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17452 IEEE INTERNET OF THINGS JOURNAL, VOL. 9, NO. 18, 15 SEPTEMBER 2022
Mobility Digital Twin: Concept, Architecture,
Case Study, and Future Challenges
Ziran Wang , Member, IEEE, Rohit Gupta, Kyungtae Han ,Senior Member, IEEE,
Haoxin Wang, Member, IEEE, Akila Ganlath, Nejib Ammar, and Prashant Tiwari
Abstract—A Digital Twin is a digital replica of a living or
nonliving physical entity, and this emerging technology attracted
extensive attention from different industries during the past
decade. Although a few Digital Twin studies have been conducted
in the transportation domain very recently, there is no systematic
research with a holistic framework connecting various mobility
entities together. In this study, a mobility digital twin (MDT)
framework is developed, which is defined as an artificial intelli-
gence (AI)-based data-driven cloud–edge–device framework for
mobility services. This MDT consists of three building blocks in
the physical space (namely, Human,Veh i c l e ,andTraffic), and
their associated Digital Twins in the digital space. An exam-
ple cloud–edge architecture is built with Amazon Web Services
(AWS) to accommodate the proposed MDT framework and to
fulfill its digital functionalities of storage, modeling, learning, sim-
ulation, and prediction. A case study of the personalized adaptive
cruise control (P-ACC) system is conducted, which integrates the
key microservices of all three digital building blocks of the MDT
framework: 1) the Human Digital Twin with user management
and driver type classification; 2) the Vehicle Digital Twin with
cloud-based advanced driver-assistance systems (ADAS); and 3)
the Traffic Digital Twin with traffic flow monitoring and variable
speed limit. Future challenges of the proposed MDT framework
are discussed toward the end of the article, including standard-
ization, AI for computing, public or private cloud service, and
network heterogeneity.
Index Terms—Amazon Web services (AWS), cloud computing,
connected vehicles, digital twin, edge computing.
I. INTRODUCTION
THE RECENT development of the Internet of Things (IoT)
has been facilitating all kinds of cutting-edge technolo-
gies, where their application scenarios are rooted both in the
user level (namely, individual consumer or private company),
and the system level (namely, commercial or industrial sec-
tor). From the user’s perspective, the introduction of the IoT
will play a leading role in scenarios like assisted living, e-
health, and enhanced learning. From the system’s perspective,
the most apparent scenarios will be industrial manufactur-
ing, logistics, business/process management, and intelligent
transportation of people and goods [1].
Manuscript received 17 October 2021; revised 30 January 2022; accepted
26 February 2022. Date of publication 2 March 2022; date of current version
7 September 2022. (Corresponding author: Ziran Wang.)
The authors are with InfoTech Labs, Toyota Motor North America
Research and Development, Mountain View, CA 94043 USA (e-mail:
ryanwang11@hotmail.com; rohit.gupta@toyota.com; kyungtae.han@
toyota.com; haoxin.wang@toyota.com; akila.ganlath@toyota.com;
nejib.ammar@toyota.com; prashant.tiwari@toyota.com).
Digital Object Identifier 10.1109/JIOT.2022.3156028
The Digital Twin, as an emerging representation of the
cyber–physical systems (CPS), attracted increasing attention
over the past decade [2]. Based on a market research report,
the global Digital Twin market size was valued at U.S. $ 5
billion in 2020, and is projected to expand to 86 billion in
2028 at a compound annual growth rate of 42.7% during this
period [3]. It was also pointed out in their report that, the
automotive and transportation industry takes one of the largest
shares in the global Digital Twin market in 2020, among other
end-uses like manufacturing, energy, and healthcare.
Although the definitions of the Digital Twin vary in dif-
ferent versions [4], [5], the basic concepts are essentially the
same: A Digital Twin is a digital replica of a living or nonliv-
ing physical entity. Digital Twin technology paves the way to
real-time monitoring and synchronization of real-world activi-
ties with the virtual counterparts [6]. The Digital Twin concept
was first born in the aerospace domain when the National
Aeronautics and Space Administration (NASA) adopted that as
a key element in its 2010 technology roadmap. Along with its
rapid development in different domains during the past decade,
including aeronautics and space [4], [7], robotics [8], [9], man-
ufacturing [10], [11], and informatics [12], the Digital Twin
also has a huge potential in the transportation domain.
The emergence of connected vehicle technology introduces
another platform to implement the Digital Twin. Since the
level of connectivity within our vehicles has greatly improved,
these equipped vehicles are able to “talk” with other entities,
such as with other connected vehicles through vehicle-to-
vehicle (V2V) communications, with traffic infrastructures
through vehicle-to-infrastructure (V2I) communications, and
with cloud servers through vehicle-to-cloud (V2C) commu-
nications [13]–[16]. Specifically, V2C communications allow
connected vehicles to: 1) upload their data to the cloud
server, enabling Digital Twins to be built in the digital (cyber)
world based on their counterparts in the physical world and
2) offload their onboard computations to the cloud server,
enabling Digital Twins to build models and calculate guid-
ance information through powerful cloud computing, which
can then be fed back to connected vehicles.
Very recently, a few Digital Twin studies have been con-
ducted in the transportation domain [17]–[19], but none of
them has a holistic framework connecting various mobility
entities (i.e., human, vehicle, and traffic) together. In this
study, a mobility digital twin (MDT) framework is proposed,
which is defined as an artificial intelligence (AI)-based data-
driven cloud–edge–device framework for mobility services.
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WANG et al.: MOBILITY DIGITAL TWIN: CONCEPT, ARCHITECTURE, CASE STUDY, AND FUTURE CHALLENGES 17453
The MDT framework is built on top of three different planes:
1) the physical space that has human beings, vehicles, and
traffic infrastructures; 2) the digital space that has the dig-
ital replicas of aforementioned physical entities; and 3) the
communication plane between these two spaces. Given the
connectivity nature of this framework, it transforms connected
vehicles into Internet of Vehicles (IoV) by leveraging IoT tech-
nologies. The cloud–edge architecture is built with Amazon
Web Services (AWS) to accommodate the proposed MDT
framework and implement its digital functionalities of storage,
modeling, learning, simulation, and prediction. The effective-
ness of the MDT framework is shown through the case study
of three digital building blocks with their key microservices:
the Human Digital Twin with user management and driver
type classification, the Vehicle Digital Twin with cloud-based
advanced driver-assistance systems (ADAS), and the Traffic
Digital Twin with traffic flow monitoring and variable speed
limit.
Since traditional mobility system frameworks heavily rely
on onboard storage and computing, their functionalities are
limited by multiple constraints, such as computing power,
accessibility to big data, and easiness of deployments and mod-
ifications. On the contrary, the proposed MDT framework in
this study addresses these constraints by making the following
contributions.
1) Powe rful: The MDT framework allows users to rapidly
adjust cloud resources to meet fluctuating/unpredictable
demands, providing high computing power at certain
periods of peak demand.
2) Shareable: Bulk data generated by an end user is
offloaded and stored on the cloud (and/or edge), which
can be retrieved and utilized by the same user at a
later time frame, or shared with other end users for
microservices on demand.
3) Manageable: The MDT framework allows users to get
their microservices up and running faster on the cloud
platform, with more manageability and less mainte-
nance. Over-the-air (OTA) updates are also available to
the MDT framework.
4) Extendable: Arbitrary mobility microservices can be
easily implemented to the MDT framework with min-
imal changes on the cloud–edge architecture and data
structure.
The remainder of this study is organized as follows.
Section II conducts a literature review regarding cloud com-
puting and Digital Twins in the context of connected vehicles.
Section III introduces the concept of this proposed MDT
framework with a detailed explanation of the communication
plane and data workflow, the physical space, and the digital
space. Then, the cloud–edge architecture based on AWS is
developed in Section IV, which accommodates the proposed
MDT framework. A case study is conducted in Section V,
where a personalized adaptive cruise control (P-ACC) system
integrates multiple microservices of the MDT framework and
outperforms traditional ACC systems. Finally, this study is fin-
ished with a discussion about future challenges in Section VI
and a brief conclusion in Section VII.
