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The Co-Simulation Architecture and Platform Establishment Method for
Cloud-based Highway Platooning Control System
Abstract
The Cloud-based Highway Platooning Control (CHPC) system can obtain road and vehicle information from the Cloud
Control Platform (CCP) and rapidly calculate the optimal speed and trajectory for Intelligent Connected Vehicles (ICVs).
It helps to reduce vehicle energy consumption, enhance road traffic capacity, and improve transportation efficiency by
managing the distance and speed of platooning vehicles. However, research on CHPC has overlooked the feasibility of
transitioning from theory to application, and there is a lack of effective validation between the consistency and rationality
of theoretical frameworks and model applications. In this paper, a model-based architectural construction method for the
CHPC system is proposed, which completes requirement analysis, functional analysis, logical architecture design, and
system parameter calibration. To validate the framework method of the CHPC system architecture, a method for
constructing a collaborative simulation platform for the intelligent highway system is proposed, based on Catia Magic,
Matlab, and Prescan. It begins by defining the intelligent highway system model and specific scenarios, then simulates
multiple operational scenarios according to these scenarios. Under different communication delay schemes and various
platooning spacings, the optimal speed and minimum energy consumption of vehicles are calculated respectively. Finally,
a collaborative simulation platform for the intelligent highway system is constructed based on Catia Magic, Matlab, and
Prescan, achieving unified architecture construction, interface implementation, and collaborative simulation. This
completes a closed-loop research and validation of the architecture, model, algorithm, and application verification of the
intelligent highway system.
Keywords Cloud-based Highway Platooning Control System, Model-Based Systems Engineering (MBSE), Cosimulation, System Modeling.
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Abbreviations
CHPC Cloud-based Highway Platooning Control
System
ICV Intelligent Connected Vehicles
CCP Cloud Control Platform
1 Introduction
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In recent years, traditional highways are undergoing a
transformation towards intelligent highways, driven by
the development of new technologies such as 5G, cloud
computing, and artificial intelligence, as well as the
multidimensional collaboration between industries. The
necessity of building CHPC system has been emphasized
in documents such as the "Outline of Digital Transport
Development Plan," "Outline of Building a Strong
Transportation Nation," and "Strategy for Innovation and
Development of Intelligent Automobiles," [1]. These
documents also highlight the importance of promoting
industry integration and facilitating comprehensive and
deep integration between intelligent connected vehicles
and CHPC system.
CHPC system involve various interdisciplinary and
highly interconnected technologies, such as intelligent
sensing, multi-source analysis, precise prediction,
proactive control, real-time interaction, and vehicle-road
coordination. As a typical complex large-scale system, it
possesses characteristics of multi-level, multidimensional, and multi-component internal structure.
Therefore, a strong adaptive theory is needed for
Intelligent Highway System modeling. In recent years,
Model-Based Systems Engineering (MBSE) has become
a research hotspot in the field of system engineering
modeling both domestically and internationally [2-4],
and it has been widely explored and adopted. Starting
from the conceptual design stage, MBSE ensures the
continuity of system requirements, design, analysis,
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verification, and validation activities throughout the
entire lifecycle of the system using digital modeling. By
transforming the information of requirements, structure,
functions, parameters, and other aspects from traditional
system design documents into digital models and
visualizing them, MBSE improves development
efficiency.
For the modeled Intelligent Highway System, it is
necessary to verify its correctness and executability. Due
to the involvement of different functionalities and
components in the Intelligent Highway System, cosimulation is required to ensure the consistency of the
overall system behavior [5]. In this paper, the cosimulation of the Intelligent Highway System is
conducted using a combination of Catia Magic, Prescan,
and Matlab. The simulation results are then validated and
evaluated.
The rest of the paper is organized as follows: Section
2 introduces the relevant work, including the
methodology of MBSE-based modeling and the basics of
co-simulation. Section 3 presents the construction of the
Intelligent Highway System model based on MBSE.
Section 4 discusses the co-simulation of the constructed
model, including the construction of the simulation
platform and case analysis. Section 5 validates and
evaluates the results of the co-simulation. Finally,
Section 6 provides the conclusion of this paper.
2 Related Work
Traditional system modeling utilizes Text-based
System Engineering (TBSE) [6] to document and convey
system engineering requirements, functions, structures,
interface definitions, and architectural descriptions
through operational documents, manuals, traceability of
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requirements, and verification matrices. However,
TBSE's documents often suffer from poor traceability,
weak information consistency, limited reusability, and
limited scalability when describing the architecture
models of CHPC system. In contrast, Model-Based
Systems Engineering (MBSE) effectively addresses
these issues and has been widely applied in various fields
[7-11].
In various domains and scenarios, different
institutions have proposed various methodologies for
MBSE. Examples include INCOSE's Object-Oriented
Systems Engineering Method (OOSEM) [12], IBM's
Harmony SE methodology [13], and Dassault Systems
Magic Grid methodology [14]. While these methods
have different characteristics and distinctions, they
generally follow the requirements, functional, logical,
and parametric analysis flow (RFLP). The key aspect of
MBSE is to clearly define system requirements,
determine system functionalities and necessary physical
components, and develop solutions. Therefore, this paper
proposes an MBSE-based design and modeling method
for CHPC system, which models the system
requirements, functions, logic, and parameters through
the RFLP modeling process, thus ensuring the
connectivity and traceability from requirements to
solutions, and facilitating the full lifecycle design of the
system, thereby improving the efficiency of modeling
CHPC system.
