A Mobility Digital Twin (MDT) Framework for Connected Vehicles Using Cloud-Edge Computing

Table of Contents

Overall Summary

Overview

This paper proposes a Mobility Digital Twin (MDT) framework to improve mobility services by creating a digital replica of the transportation ecosystem. This framework includes digital representations of humans, vehicles, and traffic, interacting in real-time with their physical counterparts through a cloud-edge-device architecture. Using Amazon Web Services (AWS), the framework supports real-time monitoring, modeling, and prediction to improve existing and future mobility services. A personalized adaptive cruise control (P-ACC) system demonstrates the MDT's practical application. The framework's potential to improve services by incorporating personalized data and real-time feedback is significant, and the paper also addresses future challenges like standardization and AI integration.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure 2

Description: Figure 2 depicts the four-layered architecture of the MDT framework, showcasing the cloud, edge, device, and API layers. It visually represents the data flow and interaction between these layers, highlighting the use of AWS services and other technologies.

Relevance: This figure is crucial for understanding the system's architecture and how it supports the MDT framework's functionality.

Table I

Description: Table I presents the P-ACC case study results, comparing takeover percentages between traditional ACC and the personalized P-ACC. It quantitatively demonstrates the significant reduction in takeover events achieved by the P-ACC system.

Relevance: This table provides strong evidence supporting the effectiveness of the MDT framework and the P-ACC system in improving driver comfort and potentially safety.

Conclusion

This paper presents a novel Mobility Digital Twin (MDT) framework for connected vehicles using a cloud-edge computing architecture built on AWS. The framework integrates digital twins of humans, vehicles, and traffic, enabling real-time data exchange and personalized services. The P-ACC case study demonstrates the framework's practical effectiveness by significantly reducing driver takeover events compared to traditional ACC. While the architecture and case study showcase the MDT's potential, future research should address security and privacy concerns, further investigate the role of edge computing, and explore the framework's generalizability to diverse traffic scenarios and vehicle types. Continued development in these areas will pave the way for broader adoption and unlock the full potential of MDT technology to revolutionize transportation systems.

Section Analysis

Abstract

Overview

This paper proposes a Mobility Digital Twin (MDT) framework, an AI-driven system for enhancing mobility services. It replicates human, vehicle, and traffic elements in a digital space, using cloud-edge-device interactions. The framework allows for real-time monitoring, modeling, and prediction to improve services like personalized adaptive cruise control (P-ACC). A cloud-edge architecture using Amazon Web Services (AWS) supports the framework, and a P-ACC case study demonstrates its effectiveness compared to traditional systems. The paper also discusses future challenges like standardization and the role of AI.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

The introduction sets the stage for the Mobility Digital Twin (MDT) framework by highlighting the growing importance of the Internet of Things (IoT) in various sectors, including transportation. It emphasizes the increasing attention and market value of Digital Twin technology, particularly in the automotive industry. The paper points out the lack of a holistic framework connecting different mobility entities (human, vehicle, and traffic) and positions the proposed MDT framework as a solution. The introduction also outlines the contributions of the MDT framework, such as its power, shareability, manageability, and extendibility, and provides a roadmap for the rest of the paper.

Key Aspects

Strengths

Suggestions for Improvement

Literature Review

Overview

This section reviews existing research on cloud computing and Digital Twins in the context of connected vehicles. It discusses transportation applications using cloud computing, highlighting the limitations of traditional systems that rely on onboard resources. The review also covers the use of Digital Twins for connected vehicles, noting the confusion surrounding its definition and its connection to other technologies like model-based design. The section emphasizes the need for a holistic framework that connects various mobility entities (human, vehicle, and traffic), which is the focus of the current paper. It also mentions related work on parallel driving and recent studies by the authors on Digital Twins for connected vehicles, highlighting the limitations of these studies in terms of cloud architecture, data structures, and workflows.

Key Aspects

Strengths

Suggestions for Improvement

Mobility Digital Twin Concept

Overview

This section introduces the Mobility Digital Twin (MDT) framework, which consists of three interconnected planes: the physical space (human, vehicle, traffic), the digital space (digital replicas), and the communication plane facilitating data exchange between them. The MDT leverages IoT technologies to connect these entities, enabling real-time data flow and synchronization. The section details the functions of each plane and their building blocks, emphasizing the importance of the communication plane in maintaining synchronization between the physical and digital worlds.

Key Aspects

Strengths

Suggestions for Improvement

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figure 1

This figure illustrates the Mobility Digital Twin (MDT) framework. It shows the interaction between the physical space (human, vehicle, and traffic), the digital space (Human Digital Twin, Vehicle Digital Twin, and Traffic Digital Twin), and the communication plane connecting them. Each digital twin has an associated data lake. The communication plane facilitates data flow between the physical and digital spaces, enabling real-time updates and synchronization.

First Mention

Text: "The MDT framework proposed in this study, as shown in Fig. 1, consists of three planes"

Context: This section introduces the core concept of the Mobility Digital Twin (MDT) framework, explaining its three main components: the physical space, the digital space, and the communication plane connecting them. It emphasizes the role of the communication plane in enabling real-time data exchange and synchronization between the physical and digital worlds.

Relevance: This figure is crucial for understanding the core concept of the MDT framework and how it connects the physical and digital worlds to enable real-time monitoring, modeling, and prediction for improved mobility services.