II. LITERATURE REVIEW
A. Transportation Applications With Cloud Computing
The emergence of commercial cloud computing services,
such as AWS [20], Microsoft Azure [21], Google Cloud
Platform (GCP) [22], and Alibaba Cloud [23], has facilitated
many applications in the domain of vehicular/transportation
CPS. Such services always provide a variety of basic abstract
technical infrastructure and building blocks for distributed
computing. Taking AWS as an example, which has the
largest market share among all competitors in 2020, it com-
prises over 200 products and services for computing, storage,
networking, database, analytics, IoT, and so on [20]. All
these features of cloud computing services, together with
their advantage of scalability, enable connected vehicles to
offload their data and onboard computing demand to the
cloud.
Guerrero-Ibanez et al. [24] demonstrated that cloud comput-
ing can be integrated with intelligent transportation systems
to address issues faced by the transportation sector, such
as traffic congestion, roadway safety, and pollutant emis-
sions. Specifically, the concept of vehicular cloud can enhance
transportation systems by storing and processing the col-
lected data (including traffic lights, parking meters, camera
images, etc.), and creating a historical registry of vari-
ous data sources [25]. Therefore, the transportation author-
ities who own these entities can make informed deci-
sions on when to change traffic directions, install new
traffic lights, and remodel/repair road segments. However,
the detailed cloud architecture design is not covered in
these studies, and the vehicular cloud applications are intro-
duced only on the conceptual level without conducting case
studies.
During the past decade, various transportation applica-
tions were proposed by leveraging the capability of cloud
computing [26], [27]. A navigation-assisted route optimizer
was developed by Gerla, where the navigator server col-
lects information from connected vehicles, and then computes
the optimal routes by constructing a traffic load map and
traffic pattern matrix, estimating road segment loads and
delays [28]. A bus smart sensor prototype was designed and
implemented by Herrera-Quintero et al. [29] using the server-
less and microservice cloud architecture, where GCP Firebase
was used for storage and AWS Lambda was used for compu-
tation. A vehicular pollutant emission detection system was
developed by Bhatnagar et al. [30], where AWS IoT and
Amazon DynamoDB were integrated to send notifications to
the vehicle driver if the emission sensor detects a gas leakage.
A vehicle-based traffic surveillance application was developed
by Deng et al. [31], where the AWS-based serverless cloud
architecture was proved to be feasible for real-time transporta-
tion applications through a field implementation. However,
aforementioned studies focus more on individual transporta-
tion application that provides solutions within a very limited
domain, while none of them designs a holistic framework that
connects various mobility entities, benefiting human, vehicle,
and traffic at the same time.
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17454 IEEE INTERNET OF THINGS JOURNAL, VOL. 9, NO. 18, 15 SEPTEMBER 2022
B. Digital Twins for Connected Vehicles
The Digital Twin concept has been loosely defined and
adopted in the transportation domain since its emergence,
partly due to its similarity and connection with other technolo-
gies. In particular, the confusion among the roles of iteration
(i.e., switches back and forth between the physical and digital
spaces), model-based design (i.e., starts with digital compo-
nents and incrementally swaps in physical components), and
Digital Twins (i.e., maintain synchronized versions of a physi-
cal system and its digital counterpart) in connected vehicles are
well discussed in the survey paper by Schwarz and Wang [32].
Nonetheless, many previous efforts related to the IoT and CPS
in the automotive industry envision the development of the
Digital Twin, since the majority of those proposed methodolo-
gies and/or algorithms were developed on multilayer system
frameworks with physical entities (i.e., vehicles) and their
digital replicas (simulation models/environments).
Alam and Saddik developed a Digital Twin framework refer-
ence model for the cloud-based CPS, where a telematics-based
driving assistance application was proposed for the vehicular
CPS with three parts: 1) computation, 2) control, and 3) sen-
sors and services fusion [17]. Kumar et al. [18] proposed a
Digital Twin-centric approach with machine learning, edge
computing, 5G communication, and data lake, aiming for
driver intention prediction and traffic congestion avoidance.
Chen et al. [19] proposed a “Digital Behavior Twin” frame-
work in which behavioral models of drivers are shared among
connected vehicles to predict future actions of neighboring
vehicles and hence improve driving safety. This idea was
extended to two subsequent patent applications by the same
authors [33], [34]. However, the term of “Digital Twin” is
used more like a simple concept in these studies, where their
presented technologies are agnostic to any IoT technologies
besides the Digital Twin. They do not systematically integrate
the Digital Twin in any cloud architecture design, nor do they
have clear data structures or workflows.
Related literature in the fields of “parallel driving” or “par-
allel transportation” has direct implications to the Digital Twin
framework of our study. In 2010, Wang brought up the paral-
lel transportation concept for the first time, where he defined
parallel control and management of transportation as “a data-
driven approach for modeling, analysis, and decision making
that considers both the engineering and social complexity in
its processes” [35]. Many subsequent works were conducted in
this research domain afterward, including the parallel driving
framework proposed by Wang et al. [36]. In this cloud-based
cyber-physical-social system framework, the physical world,
mental world, and artificial world are modeled as three paral-
lel levels, considering interactions among connected vehicles,
human drivers, and information. However, many applications
shown in these studies do not come up with a cloud-based
framework, and in such cases their capabilities of storage,
learning, and prediction are well limited by vehicle onboard
resources.
More relevant studies have been conducted very recently
by the authors of this study in the context of the Digital
Twin for connected vehicles. Wang et al. [37] proposed a
Digital Twin paradigm for an ADAS of connected vehicles.
In this paradigm, onboard devices on connected vehicles col-
lect and upload data to the cloud server through cellular-based
V2C communication, where the cloud server can create digi-
tal replicas of entities in the real world (i.e., roads, vehicles,
and drivers) based on the received data. All proposed models
and algorithms are applied to these digital copies with cloud
computing, where their results are propagated back to the real
connected vehicles through V2C communication for ADAS,
assisting the decision making of drivers in real time. A subse-
quent field implementation using this Digital Twin paradigm
was conducted by Liao et al. [38], where three human-driven
passenger vehicles performed ramp merging cooperatively,
showing the benefits of safety and environmental sustainability
compared to the traditional ramp merging scenario. A Digital
Twin simulation architecture was also proposed later to allow
researchers to implement this paradigm in a fully virtual envi-
ronment of connected and automated vehicles with the Unity
game engine [39].
Visualization of the Digital Twin information from the
cloud remains a challenging issue, where Liu et al. [40]
developed a data-fusion methodology to overlay the Digital
Twin information for the driver’s field of view with the help
of camera (RGB and depth) images, assisting the driver to
make lane-change prediction of neighboring vehicles. On top
of this study, Wang et al. [41] designed a cooperative driving
system for connected vehicles, where nonline-of-sight vehicles
are visualized as “Digital Twin slots” on the augmented reality
(AR)-based head-up display (HUD) of the ego vehicle, guid-
ing it to cross nonsignalized intersections without any collision
or unnecessary full stop. However, none of these recent stud-
ies, which are from the authors of this study as well, designs
a holistic system framework that connects mobility entities
(i.e., human, vehicle, and traffic) together, and neither do they
develop any cloud architecture with detailed data structures or
workflows.
It needs to be noted that, many studies consider the Digital
Twin simply as a high-fidelity modeling and simulation envi-
ronment of real-world entities. Although this statement is
partially correct, our understanding of the Digital Twin cov-
ers wider than merely modeling and simulation, namely,
sampling and actuation in the physical space, and storage,
modeling, learning, simulation, and prediction in the digi-
tal space. Related literature with the limited definition of the
Digital Twin is not reviewed in this study.
III. MOBILITY DIGITAL TWIN CONCEPT
The MDT framework proposed in this study, as shown in
Fig. 1, consists of three planes: 1) the lower plane, high-
lighted in yellow, stands for the physical space where human
beings, vehicles, and traffic infrastructures reside; 2) the upper
plane, highlighted in blue, represents the digital space where
the digital replicas of those physical entities are located at;
and 3) between these two planes, the communication plane
(in gray) plays a crucial role in this framework to allow real-
time and nonreal-time data streaming for both upstream and
downstream.