The traditional MBSE modeling language commonly
used is the Unified Modeling Language (UML) [15]. As
system modeling complexities increase, UML has been
extended to accommodate the specific needs of system
engineering, resulting in the creation of the Systems
Modeling Language (SysML) [16]. Compared to UML,
SysML retains the core concepts and graphical symbols
of UML while introducing additional modeling
constructs to support a wider range of requirements in
systems engineering. SysML can meet the demands of
requirements, functions, logic, and physical analysis in
multiple modeling domains, including hardware,
software, information, and manufacturing. Therefore,
this paper adopts SysML as the modeling language for
MBSE-based intelligent highway modeling.
Modeling tools ensure the efficiency of MBSE
modeling. MBSE modeling tools should enable real-time
traceability of the system model, so that modifications to
model elements can propagate changes throughout the
model, ensuring efficient model construction. Currently
available modeling tools that support SysML include
Catia Magic, PTC Integrity Modeler, and Cameo
Systems Modeler. Among them, Catia Magic stands out
for its comprehensive functionality, integration
capabilities, modeling reliability, and its long-term
commitment to the development and updates of the
SysML language. Therefore, Catia Magic is chosen as
the modeling tool in this paper.
To validate the overall behavior of the Intelligent
Highway System, collaboration between different
simulation software tools is required, which is known as
co-simulation [17]. Co-simulation enables the global
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simulation of a coupled system through the integration of
different simulation software's theories and technologies.
This coupling is achieved by connecting the output of
one simulation software to the input of another
simulation software, enabling the execution of the entire
system model with multiple simulation software tools
[18].
Co-simulation has been widely applied in various
fields. For instance, Zhenkai Zhang et al. [19] proposed
a co-simulation framework to assist in the design of timetriggered automotive cyber-physical systems. Bing Li et
al. [20] developed a fully digital wind energy conversion
system (WECS) real-time co-simulation platform and
proposed a full-digital interface technology between
RTDS and FAST. Qian Zhang et al. [21]established a
longitudinal potential field based car tracking model that
considers the cutting behavior of adjacent vehicles, and
verified the excellent performance of the proposed model
in acceleration, speed, front end tracking, and response
to cutting behavior under three different driving
conditions through comparative simulation. The
framework was validated by comparing the simulation
results with hardware-in-the-loop automotive simulators.
Sina Shojaei et al. [22] developed a vehicle model that
includes air conditioning and battery cooling loads in
vehicle level energy efficiency calculations. The
subsystem model is developed based on the
specifications of the target vehicle and integrated into
Simulink using the FMI collaborative simulation
standard.Kamyar et al. [23] used collaborative
simulation methods to study the structure of hybrid
power devices for marine vessels. Yogesh et al. [24]
proposed a method for Execution Performance Analysis
and Optimization (EXPPO) to address optimization
issues in collaborative simulation. Huixue Dang et al. [25]
conducted multidisciplinary joint simulation on the
recovery of underwater vehicles.
Although the above studies focus on co-simulation in
their respective domains, there are some existing issues.
On one hand, the modeling part of the co-simulation is
often relatively rudimentary, as the models are usually
constructed based on the demands of the co-simulation,
neglecting the complexity of the system itself. On the
other hand, the testing and experiments conducted in cosimulation mainly aim to validate the correctness and
rationality of the simulation, lacking content related to
the balancing and optimization of system models. To
address these issues, this paper defines the hierarchical
architecture and elements of the Intelligent Highway
System, models the Intelligent Highway System based
on MBSE, and builds a co-simulation platform for
intelligent highways using Catia Magic, Matlab, and
Prescan. This platform enables closed-loop research
from requirement analysis to architecture design, models,
algorithms, and simulation validation. Compared to
existing research, the main contributions of this paper are
as follows:
(1). This paper proposes an MBSE-based design and
modeling method for CHPC system. Building an
architecture for Intelligent Highway based on the "V"
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During the requirements analysis phase, ensure that a
bidirectional traceability relationship is established
between the requirements and system elements to ensure
that each requirement is reasonable and can be effectively
traced. Subsequently, the requirements are deconstructed
layer by layer, establishing traceability relationships
between top-level requirements and sub requirements, as
well as between sub requirements, to ensure consistency
between requirements and design. By using indicators
such as requirement coverage, it is confirmed whether the
needs of stakeholders have been met, and a complete
requirement model is ultimately established.
Model in MBSE Methodology . And the RFLP modeling
process is used to model and design the system's
requirements, functions, logic, and parameters. The
model is used to transfer the system's state and data,
ensuring the connectivity and traceability from
requirements to solutions, and enabling the full lifecycle
design of the Intelligent Highway System. This approach
makes the system model of the intelligent highway more
intuitive, accurate, and efficient, thus effectively
improving the efficiency of Intelligent Highway System
modeling.
(2). A co-simulation platform for CHPC system is
built based on Catia Magic, Matlab, and Prescan. This
platform integrates the road and vehicle elements of the
Intelligent Highway System into a unified platform,
achieving unified architecture construction, interface
development, and system simulation. It enables closedloop research on Intelligent Highway System
architecture, models, algorithms, and application
validation. Finally, the simulation cases are validated and
balanced, demonstrating the impact of communication
delay on platoon control as well as the influence of
different inter-vehicle distances on convoy fuel
consumption, providing references for the evaluation and
optimization of relevant solutions.
In the functional analysis stage, it is necessary to further
refine and decompose the requirements to determine the
relationships between various functional units in the
system. This stage typically employs tools and techniques
such as functional decomposition diagrams, use case
diagrams, etc. to help establish a clear functional model.
At the same time, the priority and relevance of
requirements also need to be considered in order to
determine the order and importance of functional
implementation.
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3 The CHPC Architecture Definition
3.1 Theory of CHPC System
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The design process of CHPC system architecture based
on MBSE is based on the "V" model, a typical MBSE
development model, as shown in Figure 1. The "V" model
includes the entire process of requirement development,
system architecture design, simulation verification, and
test confirmation.
