for the accuracy and
application of the models lies with the end user.
7 Conlusions
The state-of-the-art review provides the following
conclusions:
1. Approaches to validation of pedestrian models can be
divided into following subcategories:
• accident-based (case studies, empirical formulas
based on statistics)
• experimental-based (dummy tests, PMHS studies).
2. The level of detail of validation varies between pedestrian models and is dependent on the models’ intended
area of application:
• pedestrian models for forensic applications rarely
use PMHS data as a reference; dummy testing and
accident-based approach are the most common validation method
• advanced multibody and finite element models for
crashworthiness analyses, and industrial applications
predominantly use PMHS as a validation reference.
3. Due to their numerical efficiency, multibody models of
pedestrian bodies are used for large Design of Experiment test matrices. Finite element models are thoroughly validated and are capable of replicating pedes-
13
trian’s injuries with great detail. A common approach to
pedestrian impact modelling is multibody-finite element
coupling (hybrid modelling), where selected parts of a
multibody system are replaced with a detailed finite element model.
4. The usefulness of multibody forensic models in the area
of crashworthiness analyses can be deemed limited, as,
for most of them, no validation has been reported so far
in terms of their ability to predict injury.
5. Pedestrian models are used not only for pedestrian applications, but also as other vulnerable road users: cyclists
or e-scooter riders. In many commercial reconstruction
programs, multibody forensic models of pedestrians are
used as occupants as well.
6. Development of pedestrian models dovetailed with the
introduction of dedicated joint positioning tools. The
open-source distribution of human body models and the
introduction of digital testing as a replacement for physical tests could mark the future for automotive engineering.
Declarations
Conflict of interest On behalf of all authors, the corresponding author
states that there is no conflict of interest.
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long
as you give appropriate credit to the original author(s) and the source,
provide a link to the Creative Commons licence, and indicate if changes
were made. The images or other third party material in this article are
included in the article’s Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in
the article’s Creative Commons licence and your intended use is not
permitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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