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WANG et al.: MOBILITY DIGITAL TWIN: CONCEPT, ARCHITECTURE, CASE STUDY, AND FUTURE CHALLENGES 17455
Fig. 1. Illustration of the MDT framework for connected vehicles, which consists of a physical space, a digital space, and a communication plane between
two spaces.
Three entities are considered in this MDT framework:
Human,Vehicle, and Traffic. Given the existence of the com-
munication plane, each of the entity can be connected to the
digital space (e.g., Internet) and exchanges data with each
other. Therefore, this MDT framework is a good representa-
tion of the IoT, and it allows connected vehicles to act as IoVs
with IoT technologies. In this section, we provide a deep dive
into this MDT framework regarding all three aforementioned
planes, introducing their building blocks with respect to their
concepts and functionalities.
A. Communication Plane and Data Workflow
The communication plane of this MDT framework sits
between the physical space and digital space, and it provides
seamless connections between these two spaces. This MDT
framework’s end-to-end process starts from sampling data in
the physical space, where all or part of the data is transmitted
upstream to the digital space via the communication plane.
Those data will go through one or multiple processes in the
digital space internally, including storage, modeling, learning,
simulation, and prediction, and the resulting data are transmit-
ted downstream to the physical space via the communication
plane. Those data, upon receiving, is applied by the actuators
of the physical space to fulfill the end-to-end process.
The major difference between a Digital Twin framework
with an iteration framework, or a model-based design frame-
work is that, a Digital Twin framework always maintains
synchronized versions of the physical system and its digital
counterpart [32]. In the proposed MDT framework of our
study, this is guaranteed by the communication plane between
the physical and digital spaces. Without the communication
plane, data cannot be transmitted between these two spaces to
enable their interactions and synchronizations, hence Digital
Twins cannot be formed.
Since cloud computing is leveraged in this MDT framework,
the digital space of the framework is deployed fully or par-
tially on the commercial and/or private cloud. Therefore, the
communication module needs to provide access to the cloud
for the physical space, which is either direct access or indirect
access (via edges). The MDT framework does not necessarily
require any specific wireless communication technology (ded-
icated short-range communication (DSRC) [42], C-V2X [43],
or something else in the future) to be served as the commu-
nication plane, as long as it can be applied to transmit data
between the physical space and the digital space.
B. Physical Space
If we consider this MDT framework as an end-to-end frame-
work, then the physical space is in charge of both ends of this
framework, namely, sampling and actuation. We assume no
(or only minimal) computing work needs to be conducted in
the physical space, since all (or majority) of that is offloaded
to the digital space through the communication plane.
For sampling, sensors in the physical space detect the
dynamic status, operating process, or event occurrences, and
then aggregate these measurements under various resolutions
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17456 IEEE INTERNET OF THINGS JOURNAL, VOL. 9, NO. 18, 15 SEPTEMBER 2022
for their transmission to the digital space. On the other hand,
once the processed results are received from the digital space,
actuation can be made by physical entities to fulfill this end-to-
end framework. Generally, the physical space is defined on a
world coordinate, which may contain all the transportation-
related physical entities, and can be classified into three
building blocks: Human,Vehicle, and Traffic.
1) Human: In this framework, all human beings involved
in the transportation system are considered, which include not
only drivers but also passengers, pedestrians, cyclists, etc. The
sampling process can be accomplished by the human–machine
interface in an active manner, or by the in-cabin status sens-
ing (e.g., camera, seat sensor, etc.), human wellness monitor
(e.g., smartwatch, electrocardiogram, etc.), and other percep-
tion sensors in a passive manner. The preferences of a human’s
behavior can also be set actively (e.g., a driver manually sets
the preferred cruise control speed), or be measured passively
(e.g., a pedestrian’s preferred trajectory of crossing a cross-
walk is recorded by the vehicle/intersection camera), where
both of them are considered as the sampling process.
The actuation process of the Human block in this MDT
framework is mainly conducted by drivers. In the foreseeable
future, our transportation system will remain in a mixed auton-
omy traffic environment, where only part of all vehicles will be
fully autonomous vehicles (with SAE level-5 automation), but
the majority are still driven by human drivers (with no degree
or a certain degree of automation). Therefore, if drivers can be
provided with additional information from the digital space of
this MDT framework, such as adjacent vehicle’s lane-change
possibility [44] or upcoming signal phase and timing [45],
their actuation will be more accurate and in turn benefit other
entities in the transportation system.
2) Vehicle: Vehicle is the core of this MDT framework, as it
is the host of drivers and passengers, and also the fundamental
component of traffic. As can be seen from Fig. 1, all modules
in the physical space, not only the ones in the Vehicle block
itself but also those in the Human block and the Traffic block,
are serving for vehicle-related activities.
Specifically for the Vehicle block, the localization module
(i.e., GNSS), the perception sensors (i.e., ultrasonic, camera,
radar, and/or LiDAR), together with vehicle CAN BUS are in
charge of the sampling process. Related data, such as posi-
tions, speeds, and accelerations of the ego vehicle and its
surrounding vehicles can be sampled from these physical com-
ponents, and then be propagated to the digital space through
communication.
The actuation process of the Vehicle block in this MDT
framework is conducted by the vehicle steering system, accel-
erator, and brake. These physical components are able to
actuate any lateral or longitudinal control command received
from the digital space, and therefore allow the vehicle to
achieve its desired motion.
3) Traffic: Many existing intelligent vehicle platforms and
applications, such as ADAS or autonomous driving systems
(ADSs), only focus on their performances on the ego vehi-
cle without considering their interactions with the large-scale
traffic network. However, as can be seen from Fig. 1, Traffic
is indeed a crucial building block of our MDT framework for
connected vehicles. The beneficiaries of this MDT framework
include not only connected vehicles and their occupants but
also the whole traffic network on a wider scope.
Particularly, the Traffic block in the physical space includes
various traffic infrastructures, such as traffic signals, roadside
units, camera/radar/loop detectors, and electronic traffic signs.
These physical components are able to either generate data
(e.g., signal phase and timing) by themselves, or measure data
(e.g., traffic count and traffic flow) generated by other traffic
entities. Such data are sampled and sent to the digital space
through the communication plane, benefiting other building
blocks of this MDT framework.
On the other hand, guidance or adjustment received from
the digital space can also be actuated by the Traffic block
to improve the safety and efficiency of the large-scale traf-
fic network. For example, the signal phase and timing of
traffic lights can be adjusted to better serve different traf-
fic flows under different situations [46]. Guidance or warning
information can be broadcast to connected vehicles via road-
side units, and to all traffic entities via electronic traffic
signs.
C. Digital Space
The aforementioned physical space of this MDT framework
handles both ends of this end-to-end framework (i.e., sam-
pling and actuation). On the other hand, the digital space is in
charge of the processes between both ends: storage, modeling,
learning, simulation, and prediction.
One of the biggest strengths of this MDT framework over
traditional mobility system frameworks is the data lake, which
is a centralized repository that allows structured or unstruc-
tured data at any scale to be stored. Traditionally, mobility data
measured by a physical entity is only saved in its onboard data
storage due to the lack of communication capability. Such data
are only used for the physical entity itself without being shared
with other entities, and will be wiped out once the maximum
size limit of the onboard data storage is met. However, with the
proposed MDT framework, mobility data measured by Human,
Vehicle, and Traffic blocks in the physical space can be trans-
mitted to the digital space through the communication plane,
and stored in the data lakes of associated Digital Twins for
future use. Such data can be used for the microservices not
only in the original mobility block but also in other blocks
(e.g., traffic signal data measured by the Traffic block can be
used for both the “real-time monitoring” microservice in the
Traffic Digital Twin and the “cooperative control” microservice
in the Vehicle Digital Twin).