In the logical architecture design phase, the focus is on
establishing the overall architecture of the system,
clarifying the parameters of the system, subsystems, or
internal modules, and determining the interfaces and
interaction methods between each module. Based on the
system functional model, internal module diagrams, state
machine diagrams, and sequence diagrams are
constructed.
The physical design phase transforms logical design
into an executable system architecture. At this stage, it is
necessary to select appropriate technologies and tools to
begin coding, testing, and implementation of the system.
At the same time, develop a deployment plan and
strategy for the system to ensure effective operation and
maintenance in the actual environment.
3.2 Modeling and Implementation of CHPC
system based on MBSE
3.2.1 Requirement Modeling
Fig. 1."V" Model of Intelligent Highway System Based on
MBSE
The development approach of the left side of the V
model is top-down, based on requirements, followed by
architecture design and detailed design. The right side of
the V model focuses on integration, validation, and
confirmation. This article mainly focuses on the design of
the system architecture for the left half.
Requirement analysis is the primary step in the RFLP
modeling process. At this stage, starting from the needs
of stakeholders and users, the system can be clearly
defined. This stage involves refining and breaking down
requirements, establishing relationships between
different levels of requirements. Using the consistency
method of CHPC system, analyze multiple aspects of the
system from a unified perspective. Horizontally consider
consistency in security, protection, and standardization,
while vertically focus on the detailed relationships
between different users in terms of strategy, interests,
services, and system views, and establish traceable
connections between these views.
After clarifying the requirements, use SysML language
for requirement modeling. Firstly, the framework shown
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is analyzed and organized according to the process
specification; then the interrelationships between various
requirements are clarified, and the requirements are
modeled and analyzed using requirements lists and charts;
finally, the relevance and traceability of requirements are
realized, and the logical relationships between
requirements are obtained visually and clearly. After
determining the top-level requirements, their lower level
requirements are refined.
Fig. 3.Requirement Diagram of Stakeholder Requirements in
The line with a cross shaped circular endpoint in Figure
3 represents the composition or decomposition
relationship between various requirements. The graph at
one end without a cross shaped circular endpoint is used
to describe the requirements in more detail. The
stakeholder requirements model includes six sub
requirements, including traveler requirements, road
owner requirements, and law enforcement department
requirements,etc. The sub requirements can be further
subdivided, for example, traveler requirements can be
divided into logistics vehicle driver requirements and
road traveler requirements (as shown in Figure 4),
decompose the requirements layer by layer to achieve a
graphical description of the requirements.
CHPC system
The requirement diagrams shown in Figure 3 and 4 can
also utilize relationships such as decomposition and
traceability to manage requirements , such as strategic
stakeholder requirement traceability, system and service
level requirement traceability, and stakeholder service
requirement traceability, as shown in Figure 5. The above
is an example of stakeholder requirement modeling, and
other levels of requirement modeling also follow the same
process and methods to complete the requirement
modeling of the entire system.
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Fig. 4.Expanded Diagram of Traveler Requirements in
CHPC system
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When there are new research and development tasks
or design requirements changes, a graphical and intuitive
CHPC system model built by SysML language can
conveniently and quickly complete operations such as
querying, adding, removing, and modifying the model
significantly improving the system's reusability.
Fig. 5.Traceability Diagram of Requirements
3.2.2 Functional Modeling
Fig. 2.Requirement List of Stakeholder Requirements in
CHPC system
Based on the system requirement analysis and using the
RFLP method, functional analysis is performed on the
CHPC system to establish a functional model. The
relationships between actors and use cases in the CHPC
system are described through use case diagrams in the
SysML language. Activity diagrams are used to provide a
detailed description of the process involved in
implementing system functionalities. State diagrams are
utilized to analyze the states of system components during
operation. The process of functional analysis modeling is
illustrated in Figure 6.
The CHPC system encompasses four main functional
objectives: Vehicle Convoy Management, Vehicle
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Convoy Control, Special Situation Handling, and
Intelligent Highway Operations Management. The use
case diagram for the design based on these functional
objectives and actor relationships is shown in Figure 7.
Each use case in Figure 7 corresponds to one of the
system's functional objectives. Using the example of
Vehicle Convoy Control functionality, the process of
functional analysis modeling is explained in detail.
For the Vehicle Convoy Control functionality, the first
step is to conduct behavioral analysis of this functionality
within the CHPC system. It includes six subfunctionalities: Speed Control, Curved Road Driving,
Lane Control, Emergency Braking, and Distance
Adjustment. The use case diagram for the Vehicle
Convoy Control functionality is defined as shown in
Figure 8(a). The stakeholders for the CHPC system
include Intelligent connected trucks and other vehicles on
the road.
requirements and facilitates analyzing the impact of
requirement changes, thereby enhancing traceability of
requirements.
Next, an activity diagram is used to expand and define
each target use case. Taking Lane Control as an example,
the functionality is expanded and depicted in Figure 8(b)
and 8(c). Figure 8(b) describes the lane control activity,
where the lead vehicle and the following vehicles provide
real-time feedback on convoy ID, vehicle ID, vehicle
speed, and other information. The Intelligent Highway
Road Testing System receives this information and
transmits it in real-time to the cloud control system. Upon
receiving the data, the cloud control system integrates it
into lane position information through lane status
monitoring. The lane position check device
simultaneously checks the vehicle's position, generates
lane control strategies based on the check results, and
sends the information to the next associated functional
objective.