Note that there exists a misunderstanding about the Digital
Twin in the research community, where some simply con-
sider Digital Twin technology as a modeling and simulation
technology. In our MDT framework, modeling and simula-
tion are part of the digital processes that are enhanced by
the data lake and data sharing in the digital space, where co-
simulation platforms can be built to synchronize data from
multiple simulators (such as the Unity-SUMO-AWS integrated
platform [47]). However, as shown in Fig. 1, our MDT frame-
work is more than just modeling and simulation, where other
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WANG et al.: MOBILITY DIGITAL TWIN: CONCEPT, ARCHITECTURE, CASE STUDY, AND FUTURE CHALLENGES 17457
digital processes (i.e., storage, learning, and prediction) play
equivalently crucial roles in the digital space. All of the
aforementioned digital processes can be applied to mobility
microservices, and they are realized in a more powerful, share-
able, manageable, and extendable manner by leveraging cloud
computing and edge computing.
1) Human Digital Twin: Human Digital Twins are digital
replicas of real humans in the physical space. This build-
ing block in the digital space has a human data lake that
stores all data sampled from the Human block in the physical
space, where different humans have their personal databases
to be differentiated from others. With real-time data sam-
pling and historical data storage, the Human Digital Twin is
able to classify drivers into specific driver types by machine
learning algorithms like k-nearest neighbors (KNNs), and
to provide guidance in a customized or personalized man-
ner [48]. Taking advantage of the data coming from the
Vehicle block, the Human Digital Twin can also predict future
behaviors of drivers (e.g., lane-change intention [49]) and
detect their anomalies [50]. The results of the aforementioned
microservices can be applied to third parties such as insur-
ance companies, where they can further build a microservice
to set the insurance pricing for different drivers based on their
driving behaviors [51].
2) Vehicle Digital Twin: Vehicle Digital Twins are the dig-
ital replicas of real vehicles in the physical space. Once
the sampled data are received from a connected vehicle in
the physical space, it can be saved in this particular vehi-
cle’s data lake with a unique identification number. Those
data in the Vehicle Digital Twin about the ego vehicle (e.g.,
position, speed, and acceleration) and its surrounding envi-
ronment (perceived by perception sensors) can also be shared
with the Human Digital Twin,theTraffic Digital Twin,or
other connected vehicles’ Vehicle Digital Twins for various
microservices.
With massive data storage and data sharing in the dig-
ital space, multiple vehicle-related microservices can be
enabled, such as the ones requiring cooperation among
multiple connected vehicles: cooperative localization, coopera-
tive perception, cooperative planning, and cooperative control.
Additionally, microservices that need time-series data can also
be benefited from this MDT framework, where one typical
example is predictive maintenance: Based on modeling and
simulation of the time-series vehicle data that is sampled from
the Vehicle block in the physical space and stored in the Vehicle
Digital Twin, the learning process can be conducted in the
digital space and predictions can be made regarding poten-
tial failures of vehicle components at a future time [52]. Such
prediction results can be used by the vehicle owner or manu-
facture to schedule onsite maintenance before the components
break down.
3) Traffic Digital Twin: Traffic Digital Twins are the digital
replicas of traffic infrastructures, which receive data from the
Traffic block in the physical space. Such sampled data, such as
signal phase and timing, traffic count, and traffic flow, can be
stored in the traffic data lake for future reference. It can also
be used for multiple traffic microservices in real time, such as
monitoring the traffic condition [18], variable speed limit [53],
routing and navigation [54], ridesharing planning [55], and
parking management [56].
Similar to the Human Digital Twin and Vehicle Digital
Twi n,theTraffic Digital Twin can be enhanced by the com-
munication among these Digital Twin blocks. For example,
the microservice of routing and navigation can be carried
out solely by the real-time traffic flow data sampled from
camera/radar/loop detectors in the real world. However, they
can be further enhanced if behavior preferences are set by
the Human block and predictions are made by the Human
Digital Twin (e.g., a driver/passenger always goes to gro-
cery stores when his/her commute route is highly congested).
Additionally, if the Vehicle block detects the fuel/battery level
is low and sends that to the Vehicle Digital Twin,itcan
also assist the routing and navigation microservice to find a
gas/charging station near a user-preferred grocery store along
the original route.
IV. EXAMPLE ARCHITECTURE WITH
CLOUD–EDGE COMPUTING
In this section, we build an example architecture for the
proposed MDT concept by leveraging cloud computing and
edge computing, enabling both real-time and bulk-batch inges-
tion, processing, and analytics of mobility data. As shown
in Fig. 2, the architecture can be divided into four layers:
1) the cloud layer, which is built on AWS and its Virtual
Private Cloud (VPC) [57]; 2) the edge layer, which has a
computing component, a communication component, and a
storage component; 3) the device layer, which generates data
and consumes guidance; 4) the API (Application Programming
Interface) layer, which hooks up the cloud layer with external
APIs.
The major purpose of designing this architecture is to
accommodate the proposed MDT framework shown in Fig. 1,
so the Digital Twin does not just remain in the conceptual
phase but can also be deployed in the real world with the
help of cloud computing and edge computing. Particularly,
the physical space of the MDT framework shown in Fig. 1
is represented by the device layer of this example architec-
ture, where mobile apps, simulators, real vehicles and radio
control (RC) vehicles sample data from Human,Vehicle and
Traffic, and also actuate commands received from the edge and
cloud layers. The communication plane of the MDT frame-
work is positioned within the edge layer’s communication
component of this example architecture. The digital space of
the MDT framework, along with its data lakes and microser-
vices, spans the whole part of the cloud and API layers, as
well as part of the edge layer (except for its communication
component).
As mentioned in Section II, the Digital Twin concept is
sometimes inadequately considered as a high-fidelity modeling
and simulation environment by many studies, since none
of these studies fulfilled all functionalities of the Digital
Twin concept (storage, modeling, learning, simulation, and
prediction) by building a cloud–edge architecture. Although
the example architecture built in this study is not the sole
solution, it does provide an idea of deploying the Digital
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17458 IEEE INTERNET OF THINGS JOURNAL, VOL. 9, NO. 18, 15 SEPTEMBER 2022
Fig. 2. Example architecture of the proposed MDT with the cloud layer, edge layer, device layer, and API layer.
Twin concept in the real world. Throughout the rest of this
section, various modules in this architecture are explained in
corresponding sections sorted by the layers they are in.
A. Cloud Layer
In Amazon VPC of the cloud layer, there are five dif-
ferent modules: 1) distributed real-time processing; 2) data
stores; 3) analytics workbench; 4) rule engine; and 5) AI/ML
framework & Digital Twin microservices. Specifically, in
Distributed Real-time Processing, we leverage existing tools
like Amazon EKS [58], Apache Kafka [59], and Apache
Storm [60] to provide real-time processing and analytics. The
Analytics Workbench is the workhorse for big data analytics. It
consists of OpenTSDB, which is a distributed, scalable, time-
series database built on top of Hadoop and HBase [61]. This
supports a writing rate of up to millions of entries per second,
supports data storage with millisecond-level precision, and
preserves data permanently without sacrificing precision. In
addition, Apache Spark [62], a distributed processing system,
is used to conduct predictive analytics using Amazon EMR
clusters [63].
Rule Engine service evaluates the rules configured for enti-
ties (e.g., humans and vehicles) on the data received from
the Kafka queue, and redirects it to AI/ML Framework and
Digital Twin Microservices based on the rule validation result.
AI/ML Framework and Digital Twin Microservices are the
core of this cloud–edge architecture, where end users are able
to implement customized algorithms and applications with
various objectives. This module processes time-series data
sent from the physical space using statistical techniques, and
sends guidance back to the entities in the physical space. The
data workflow is triggered via Apache Airflow, an opensource
workflow management platform.
Data Stores of our cloud–edge architecture are made up
with: 1) Amazon S3, a scalable storage infrastructure to build
our Digital Twin data lakes [64]; 2) Amazon DocumentDB
(with MongoDB compatibility), a database service that is
purpose-built for JSON data to execute flexible, low latency
queries to obtain a near real-time record of events in par-
allel on a massive scale [65]; and 3) Redis, an opensource,
highly replicated, nonrelational kind of database and caching
server [66].
Outside of Amazon VPC but still inside of the cloud
layer sits AWS IoT Core [67], which enables the connection
between IoT devices (such as mobile apps, simulators, real
vehicles, and RC vehicles in this study) and AWS cloud with-
out the need to provision or manage servers. It supports various
devices and messages, and can process/route those messages
to AWS endpoints/devices reliably and securely. A Bulk Data
Ingestion module is also developed in this cloud–edge archi-
tecture, enabling the ingestion of terabytes of data in batch
mode into our data lakes. Some scenarios where this module
can be triggered are: 1) end-of-vehicle-trip bulk data inges-
tion; 2) periodic bulk data ingestion; 3) event-triggered data
ingestion; and 4) in-vehicle data logging. OpenID Connect is
a simple identity layer on top of the OAuth 2.0 authorization
protocol, which is adopted in this cloud–edge architecture to
verify the identity of end users based on the authentication
performed by an authorization server, as well as to obtain the
basic profile information about end users [68].
B. API Layer
External data sources can be integrated into this architecture
through the API layer, which enriches the functionalities of
digital microservices. The API layer is connected with the
cloud layer of this example architecture through Amazon API
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WANG et al.: MOBILITY DIGITAL TWIN: CONCEPT, ARCHITECTURE, CASE STUDY, AND FUTURE CHALLENGES 17459
Gateway, which is an AWS managed service that can create,
publish, maintain, monitor, and secure APIs at any scale [69].
The API Gateway may act as the “front door” for applications
to access data or functionalities from our back-end services.
In the API layer, traffic data (TomTom [70]), map data
(OpenStreetMap [71]), and weather data (OpenWeather [72])
can be connected to Amazon API Gateway via HTTPS. With
such data, more microservices can be deployed in the Traffic
Digital Twin of the proposed MDT framework, and hence
provide better guidance toward humans and vehicles in the
physical space. Additionally, a customized Event Query API is
developed to receive notifications of events and fetch data from
the cloud layer. Examples of using this API are event-triggered
detection of anomalous driving behavior, and notification of
data ingestion in the cloud layer at the end of a trip. A
customized Web portal is designed to visualize the digital
processes on the cloud, and it enables end users to create and
modify microservices (to be shown in Section V).
C. Edge Layer
Although the cloud layer of this example architecture is
designed to accommodate most of the digital processes of the
proposed MDT framework, this does not necessarily indicate
all digital processes shown in Fig. 1 must sit on the cloud. In
fact, it gets difficult sometimes for connected vehicles to have
the cloud access, since they continuously move around and
may lose Internet connection every now and then. Therefore,
a hybrid approach with cloud computing and edge comput-
ing can meet the requirements of ultralow latency for running
safety-critical microservices at the edge (e.g., road-side units),
and of extensive resources for running data-driven microser-
vices on the cloud [27]. Edge computing has already been
widely researched by various works in the field of connected
vehicles [73], [74], and also deployed in the real world in
projects like 5G-MOBIX [75] and initiatives like automotive
edge computing consortium (AECC) [76].
In this example architecture, an edge layer is designed
with three different modules: computing, communication, and
storage.
1) The computing module provides a hierarchical com-
puting platform for connected vehicles, where hetero-
geneous computing nodes (e.g., microprocessor, GPU,
and TPU) are responsible for processing a variety of
tasks offloaded by vehicles. Sophisticated middleware
mechanisms are designed for determining which type of
computing nodes should be selected to handle specific
tasks and services.
2) The communication module enables real-time data
exchange among the cloud, edge, and device layers.
The protocols implemented in the communication mod-
ule are HTTPS and two lightweight publish-subscribe
network protocols, MQTT and Zenoh [77]. Zenoh has
been designed to address the needs of applications that
deal with data in movement, data at rest and computa-
tion in a scalable, efficient and location transparent data
manner.
3) The storage module is capable of caching temporary
data. The cached data could be either downloaded from
the cloud layer or uploaded from the device layer. Some
representative mobility applications that could benefit
from the storage module include OTA (i.e., caching the
update content distributed by the cloud at the edge) and
crowdsourcing high-definition (HD) map (i.e., caching
uploaded sensor data from vehicles to update the local
map fragment accordingly at the edge). In our case study
shown in Section V, the storage module of the edge layer
also plays a crucial role to store the raw CAN BUS data
of the vehicle.
D. Device Layer
The device layer of this example architecture is shown on
the right side of Fig. 2, which stands for the Human,Vehicle,
and Traffic building blocks in the physical space of the MDT
framework. Mobile apps are designed for both Android and
iOS, where end users’ position and speed data (measured
by GPS and gyroscope) can be uploaded to AWS IoT Core
directly via MQTT in real time.
Real vehicles and RC vehicles are the major data sources in
the device layer of this example architecture. Dynamics data
from CAN BUS (or ROS2 for RC vehicles), position data from
the localization module, together with perception data from
perception sensors (e.g., camera, radar, LIDAR), is sampled
in real time and pushed to the edge layer. On the other hand,
guidance from the digital space of the MDT framework is
received from the edge layer, and gets actuated on real vehicles
and RC vehicles.
External simulators, such as MATLAB [78], microscopic
traffic simulators SUMO [79] and PTV VISSIM [80], and
game engine-based simulators CARLA [81] and Unity [82],
are used in this architecture to emulate the physical devices of
the MDT framework. Note these simulators in the device layer
only play the role as physical entities in the physical space,
i.e., only for sampling and actuation processes. However, sim-
ulators can also be deployed in the cloud layer or the edge
layer to play the role as digital entities, i.e., for modeling,
learning, simulation and prediction.
V. EXAMPLE MICROSERVICES AND CASE STUDY
This section first introduces example microservices of each
digital building block of the proposed MDT framework,
including user management and driver type classification of the
Human Digital Twin, cloud-based ADAS of the Vehicle Digital
Twi n, and traffic flow monitoring and variable speed limit of
the Traffic Digital Twin. Then, a case study of the P-ACC
system is conducted, which integrates aforementioned example
microservices into one application. Real-world experimental
results showcase the effectiveness of P-ACC while functioning
in the MDT framework, compared to traditional ACC systems
that purely rely on onboard sensing and computing.
A. Human Digital Twin: User Management and Driver Type
Classification
This section introduces example microservices of the
Human Digital Twin. A Web portal is built to enable various
data management and visualization functionalities, where
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17460 IEEE INTERNET OF THINGS JOURNAL, VOL. 9, NO. 18, 15 SEPTEMBER 2022
Fig. 3. Real-world demonstration in Mountain View, CA of the cloud-based
driver scoring system, used for user management and driver type classification.
related data sampled in the physical space is transmitted and
visualized on the Web portal through HTTPS.
To begin with, each human user needs to register a unique
account of the MDT through the Web portal, and also to
specify the group(s) he/she belongs to: “viewer,” “super-
visor,” and/or “admin.” Particularly, the user account that
belongs to the “admin” group is granted full access to the
human data lake, which can register/delete any account in
the digital space, alter the group(s) any account belongs
to, and subscribe/unsubscribe microservices for any account.
“Supervisor” account can only access its own human data
lake with modification right, while “viewer” account has no
modification right at all.
Human–vehicle association is also available through the
Web portal, which enables the human user to associate his/her
vehicle in the vehicle data lake, building the connection
between the Human Digital Twin and the Vehicle Digital Twin.
Therefore, once the user account is registered, all the data gen-
erated by this human user in the physical space will be stored
both in the user data lake (under the particular user ID) and
in the vehicle data lake (under the particular vehicle ID).
Besides the aforementioned details regarding user manage-
ment, the built-in microservices of the Human Digital Twin
can also be applied. One example is shown as Fig. 3, where
different driving scores of a driver (i.e., overall score, eco
score, safe score, and comfort score) are calculated by the
opensource MOVESTAR model [83] running on the cloud in
real time. These driving scores can be further compared with
the historical data, which is generated by other drivers and
stored in the human data lake to classify this driver into a
certain type. An example classification result is visualized on
the Web portal (currently shown as “Competent” in Fig. 4),
which can be used for other microservices, such as behav-
ior prediction, personalized guidance, and insurance pricing.