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Fig. 6.Functional Analysis Process
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Fig. 7.Use Case Diagram of CHPC system
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In parallel, the road testing system performs roadside
monitoring to collect radar data, video streams, and other
information, which is processed into structured
monitoring information and forwarded to the cloud
control system. The cloud control system, through road
testing information monitoring, converts the received
data into lane information of other vehicles on the
roadside, which is then outputted along with lane position
information to the next associated functional objective.
The analysis and modeling process of this functionality
involve decomposing the target functionality, defining
the functional interfaces and related parameters, and
determining the content of object flows between activities.
Due to space limitations, only the specific modeling
process of one sub-functionality is provided above, while
the remaining sub-functionalities follow the same
modeling methods and processes.
The aforementioned functional analysis modeling
process uses activity diagrams to specifically describe
the implementation process of the Vehicle Convoy
Control functionality within the CHPC system. This
allows for the identification of input and output
parameters for each activity node in the convoy control
process, ensuring that the sources and subsequent
associated information of each system requirement are
properly understood. This aids in quickly locating
relevant requirements in case of changes to top-level
Fig. 8.Use Case Diagram and Activity Diagrams
3.2.3 Logical Modeling
Functional analysis determines the activity behavior of
the CHPC system, and based on requirement analysis and
functional analysis, logical architecture design is carried
out to establish data interaction interfaces and data flow
types that can meet the needs of system requirements and
functions for system behavior.
Taking the vehicle platooning control functionality
described in section 3.2 as an example, it is implemented
by the CHPC system, road sensing system, and cloud
control system. The module definition diagrams of these
three systems are shown in Figures 9-11. The vehicle
system consists of electrical, body, chassis, and power
systems. The roadside system comprises IHTP road
sensing unit, IHTP roadside computation unit, IHTP road
infrastructure, and IHTP roadside communication unit.
The cloud control system consists of IHTP cloud control
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infrastructure and IHTP cloud control application
platform. Moreover, each of these systems can be further
divided into various subsystems and submodules. Finally,
by using internal module diagrams, the association
relationships between different systems, subsystems, and
components of the CHPC system are determined, as
shown in Figure 12, specifying the interface definitions
and interaction relationships among the internal modules
of the vehicle-road-cloud system.
In Figure 12, the specific functional stages of CHPC
system include: (1) Establishment of internal module
connections in the intelligent connected vehicle (ICV)
system phase: linkages are built between the ICV
intelligent driving domain, ICV cockpit domain, and
ICV vehicle control domain. The ICV intelligent driving
domain receives vehicle information from the ICV
cockpit domain and ICV vehicle control domain and
provides feedback instructions to the ICV vehicle control
domain. (2) Establishment of internal module
connections in Intelligent Highway roadside system
phase: linkages are established among the road sensing
unit, roadside computation unit, roadside infrastructure,
and roadside communication unit. The road sensing unit
transfers critical information to the roadside computation
unit and roadside communication unit, and the roadside
communication unit provides information feedback to
the roadside computation unit. (3) Establishment of
internal module connections in the Intelligent Highway
cloud control system phase: linkages are established
between the cloud control infrastructure and cloud
control application platform. Within the cloud control
infrastructure, connections are established between the
regional cloud and the edge cloud, with the regional
cloud transmitting self-check signals to the edge cloud.
Additionally, information exchange relationships are
formed among the submodules within the regional and
edge clouds. The cloud control infrastructure exchanges
key information such as vehicle ID and driving strategies
with the cloud control application platform, and receives
signal data from the application platform. (4)
Establishment of connections between the ICV,
Intelligent Highway roadside system, and Intelligent
Highway cloud control system: the ICV transmits critical
information such as vehicle position, speed, and planned
trajectory to the Intelligent Highway road sensing
system. The road sensing system receives the
information and transfers it to the Intelligent Highway
cloud control system, which processes the data and
transmits relevant signals and instructions back to the
ICV, thus completing the closed-loop control and
achieving the desired functionality
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Fig. 9. Module Definition Diagram of Intelligent Connected
Vehicle
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Fig. 10. Module Definition Diagram of Intelligent Highway
Road Sensing System
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Fig. 11. Module Definition Diagram of Intelligent Highway
Cloud Control System
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calibrate critical parameters that meet the complex
operational requirements of the large-scale system.
Fig. 13. Process of Parameter Calibration in the Intelligent
Highway System
4 Co-Simulation Platform Of CHPC system
4.1 Introduction to CHPC system Modeling
4.1.1 Description of CHPC system Modeling
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Fig. 12. Internal Module Diagram of Intelligent Highway
System
3.2.4 Physics Modeling
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After the logical architecture design and model
construction are completed, it is necessary to establish a
physical scene for simulation verification in real
situations, and implement the system architecture from a
physical perspective. This process involves creating
multiple scene instances and conducting joint simulation
tests to obtain information from simulation results for
comprehensive evaluation. Through this in the loop
verification method, potential issues in system design can
be detected, providing guidance and optimization
direction for the implementation of actual systems.
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The calibration process of parameters in the CHPC
system is depicted in Figure 13. In the first step, scenario
simulation parameters are set, and the parameter
constraints are defined in the architecture model. In the
second step, the scenario simulation model is executed,
and relevant evaluation parameters are extracted.
Finally,Simulink passes the simulation results to Catia
Magic through the interface for verification and
evaluation of the results. The system parameter
calibration method utilizes SysML to establish a
simulation-verification interface block diagram,
facilitating the unified invocation and control of multiple
simulation software throughout the entire process. It
optimizes the CHPC system architecture from multiple
dimensions and evaluates alternative solutions based on
specific perspectives. Ultimately, the most optimal
architecture and implementation approach are selected to
The Intelligent Highway System is a typical complex
cyber-physical system that integrates road, vehicle, traffic,
information, and communication systems. This paper
focuses on the vehicle platoon scenario in the Intelligent
Highway System and designs an operational plan for the
"Through Port to Garden" vehicle platooning on mixeduse roads in 2025.