The historical classification results of this driver can also be
retrieved by clicking the detailed trip list shown in Fig. 4,
which further validates the power of the Human Digital Twin
in terms of storage.
B. Vehicle Digital Twin: Cloud-Based ADAS
This section introduces example microservices of the
Vehicle Digital Twin through a cloud-based ADAS, where
cloud computing is leveraged to provide visualization guid-
ance and control commands toward connected vehicles. As
Fig. 4. User management page on the Web portal of the MDT, showing user’s
driver risk class (among reckless, intermediate, and competent), associated
vehicle (currently and historically), and past trips with clickable links (in
light blue fonts) for more details.
Fig. 5. Human-in-the-loop simulation of the cloud-based ADAS based on
AWS, the Unity game engine, and the Logitech G29 Driving Force.
shown in Fig. 5, a human-in-the-loop simulation (built with
AWS, the Unity game engine, and the Logitech G29 Driving
Force) is conducted to emulate the data sampling process from
a connected vehicle in the physical space [39]. When a human
driver manually controls the vehicle, its data are sampled from
the physical components (e.g., CAN BUS, radar, camera) and
uploaded to the data lake of the Vehicle Digital Twin.This
process is shown as the lower-left corner “Unity-AWS Uplink
Message” of Fig. 5.
The data stored in the data lake (potentially from all his-
torical trips) of this vehicle is inputted to microservices of
the Vehicle Digital Twin (e.g., cooperative planning, coop-
erative control, etc.). Machine learning-based algorithms are
implemented to learn the performance and preference of
each vehicle and/or driver, where the algorithm outputs may
include prediction/guidance of their current status and future
behaviors.
Such algorithm outputs are downloaded from the Vehicle
Digital Twin to the vehicle, shown as the lower-right corner of
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WANG et al.: MOBILITY DIGITAL TWIN: CONCEPT, ARCHITECTURE, CASE STUDY, AND FUTURE CHALLENGES 17461
Fig. 6. Prediction and guidance information received from the Vehicle Digital
Twi n on the cloud is visualized through an AR HUD, which may include:
driving proficiency score and its trend, potential action (e.g., hard braking or
lane change) and its possibility, as well driving mood score.
Fig. 5 “AWS-Unity Downlink Message.” By leveraging com-
puter vision technologies, this information is overlaid on top
of each vehicle through an AR HUD design, assisting the deci-
sion making of other drivers [84]. As shown in Fig. 6, from
this driver’s field of view, the following information of the
surrounding vehicles and their drivers can be known (from
top to bottom of the overlaid information): driving proficiency
score and its trend, potential action (e.g., hard braking or lane
change) and its possibility, as well as driving mood score.
Compared to a traditional ADAS that relies on pure onboard
sensing and processing of the ego vehicle, the key advantages
that MDT brings to this cloud-based ADAS are:
1) Heavy computations, such as training a machine learning
algorithm based on the sampled data, can be offloaded
to the cloud to utilize more computing power and hence
saves time.
2) Additional data sources in the physical space, such as
surrounding vehicles and downstream traffic, can be
utilized to enhance the functionalities of ADAS.
3) The cloud-based ADAS can be easily migrated through
the cloud and hence increases accessibility, which can
be available on various vehicles for the same driver, or
for various drivers on the same vehicle.
4) Updates of ADAS algorithms and applications can
be conducted much quicker and easier through OTA
updates.
C. Traffic Digital Twin: Traffic Flow Monitoring and
Variable Speed Limit
This section introduces example microservices of the Traffic
Digital Twin: traffic flow monitoring and variable speed limit.
A mobile app is built (both in iOS and Android versions) to
allow end users to upload their data from the physical space to
the digital space, which may include latitude, longitude, and
speed. At the initialization step of the app, the user is asked to
associate with a vehicle in the digital space, and also set the
data push rate. Once “START” button is pressed, the app will
get data from the mobile phone, and push that to the Vehicle
Digital Twin through AWS IoT Core. This app is adopted to
generate traffic flow and hence represents connected vehicles
traveling in the physical space.
Fig. 7. (a) Traffic flow monitoring page of the Web portal (digital space) that
visualizes the data received from end users (physical space) and (b) Variable
speed limit based on geo-fences.
When the traffic flow monitoring microservice is turned on,
it gets the data from all Vehicle Digital Twins and aggregates it
in the Traffic Digital Twin. A demonstration of this microser-
vice running in motion is shown in the snapshot Fig. 7(a). The
count of all vehicles running on the specific link is calculated,
and then gets further divided by the link length and number
of lanes to get the traffic density value. This value is com-
pared with the predefined ranges of heavy congestion (red),
moderate congestion (orange), and no congestion (green) to
determine the traffic density on that link and gets represented
in the corresponding color on the map. Additionally, the dif-
ferent colors of vehicles visualized on the map indicate their
current speeds, where the ones traveling equal or below the
average link speed are shown in blue color, and the ones above
the average link speed are in white.
In order to better manage traffic flows in the physical
space, variable speed limits can be applied to connected vehi-
cles through the Traffic Digital Twin. As can be seen from
Fig. 7(b), users can easily draw a geo-fence by specifying
its name, radius, latitude, and longitude of the center loca-
tion. A hexagon will then be visualized on the map, and users
are prompted to choose the rules that are applied to vehicles,
together with their states (“entry” or “exit”) and correspond-
ing values. Users can customize the settings of the variable
speed limit on the Web portal, for example, setting the rule
state entry with a value “40” and another rule state exit with
a value of “60.” This setting indicates that all connected vehi-
cles which are subscribed to this microservice will receive
a speed recommendation/limit of 40 mph when entering this
geo-fence, and 60 mph while exiting.
D. Personalized Adaptive Cruise Control With
Human/Vehicle/Traffic Digital Twins
This section conducts a case study of P-ACC, a system
which integrates aforementioned example microservices from
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17462 IEEE INTERNET OF THINGS JOURNAL, VOL. 9, NO. 18, 15 SEPTEMBER 2022
Fig. 8. P-ACC system integrates microservices from the driver digital twin
(yellow), the vehicle digital twin (orange), and the traffic digital twin (red).
Human,Vehicle, and Traffic Digital Twins. Traditional ACC
systems allow the ego vehicle to travel at a set speed and/or
maintain a desirable gap with its preceding vehicle with the
help of ranging sensors like radars, however, they require
drivers to manually adjust speed and gap settings based on
dynamic environments [85]–[87]. They do not consider each
driver’s personalized driving style, and cannot account for the
changes of environmental factors, such as traffic conditions,
road types, and weather types [88].
As shown in Fig. 8, the P-ACC system is proposed with an
edge layer on the vehicle and a cloud layer with AWS. While
the computer on the edge layer (i.e., Nvidia Jetson TX2) con-
ducts some basic edge computing tasks to filter the raw data
generated by the vehicle CAN BUS, most computing tasks are
offloaded from the edge layer to the cloud layer (i.e., AWS),
where the Human/Vehicle/Traffic Digital Twins get deployed.
Once the personalized driving data sampled from a driver’s
naturalistic car-following behavior gets filtered on the edge
layer and uploaded to the cloud layer, it is further classified
by the following Digital Twins.
1) The Human Digital Twin, with the example microser-
vice of driver type classification (aggressive, neutral,
conservative, etc.).
2) The Vehicle Digital Twin, with the example microser-
vice of vehicle type classification (ego/preceding vehicle
being a sedan, SUV, truck, etc.).
3) The Traffic Digital Twin, with the example microser-
vices of weather type classification (sunny, cloudy, rainy,
snowy, foggy, etc.), road-type classification (rural, urban,
highway, etc.) and traffic-type classification (congested,
moderate, light, etc.).
The aforementioned classifications can be conducted either
by simple rule-based algorithms or complex machine learn-
ing algorithms [48]. Once the driving data are classified into
various clusters, the data in each cluster can be used by
machine learning algorithms to train a personalized driving
model. In this case study, the maximum entropy inverse rein-
forcement learning (Max-Ent IRL) algorithm [89] is adopted,
where the probability of a trajectory is proportional to
the sum of the exponential rewards accumulated along the
Fig. 9. (a) Mass-produced vehicle that is used for the end-to-end latency
test and (b) 10-min trip conducted on Highway 237 in California to collect
CAN BUS data from the vehicle.
trajectory
p(ξ |α) =1
Z(α) exp
t
Rα(st)
=1
Z(α) exp
t
αT(st)(1)
where Z(α), called partition function, equals to
ξexp(tRα(t)). To recover the reward function, the
maximum log likelihood method is used at the demonstration
trajectories with respect to the weight of the reward function.
L(α|ξ) =max
α
ξ
log p(ξ|α)(2)
Then, the gradient of the weight αcan be written in the
following form:
∇αL=
ξ
p(ξ )
s∈ξ
(s)−
ξ
Ds
s∈ξ
(s)(3)
The first term ξp(ξ ) s∈ξ(s)=˜
fis named expected
feature count. The second term ξDss∈ξ(s)=¯
fis
named empirical feature count, and Dsis the state visitation
frequency.
Based on the recovered reward function from the Max-Ent
IRL algorithm, the particular driver’s desired car-following gap
(e.g., 70 m) at each speed value (e.g., 35 m/s) can be calculated
by the following equation and then put into a look-up table:
ddes(v)=arg max
gr(v)(4)
This ddes-vtable is the trained driving model for the certain
cluster of driving data, which can be inferred in real time once
the driver/vehicle/weather/road/traffic type match.
A real-world end-to-end test is conducted, which utilizes the
CAN BUS data generated from a Lexus LS test vehicle shown
as Fig. 9(a), including 15 data fields, such as position, speed,
acceleration, and so on. Time-series data generated by natural-
istic driving [i.e., Fig. 9(b)] gets filtered on the edge and sent
to AWS through edge communication module every 10 min.
When the driver turns on the ACC feature, the cloud layer
infers the particular driving model trained with the data in the
particular driver/vehicle/weather/road/traffic type, and pushes
that model to the vehicle through the edge communication
module. ACC at this time will satisfy this driver’s personalized
car-following preference, and hence acts as a P-ACC.
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WANG et al.: MOBILITY DIGITAL TWIN: CONCEPT, ARCHITECTURE, CASE STUDY, AND FUTURE CHALLENGES 17463
TAB L E I
TAKEOVER RESULTS OF P-ACC COMPARED TO TRADITIONAL ACC
(PERCENTAGE OF TAKEOVER DURATION DURING A TRIP)
TAB L E II
LATENCY RESULTS OF PROCESSING 10-MIN CAN BUS
DATA AND QUERYING THE DATA LAKES
The benefit of the proposed P-ACC with the MDT frame-
work is quantitatively measured using the takeover percentage.
Takeover denotes the status where the driver steps onto the
acceleration pedal or brake pedal when he/she feels uncom-
fortable. The takeover percentage is the takeover time divided
by the overall P-ACC (or ACC in the baseline) activation time,
and the results are presented in Table I. Based on the test
results from three different drivers in different environments,
the takeover event happens 86.4% less frequently when drivers
use the proposed IRL-based P-ACC, compared to a tradi-
tional PID-based ACC that purely relies on real-time onboard
computing.
The end-to-end latency results are shown in Table II, indi-
cating that under different sampling frequency of the CAN
BUS data, the uplink latency is relatively proportional to the
uplink batch size, with a minimum of 1.4 s for 12 MB and
a maximum of 21.1 s for 240 MB. However, the majority
of the end-to-end latency is made up by the aforementioned
cloud computing processes, which roughly takes 16 s under
different data frequency. This means once the P-ACC feature
is switched on by a driver, it needs a 16 s warm-up period to
get the corresponding IRL-based personalized model. During
this warm-up period, the baseline PID model can be run to
guarantee car-following safety.
In a nutshell, the end-to-end testing showcases the capa-
bility of the proposed MDT framework to execute complex
cloud computing processes based on bulk uplink data within
a rational range of latency, and the computing results improve
the performance of commercially available vehicle applica-
tions. In real-time mobility applications like the ones shown
in the Human Digital Twin as Fig. 3, and in the Vehicle Digital
Twi n as Figs. 5 and 6, where only a limited number of cloud
computing processes are executed based on the uplink data
stream, our MDT framework guarantees 80 ms as the medium
end-to-end latency to enable safety-critical and time-critical
applications [37].
VI. FUTURE CHALLENGES
In the future development and utilization of the Digital Twin
technology in both academia and industry, together with the
involvements of the connected vehicle technology and cloud–
edge computing, numerous challenges need to be addressed
from the perspectives of both research and engineering. Some
of the major challenges are discussed in this section with open
questions for future studies to solve.
A. Digital Twin Standardization
Although Digital Twin technology has gained momen-
tum in various domains during the past decade, there is
no universal definition of this technology, let alone exist-
ing standardization. Currently, the joint technical committee
“IoT and Digital Twin” of the International Organization for
Standardization (ISO) and the International Electrotechnical
Commission (IEC) is still developing the standards for the
Digital Twin in terms of concepts and terminology [90], as
well as use cases [91]. Additionally, the specific standards
for the Digital Twin manufacturing framework are also under
development by the ISO technical committee “Industrial Data,”
with focuses on overview and general principles [92], refer-
ence architecture [93], digital representation of manufacturing
elements [94], and information exchange [95].
Similar to manufacturing, specific standards need to be
developed for the transportation domain, so Digital Twin tech-
nology can be fully deployed on the connected vehicles we
will be riding in the future. Such standards can be used by
different organizations to define APIs for Digital Twin data
access, enabling different transportation entities (e.g., human
drivers, vehicles, and traffic infrastructures) to securely and
reliably store, manage, and retrieve records. Related standards
can also help developers design human machine interfaces
(HMIs) to enable better interactions between physical and
digital spaces of the Digital Twin.
However, the standardization of the Digital Twin in the
transportation domain can face numerous challenges, since
the consensus may be difficult to reach across the public
sector (e.g., transportation agencies) and the private sec-
tor (e.g., automotive manufacturers, suppliers, and network
providers), similar to the everlasting debate between DSRC
and Cellular Vehicle-to-Everything (C-V2X) communication
for the communication technology of connected vehicles.
B. AI for Cloud–Edge Computing Systems
Breakthroughs in AI techniques, including advanced
machine learning algorithms and the availability of high
performance computing platforms, have recently received
much attention as a key enabler for edge AI, next-generation
cloud computing, and large-scale intelligent transportation
systems. Meanwhile, machine learning techniques, such as
federated learning, provide many new opportunities in the way
we optimize complex systems and manage different connected
services and system resources [96], [97]. However, the evo-
lution toward learning-based cloud–edge computing systems
is still in its early stage, and the realization of its promised
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17464 IEEE INTERNET OF THINGS JOURNAL, VOL. 9, NO. 18, 15 SEPTEMBER 2022
benefits in our proposed MDT framework requires thorough
research and development.
For example, directly applying AI techniques in system
management might lead to significant performance degrada-
tion due to the unpredictable management actions during the
training phase. Therefore, fundamental questions such as how
AI techniques can significantly complement the system design
and management of the MDT framework still remain. Other
open research challenges are how to collect high-quality data
that contain both system profiles and performance metrics for
training high-fidelity Digital Twins; how to design AI and
machine learning solutions with high generalization ability
that can adapt in the high-variance and dynamic traffic cir-
cumstances; and how to enhance the robustness of AI-based
Digital Twin methods in real-world environments.
C. Public Cloud or Private Cloud
In this study, a public cloud–edge architecture has been
designed and deployed on AWS. However, this does not neces-
sarily mean our proposed MDT framework can only work on
AWS instead of other cloud platforms. Other commercial plat-
forms like Microsoft Azure (especially with its “Azure Digital
Twins”), GCP and Alibaba Cloud could also be the alternatives
to accommodate the MDT framework.