To describe the vehicle platooning scenario in the
Intelligent Highway System and facilitate a common
understanding of system operations among stakeholders,
the vehicle platooning scheme for the Intelligent
Highway System is depicted in Figure 14. It comprises
the Intelligent Highway Cloud Control System,
Intelligent Highway Roadside System, as well as other
systems such as Intelligent connected trucks, map
platforms, other road vehicles, and freight companies that
interact with the vehicle platooning system in the
operational environment. In this scenario, commercial
vehicles register their vehicle information with the cloud
control system upon entering the service section. The
cloud control system provides platooning services to the
target vehicles based on the registration information
provided by the freight companies. Once the commercial
vehicles activate the service, they comply with the service
agreement and are directly controlled by the cloud in
certain service aspects. In case of failure in cloud control
services, individual vehicles intelligently take over and
alert the driver.
As a complex large-scale system, the intelligent
highway vehicle platooning system involves coordination
and collaboration among multiple technologies and
domains. It is characterized by multiple coupled factors
and complex interrelationships. System modeling of the
Intelligent Highway System needs to encompass top-level
architectural design, architectural requirement model
construction, architectural function model construction,
logical architecture design, system parameter calibration,
simulation algorithm development, and parameter
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calibration. During the system modeling process, it is
essential to enhance requirements traceability, ensure
information consistency, improve model reusability and
scalability, as well as enhance modeling accuracy.
Fig. 16. General Characteristic Scenarios
Fig. 14. Schematic Diagram of Intelligent Highway Vehicle
For the aforementioned highway model, conventional
strategy verification and optimization have been
conducted, including convoy lane change strategy,
emergency braking, and emergency response strategy
optimization. Additionally, parameter validation and
optimization have been performed on parameters such as
convoy driving spacing, speed, and trajectory under
different scenarios.
Platooning System Elements
4.1.2 Highway Road Model Scenario
4.1.3 Convoy Motion Behavior Model
The research in this paper investigates the elements of
the highway scenario, defines the highway cross-section,
general characteristic scenarios, and outlines the convoy
driving scenarios.
(1) Scenario of Highway Cross-Section: In the cosimulation of the Intelligent Highway System, the
highway scenario is defined as a bi-directional six-lane
road with additional on-ramps and off-ramps, ensuring
the authenticity of the highway model scenario, as shown
in Figure 15.
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Simulate and simulate the 12 convoy behaviors
involved in intelligent highway convoy management and
control. The convoy management simulation includes
convoy creation, follow vehicle joining, follow vehicle
leaving, convoy dissolution, convoy adjustment, and
spacing adjustment. The convoy control simulation
includes constant speed driving, acceleration driving,
deceleration driving, emergency braking, curve driving,
and lane change.
Convoy Creation: Ensure the convoy is created within
the specified time and on the designated road segment.
Follow Vehicle Joining: Ensure the follow vehicle joins
the convoy within the specified time and road segment.
Follow Vehicle Leaving: Ensure the follow vehicle
leaves the convoy within the specified time and road
segment.
Convoy Dissolution: Ensure the convoy is dissolved
within the specified time and road segment.
Convoy Adjustment: Ensure the convoy completes the
required adjustments within the specified time and road
segment.
Fig. 15. Highway Scenario
(2) Definition of General Characteristic Highway
Scenarios: Based on the actual highway scenarios, six
general characteristic highway scenarios are defined:
straight road, curved road, on-ramp, off-ramp, tunnel, and
elevated bridge, as shown in Figure 16.
Spacing Adjustment: Ensure the convoy completes the
spacing adjustment within the specified time and road
segment.
Constant Speed Driving: Frist,maintain a constant speed.
Second, keep the convoy vehicles in the same lane.
Third,maintain a constant spacing.
Acceleration Driving: First, accelerate at the required
rate. Second,keep the convoy vehicles in the same lane.
Third, ensure a safe spacing.
Deceleration Driving: First, decelerate at the required
rate. Second, keep the convoy vehicles in the same lane.
Third,ensure a safe spacing.
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Emergency Braking: First,avoid collision between
vehicles. Second,keep the convoy vehicles in the same
lane.
Curve Driving: First, keep speed deviation within a
certain range. Second, prohibit crossing lane markings.
Third,ensure a safe spacing.
e.Queue Driving Algorithm Module: This module
implements queue driving algorithms that control the
acceleration of vehicles based on their relative positions
and speeds to maintain stability and safety within the
queue.
Lane Change: First,ensure safe lane change. Second,
prohibit crossing lane markings. Third, Signal the
following vehicles before changing lanes. Fourth, Ensure
a safe spacing.
f.Output Control Quantity to Prescan Simulation
Environment: Prescan is a software tool used for railway
and traffic safety simulation. Through this module, joint
simulation can send control signals to the Prescan
simulation environment to simulate and control the
operation of railway systems.
Validate and optimize the control accuracy of the above
12 convoy motion behavior models: determine the lateral
and longitudinal control accuracy, braking control
requirements for different vehicle behaviors; and validate
and optimize communication requirements: determine the
delay requirements, transmission loss requirements, and
packet loss rate for internal communication within the
convoy
By dividing the joint simulation module into these submodules, effective simulation and validation of the
performance and behavior of complex traffic systems can
be achieved.
4.1.4 Special Scenario Model
Establish a smart highway system with six special
scenario models: strong winds, heavy rain, blizzards,
dense fog, overtaking, and accidents. Investigate the
impact of different parameters on convoy driving in these
scenario models, including the range and accuracy of
perception devices and road friction parameters. And
conduct validation and optimization of special strategies:
determine the optimal strategies for convoy driving in
adverse environments, such as the best speed and
distance.
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4.2 Co-simulation environment
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Fig. 17. Co-simulation module
In the dashboard module, it encompasses the following
sub-modules:
The co-simulation environment includes cosimulation module and dashboard module.
The co-simulation module can be divided into:
a.Vehicle State Input Module: This module is
responsible for receiving and storing various state
information from the vehicle, such as vehicle position,
speed, acceleration, etc. This information can be derived
from the actual vehicle system or generated through
simulation.
b.Ideal Sensor Input Module: This module simulates the
behavior of various ideal sensors installed on the vehicle.
It receives vehicle state information and generates ideal
sensor output signals based on this information.
c.OBU Signal Input Module: This module is
responsible for receiving and processing signals from the
on-board unit (OBU). The OBU is a crucial component in
intelligent transportation systems and typically contains
vehicle identity information, position information, and
other relevant data.
d.OBU Signal Output Module: This module processes
the signals from the OBU and sends them to external
systems or devices. For example, it can transmit the
vehicle's position and speed information to surrounding
vehicles or traffic control systems.
Fig. 18. Dashboard module
a.Vehicle Position Output Module: This module is
responsible for displaying the vehicle's current position
on the dashboard. It receives data from the vehicle's GPS
or other positioning system and updates the dashboard
display accordingly.
b.Vehicle Acceleration Output Module: This module
provides information on the vehicle's acceleration by
indicating the changes in speed over a period of time. It
calculates and displays the acceleration value on the
dashboard, allowing the driver to make informed
decisions on how to operate the vehicle.
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
 xi  vi

 vi  ai

ui ai
ai  
 

c.Vehicle Fuel Consumption Output Module: This
module calculates and displays the fuel consumption of
the vehicle in real-time. It tracks the amount of fuel used
and provides feedback on fuel efficiency, enabling the
driver to optimize fuel usage and reduce costs.
d.Vehicle Speed Output Module: This module displays
the current speed of the vehicle on the dashboard. It
receives speed data from the vehicle's speed sensor and
updates the display accordingly, ensuring that the driver
has constant access to vital speed information.
In the formula, xi, vi, and ai respectively represent the
displacement, velocity, and acceleration of vehicle i; ui
represents the expected acceleration input of vehicle i; τ
Indicates the inertia delay of the vehicle.
In the process of constructing the longitudinal
following model for vehicle formation control, this paper
adopts a nonlinear queue following model and fully
considers the impact of vehicle inertia delay and
communication delay, as shown in the formula:
By incorporating these modules into the dashboard,
drivers can be provided with a comprehensive set of
information that allows them to understand and optimize
the performance of their vehicle in real-time. This
information can be used to make informed decisions on
how to operate the vehicle, reduce fuel consumption, and
ensure safety on the road
h
i  vi 1  v

ui   V (hi (t   ))  vi (t   )  hi (t   )   i 1
4.3 Vehicle motion model algorithm
(6)
Before implementing the algorithm in the above scene,
the basic algorithm needs to be designed.
(1)Vehicle dynamics
implementation:
modeling
for
algorithm
mv(t )  F (t )  F f ( s)  Fair (v)
(1)
F f ( s )  mg ( f cos  ( s )  sin  ( s ))
(2)
Fair (v) 
Cd Av 2 (t )
(3)
2
Where m is the mass of the vehicle; s is the driving
distance; v is the driving speed; F(t) is the driving force
of the vehicle; Ff (s) is the rolling resistance of the vehicle;
Fair (v) is the air resistance; g is the acceleration of gravity;
f is the rolling resistance coefficient; α(s) Is the lane slope,
which can be determined from the positioning system
according to the position of the vehicle; ρ is the air density;
Cd is the air drag coefficient; A is the orthographic
projection area.
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(2)Fuel emission algorithm:
When the vehicle is driving at a fixed speed on a level
road surface:
(4)
Where F represents fuel consumption;k is the efficiency
factor (depending on engine speed, gearbox gear ratio,
etc.)
(3)Formation Control Model
This part represents the longitudinal dynamics of
vehicles as a first-order inertial delay model, where there
is a first-order inertial delay between the actual
acceleration and the expected acceleration of the vehicle,
which can be represented as:
In the formula: hi is the distance between the i-th
vehicle and the front vehicle; V(h) is the expected vehicle
speed at different following distances; σ is the
communication delay; α、β、γ These are the controller
gain values.
In response to the impact of following distance on
expected acceleration, a nonlinear distance control model
is defined in the model, which is：
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F  k[ Fair (v)  F f ( s )]  k ( ACd v 2  fmg)
2
(5)
0,
h  hst

V (h)  F (h),
hst  h  hgo

vmax , h  hgo
v
h  hst
Where F (h)  max (1  cos(
)) .
2
hgo  hst
(7)
(8)
When the distance between vehicles is less than hst,
the expected speed of the vehicle will be 0, and when the
following distance is greater than hgo, the expected speed
of the vehicle will be the maximum speed vmax.
4.3.1 Vehicle convoy and formation adjustment
algorithm
Algorithm Application Scenarios: Scenarios where the
number of lanes changes, as depicted in Figure 19.
Fig. 19. Vehicle Convoy and Formation Adjustment
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4.3.4 Cooperative Convoy Formation Algorithm for
Signal-Free Intersection Crossings
Algorithm
Algorithm Scenario: Fully Intelligent Connected
Vehicle Scenario, as shown in Figure 22.
4.3.2 Cloud-based Convoy Assistance Algorithm
Algorithm scenario: Stable driving of the queue, as
shown in the Figure 20.