Nevertheless, a cloud platform must be trustworthy to
deploy any of the microservices mentioned in this study (espe-
cially for the ones related to the Human Digital Twin), as end
user information and related data need to be secured from
being compromised. The nature of public cloud platforms
inevitably gives away the control of resources to some extent,
which may introduce cybersecurity and privacy risks for end
users.
A private cloud platform, on the other hand, consists of
cloud computing resources used exclusively by one business or
organization. Since the services and infrastructure are always
maintained on a private network without sharing with others,
a private cloud platform can address the security and privacy
issues faced by a public cloud platform. Private cloud plat-
forms also make it easier for end users to customize cloud
resources to meet specific requirements and implement spe-
cific functions, which was proved in our private cloud-based
cooperative ramp merging experiment [38].
Although private cloud platforms are more secured and flex-
ible, it has several disadvantages compared to public cloud
platforms. In general, private cloud platforms are more expen-
sive, since hardware and software should be dedicated solely
to particular organizations that they serve (and hence paid
solely by those organizations). In terms of scalability and
reliability, private cloud platforms are also outperformed by
public cloud platforms, because the public ones provide on-
demand resources to meet various organizations’ needs, and
also provide a vast network of servers to ensure against failure.
Therefore, based on specific requirements and needs of
end users, choices can be made between public and private
cloud platforms, considering their advantages and disadvan-
tages described above. Additionally, building Digital Twins
with a hybrid cloud approach (combining public and private
cloud platforms, potentially with edge/fog computing) pro-
vides another possibility.
D. Heterogeneous Wireless Communication Environment
Future vehicular networks tend to be highly heterogeneous,
where a variety of radio access technologies, such as DSRC,
Wi-Fi, long-term evolution (LTE), and C-V2X, will co-exist in
the vehicular environment. DSRC has been widely deployed
in the past decades, which enables vehicles to communicate
with each other and road-side infrastructures directly, without
involving cellular or additional infrastructures. But its draw-
backs like low peak data rate and limited coverage become the
bottleneck for satisfying the Quality-of-Service (QoS) require-
ments for many emerging microservices of our proposed MDT
framework, such as cooperative perception, HD map, and any-
time OTA update, that require transmitting a high volume of
data in a short time window.
Although WiFi and LTE are capable of providing a larger
bandwidth and throughput, they require more sophisticated
mechanisms to tackle MDT management challenges incurred
by one of the traits of vehicles - high mobility. In contrast,
the more recent C-V2X technology is compatible with 5G and
could address the communication challenges due to vehicles’
high mobility. However, C-V2X is not affordable and widely
deployed compared with DSRC [98].
Therefore, network heterogeneity might be a promising
solution to tackle the challenges presented above and to
enable connected vehicles to achieve diverse QoS requirements
for our proposed MDT framework. However, multiple future
challenges might be imposed by the network heterogeneity:
1) Maintaining a seamless connectivity within a heteroge-
neous network environment is challenging. Sophisticated
mechanisms for managing the connectivity across differ-
ent radio access technologies is desirable.
2) Balancing the tradeoff between latency and cost is cru-
cial. How to achieve a satisfactory latency with the
lowest overhead (e.g., data transmission cost and energy
consumption) is challenging.
3) Connected vehicles heavily rely on data that are shared
with surrounding intelligent nodes, such as roadside
units, pedestrians’ devices, other vehicles. Open issues
like how to secure these shared data with cost-efficient
solutions, and what might be the role of cloud–edge
computing systems still remain.
VII. CONCLUSION
In this study, a MDT framework has been developed for
connected vehicles with the help of cloud computing and edge
computing. It has been found from the literature review that,
although the Digital Twin concept has been recently studied
in the transportation domain, there is no related literature that
leverages this technology together with cloud–edge comput-
ing to benefit connected vehicles with detailed microervices.
In this study, the proposed MDT framework has been demon-
strated with details regarding all of its components: Human,
Vehicle, and Traffic, together with their associated Human
Digital Twin,Vehicle Digital Twin, and Traffic Digital Twin.
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WANG et al.: MOBILITY DIGITAL TWIN: CONCEPT, ARCHITECTURE, CASE STUDY, AND FUTURE CHALLENGES 17465
The public cloud service AWS has been adopted in this
study to design the cloud–edge architecture, which deploys
the MDT framework and makes it a reality rather than a con-
cept. To showcase the effectiveness of the MDT framework, a
case study of P-ACC has been conducted leveraging the key
microservices of the MDT framework: user management and
driver type classification of the Human Digital Twin, cloud-
based ADAS of the Vehicle Digital Twin, and traffic flow
monitoring and variable speed limit of the Traffic Digital Twin.
In conclusion, scalability, reliability, security, cost, latency,
and fidelity are the key factors to consider when designing any
Digital Twin frameworks. Along with the rapid development
of the Digital Twin, it can be envisioned that more related
studies will be conducted in the mobility domain to tackle the
real-world challenges in the near future.
ACKNOWLEDGMENT
The authors want to sincerely thank the Atos team for
their technical supports in building the Connected Car Cloud
(CCC), and also thank Shalini Keshavamurthy, Dr. Seyhan
Ucar, Haritha Muralidharan, Dr. Takamasa Higuchi, Johnny
Wang, Divya Sai Toopran, Kazuhisa Shitanaka, Akio Orii, and
Kenichi Murata for their support and guidance.
The contents of this study only reflect the views of the
authors, who are responsible for the facts and the accuracy
of the data presented herein. The contents do not necessarily
reflect the official views of Toyota Motor North America.
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Ziran Wang (Member, IEEE) received the B.E.
degree from Beijing University of Posts and
Telecommunications, Beijing, China, in 2015, and
the Ph.D. degree from The University of California
at Riverside, Riverside, CA, USA, in 2019.
He is currently a Principal Researcher with
the InfoTech Labs, Toyota Motor North America,
Mountain View, CA, USA, where he leads the
“Digital Twin” project. His research focuses on
intelligent vehicle technology, including cooperative
automated driving, driver behavior modeling, and
vehicular cyber–physical systems.
Dr. Wang serves as a Founding Chair for IEEE Technical Committee on
Internet of Things in Intelligent Transportation Systems.
Rohit Gupta received the Ph.D. degree in mechani-
cal engineering from The University of California at
Santa Barbara, Santa Barbara, CA, USA, in 1998.
He is currently as a Principal Cloud Architect with
the InfoTech Labs, Toyota Motor North America,
Mountain View, CA, USA. Prior to joining Toyota,
he worked in various industries: semiconductors,
telecommunications, finance, and software. His
research interests include cloud and edge computing
architectures, connected and autonomous vehicles,
AI/ML, “Digital Twin” of human–vehicle interac-
tions, and high-speed mobility.
Kyungtae Han (Senior Member, IEEE) received the
Ph.D. degree in electrical and computer engineering
from The University of Texas at Austin, Austin, TX,
USA, in 2006.
He is currently a Senior Principal Scientist with
the InfoTech Labs, Toyota Motor North America,
Mountain View, CA, USA. Prior to joining Toyota,
he was a Research Scientist with Intel Labs,
Santa Clara, CA, USA, and a Director of Locix Inc.,
San Bruno, CA, USA. His research interests include
cyber–physical systems, connected and automated
vehicle techniques, and intelligent transportation systems.
Haoxin Wang (Member, IEEE) received the B.S.
degree in control science and engineering from
Harbin Institute of Technology, Harbin, China, in
2015, and the Ph.D. degree in electrical and com-
puter engineering from The University of North
Carolina at Charlotte, Charlotte, NC, USA, in 2020.
He is currently a Research Scientist with the
InfoTech Labs, Toyota Motor North America,
Mountain View, CA, USA, where he leads the “Edge
Computing” project. His research interests include
edge computing for connected and autonomous vehi-
cles, applied machine learning for intelligent systems, and low-power and
high-assurance cyber–physical systems.
Akila Ganlath received the B.S. and M.S. degrees
in mechanical engineering from the University of
California at Riverside, Riverside, CA, USA, in 2016
and 2019, respectively.
He is currently a Principal Researcher with
the InfoTech Labs, Toyota Motor North America,
Mountain View, CA, USA. His research interests
include control systems,