(1) Trigger condition:
The inter-vehicle spacing and relative velocities of the
vehicles in the queue fall within the range required for
stable driving, i.e., the spacing and relative velocities are
not too large.
Fig. 22. Intelligent Connected Vehicle Scenario
(2) Control process:
4.4 Construction of a Collaborative Simulation
Platform
The PLF (Preceding-Leading-Following)communication topology is employed. Each vehicle simultaneously
receives status information from the leading vehicle and
the preceding vehicle, including the vehicle's position,
velocity, and acceleration. Based on the status
information of the vehicle, the preceding vehicle, and the
leading vehicle, the desired acceleration for the vehicle is
computed. Acceleration calculation methods such as
CACC can be utilized.
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(3) Termination condition:
The queue maintains stable driving with the desired
vehicle speed and inter-vehicle spacing.
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Fig. 20. Stable queueing and driving Scenario
4.3.3
Formation
Algorithm
at
Intersections in Mixed Traffic Flow
Signalized
Algorithm Scenario: Formation Algorithm for Mixed
Traffic Flow with Coexistence of Manual and Intelligent
Connected Vehicles
Main Algorithm Components: Formation of Vehicle
Convoy within the Same Green Phase, with Intelligent
Connected Vehicles as Lead Cars and Manual Vehicles
as Follower Cars. As shown in Figure 21.
Fig. 21. Signalized Intersection Convoy Formation
Algorithm for Mixed Traffic Flow.
Due to the complexity of the Intelligent Highway
System model, it is challenging to obtain accurate
simulation results using a single software. Therefore, this
paper utilizes three software programs, namely Catia
Magic, Prescan, and Matlab, to achieve the co-simulation
of the Intelligent Highway System. Catia Magic is used
for co-simulation software input-output interface,
simulation parameter definition, and integrated modeling
of the collaborative simulation platform, as Prescan and
Matlab require interfaces for data exchange. The
approach of the Co- simulation is as follows: firstly, key
parameter calibration for the co-simulation of the
Intelligent Highway System's member systems is built in
Catia Magic. The co-simulation is initiated and Matlab
script is called through Catia Magic, which then launches
the algorithm and sends it to Prescan. Prescan inputs realtime data such as speed, acceleration, spacing, and fuel
consumption of the target convoy into Matlab. Matlab
utilizes its data processing capability and current situation
information to verify the fuel consumption of the convoy
under different spacing conditions, and comprehensively
evaluates and optimizes convoy driving strategies by
considering different time delay schemes. The evaluated
and optimized information is then fed back to Prescan.
After the simulation, Matlab uploads the processed
simulation evaluation parameters to Catia Magic for
parameter trade-off, simulating the entire process of a
typical scenario in the Intelligent Highway System.
Specifically, the steps of the co-simulation include the
following four steps: (1) setting simulation parameters
and invoking Matlab scripts in the Catia Magic
environment; (2) launching Prescan and Simulink
simulation through Matlab scripts to simulate the convoy
situation; (3) performing parameter transfer and cosimulation between Prescan and Simulink through
Matlab scripts. After the simulation, Prescan outputs an
evaluation parameter file, and the Matlab script initiates
the evaluation result processing program; (4) Simulink
transfers the simulation results to Catia Magic through the
interface for validation and evaluation.
Fig. 25. The structure of the Intelligent and Connected Truck
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(ICT)
5.2 Platoon Driving with Different Time Delays
Fig. 23. Co-simulation system main interface
In order to verify the effectiveness of model in the
above sections, we first give the simulation of the
different time delays. In the simulation process of this
section, the simulation is verified with three trucks in the
Figure 25.
The truck system parameters are set as in Table I .
Table I Intelligent and Connected Truck (ICT)
Parameters
Parameter Names
5 Using Verification And Evaluation
Truck length
In order to validate the Intelligent Highway System
model, this section conducts co-simulation on the vehicle
platoon control of the Intelligent Highway System. It
verifies the impact of communication delays under
different delay schemes on formation driving and
evaluates the convoy driving strategy by considering fuel
consumption at different spacing intervals combined
with various delay scenarios.
Position of the first truck
Initial speed of the trucks
Initial acceleration of the
trucks
L  m
p1  m 
Vi  km / h 
ai  m / s 
2
Value
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96
80
0
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5.1 Co-simulation Visualization Interface
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Parameter
Symbols
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Under different time delays, with the remaining
parameters set as follows: the position of the second
vehicle (d2) is 60 meters, and the position of the third
vehicle (d3) is 24 meters. Thus, at the initial moment, the
range between the first and second vehicles is 20 meters,
and the range between the second and third vehicles is
also 20 meters. Figures 26 and 27 respectively depict the
curves of speed, acceleration, range, and average fuel
consumption of Intelligent and connected trucks at time
delays of 0.1 seconds and 0.01 seconds.
Fig. 24. Simulation Visualization Interface
The visualization display interface of the Intelligent
Highway Information-Physical System Co-simulation
Platform is depicted in Figure 24. The visual interface is
divided into two sections: the train movement screen and
the simulation image interface. The simulation images
include displays for speed, acceleration, range, and
average fuel consumption.
To validate the effectiveness of the Intelligent
Highway System model proposed in this paper, numerical
simulations were conducted for the vehicle platoon
control within the Intelligent and Connected Truck (ICT)
system. Prior to the formal simulation validation, the
structure of the ICT system was defined, with a convoy
consisting of three trucks chosen as the simulation entity.
The simulated structure of the Intelligent and Connected
Trucks during the simulation is outlined in Figure 25.
Fig. 26. Simulation results of the effect of high
communication delay on formation driving
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Fig. 27. Simulation results of the effect of low
communication delay on formation driving
Fig. 29. Simulation results of formation driving fuel consumption
at 10m range
Table II Comparison of simulation results of different
time delays
Different
time delays
Parking
distance of
Truck 1 and
2
Parking
distance of
Truck 2 and
3
Average fuel
consumption
Low time
delay
19.76
20.01
33.15
High time
delay
17.76
17.79
32.89
Fig. 30. Simulation results of formation driving fuel
consumption at 15m range
Table II compares the simulation results of the different
time delays, and analyzing the data in the table, we can
find that in emergency braking conditions, the stopping
distance under the low time delay is 19.76m and
20.01m,while under the high time delay, the stopping
distance is 17.76m and 17.79m. The low time delay can
improve the final stopping distance by more than 2m.
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Fig. 31. Simulation results of formation driving fuel
consumption at 20m range
5.3 Formation driving with varying range
Under different spacings, basic parameters as listed in
Table I with the remaining parameters set as follows:
delay time is 0.1 seconds. Figures 28 to 31 show the
curves of spacing and average fuel consumption for
Intelligent and connected trucks under different initial
range of 5m, 10m, 15m, and 20m.
Fig. 28. Simulation results of formation driving fuel
consumption at 5m range
Table III Comparison of simulation results of four
ranges
Different
range
Parking distance
of Truck 1 and 2
Parking distance
of Truck 2 and 3
Average fuel
consumption
5m
10m
15m
20m
2.9
7.79
12.78
17.76
2.9
7.8
12.8
17.79
32.84
31.36
31.97
32.89
According to Table III, all fleets have parking
distances greater than 0, indicating that no collisions
have occurred, and all fleets are in a safe position. When
the fleet spacing is 5m, the average fuel consumption is
32.84L/100km; when the fleet spacing is 10m, the
average fuel consumption is 31.36L/100km; when the
fleet spacing is 15m, the average fuel consumption is
31.97L/100km; when the fleet spacing is 20m, the
average fuel consumption is 32.89L/100km. In the
scenario of a safety braking event with an initial gap of
10 meters, the optimal condition for minimizing average
fuel consumption.This result indicates that average fuel
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consumption decreases initially and then increases with
the increase in fleet range.
5.4 Summary
This nuanced observation is crucial for understanding
the complex relationship between convoy spacing and
fuel efficiency, offering valuable implications for
optimizing convoy configurations in real-world driving
scenarios. The detailed examination of these specific fuel
consumption values across varying convoy distances
provides a comprehensive understanding of the fuel
efficiency dynamics within the context of formation
driving.
In the Intelligent Highway System, communication
and information exchange between vehicles are very
important. By using MBSE methodology, we can model
and describe the system, thereby better understanding
and optimizing the performance of the system.
In the simulation experiment, we observed the changes
in stopping distance under different time delays and the
changes in average fuel consumption under different
range. These data indicate that the MBSE methodology
can effectively model and describe the smart expressway
system. By using this methodology, we can better
understand the performance of the system and optimize
it for different scenarios and conditions.
In addition, the MBSE methodology can also help
design and implement other functions in the Intelligent
Highway System better, such as autonomous driving and
intelligent traffic control. By using this methodology, we
can deeply understand the various components of the
system and their relationships, thereby better designing
and implementing these functions.
In summary, the effectiveness of the MBSE
methodology for modeling CHPC system has been
validated by simulation results, and the methodology can
also make it more outstanding to design and implement
other functions in CHPC system.
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the Intelligent Highway System. It enables
comprehensive and precise definition of requirements,
enhances traceability of requirement information,
ensures consistency between system design and
requirements, improves model reusability and
architectural scalability, and enhances overall modeling
and architectural design efficiency and accuracy.
3. A method for constructing a collaborative
simulation platform for the Intelligent Highway System
is proposed, based on Catia Magic, Matlab, and Prescan.
Firstly, the Intelligent Highway System model and
specific scenarios are defined. Then, multiple operational
scenarios are simulated according to specific scenarios.
Finally, a collaborative simulation platform for the
Intelligent Highway System is built based on Catia
Magic, Matlab, and Prescan, enabling unified
architecture construction, interface implementation, and
co-simulation. It accomplishes closed-loop research on
the architecture, model, algorithm, and application
verification of the Intelligent Highway System.
4. The impact of communication delay on platoon
driving and the influence of different spacing on convoy
fuel consumption are validated through simulation cases.
The results demonstrate that the low-delay scheme can
improve the final stopping distance, and the average fuel
consumption decreases initially and then increases as the
convoy distance increases.
In future work, more testing scenarios for the
Intelligent Highway System will be addressed through
co-simulation. Additionally, field experiments will be
conducted to validate the effectiveness and authenticity
of the proposed architecture and simulation platform.
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References
1.
6 Conclusions
This article models the Intelligent Highway System
using MBSE and verifies the feasibility and rationality of
the model through co-simulation. The specific
contributions are as follows:
1. The RFLP modeling process is utilized to model the
Intelligent Highway System, including requirements
analysis, functional analysis, logical architecture design,
and system parameter calibration. SysML modeling
language and Catia Magic modeling tools are employed
to construct system models corresponding to each stage,
enabling visualization and digital description of the
system model. This integrated Intelligent Highway
System model achieves a hierarchical and interconnected
structure of requirements, functions, logic, and
parameters.
2. The modeling results demonstrate the effectiveness
of the MBSE approach in addressing modeling issues of
2.
3.
4.
5.
Cui Y, Lei D. Design of highway intelligent
transportation system based on the internet of
things and artificial intelligence[J]. IEEE Access,
2023, 11: 46653-46664.
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