Critique
Visual Aspects
  • The figure could benefit from a clearer visual separation between the physical and digital spaces. Perhaps using different background colors or adding a border could enhance this distinction.
  • The arrows representing data flow could be made more prominent and directional to better illustrate the two-way communication between the physical and digital spaces.
  • Adding a legend explaining the different elements and their functionalities would improve the figure's accessibility to a wider audience.
Analytical Aspects
  • The figure could include a simplified representation of the data flow within the digital space, showing how the different digital twins interact and share information.
  • The figure could visually represent the types of data being exchanged between the physical and digital spaces, such as sensor data, control commands, or model parameters.
  • The figure could highlight the role of AI and machine learning within the digital twins, perhaps by adding icons or labels representing these functionalities.
Numeric Data

Example Architecture with Cloud-Edge Computing

Overview

This section details an example architecture implementing the MDT framework using cloud and edge computing. It leverages AWS for cloud services and incorporates an edge layer for low-latency processing. The architecture is divided into four layers: cloud, edge, device, and API. The cloud layer handles data storage, analytics, and microservices. The edge layer manages communication, computing, and temporary data caching. The device layer represents real-world entities like vehicles and simulators. The API layer connects the system to external data sources. This architecture enables real-time and batch data processing, supporting the MDT's functionalities.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure 2

This figure presents a four-layered architecture designed to implement the Mobility Digital Twin (MDT) concept using cloud and edge computing. The layers are: 1) Cloud Layer (built on AWS and its Virtual Private Cloud), containing data lakes, microservices, model training, and real-time processing components; 2) Edge Layer, responsible for storage, computing, and communication between the cloud and devices; 3) Device Layer, comprising mobile apps, simulators, real vehicles, and RC vehicles, which generate data and receive instructions; and 4) API Layer, connecting the cloud to external APIs for additional data sources like traffic, map, and weather information. Arrows indicate the flow of data and interactions between the layers.

First Mention

Text: "As shown in Fig. 2, the architecture can be divided into four layers"

Context: This section details the architecture of the proposed MDT framework, which leverages cloud and edge computing. It describes the four layers of the architecture (cloud, edge, device, and API) and how they interact to enable real-time and bulk data processing for various mobility applications.

Relevance: This figure is essential for understanding how the MDT framework can be implemented in a real-world setting using cloud and edge computing. It provides a visual representation of the system's architecture and the flow of data between different components.

Critique
Visual Aspects
  • While the layered structure is clear, visually separating the layers with distinct backgrounds or borders could improve readability.
  • The arrows indicating data flow could be thicker and more directional to enhance clarity. Labeling the arrows with the type of data being exchanged (e.g., sensor data, control commands) would be beneficial.
  • Using different icons or visual cues to represent specific technologies within each layer (e.g., AWS, Kafka, MQTT) could make the diagram more informative.
Analytical Aspects
  • The figure could benefit from a more detailed representation of the data flow within the cloud layer, showing how data is processed and used by different microservices.
  • The figure could visually highlight the security measures implemented at different layers, such as encryption or access control, to address potential security concerns.
  • The figure could include a simplified representation of the interaction between the MDT architecture and the three digital twins (human, vehicle, and traffic) to better connect the architecture to the overall framework.
Numeric Data
figure 2

This figure illustrates the architecture of the proposed Mobility Digital Twin (MDT) system, which comprises four layers: Cloud Layer, Edge Layer, Device Layer, and API Layer. The Cloud Layer, within an Amazon VPC, includes Data Lakes, Analytics Workbench, Data Stores, Distributed Real-time Processing, Rule Engine, AI/ML Framework & Digital Twin Microservices, Bulk Data Ingestion, and OpenID Connect for authentication. The Edge Layer handles Edge Computing and Communication. The Device Layer consists of Mobile Apps, Real & RC Vehicles, and Simulators. The API Layer connects to external data sources like Traffic, Map, and Weather Data via an API Gateway, Customized Event Query API, and a Customized Web Portal.

First Mention

Text: "As shown in Fig. 2, the architecture can be divided into four layers"

Context: This section presents a detailed architecture for the proposed MDT concept, leveraging cloud and edge computing. It describes the four layers of the architecture (cloud, edge, device, and API) and their respective components, explaining how they interact to enable real-time and bulk data processing for various mobility applications.

Relevance: This figure is crucial for understanding the detailed architecture of the MDT system and how different components interact to support the framework's functionalities. It shows the specific technologies and services used in each layer, providing a concrete implementation of the MDT concept.

Critique
Visual Aspects
  • The diagram is quite dense; grouping related components within each layer and using visual separators could improve clarity.
  • The arrows representing data flow, while directional, could be labeled with the type of data being exchanged to enhance understanding.
  • Different colors or icons could be used to distinguish between different types of components (e.g., data stores, processing units, communication channels) within each layer.
Analytical Aspects
  • The diagram could better illustrate the interaction between the different digital twins (human, vehicle, and traffic) within the cloud layer.
  • The security aspects of the architecture could be visually highlighted, showing how data is protected during transmission and storage.
  • The diagram could include a simplified representation of the data processing pipeline, showing how data flows from the device layer to the cloud layer and back, highlighting key processing steps.
Numeric Data

Example Microservices and Case Study

Overview

This section details example microservices within each Digital Twin (Human, Vehicle, Traffic) and presents a case study of a Personalized Adaptive Cruise Control (P-ACC) system. The Human Digital Twin demonstrates user management and driver classification. The Vehicle Digital Twin showcases cloud-based ADAS features. The Traffic Digital Twin covers traffic flow monitoring and variable speed limits. The P-ACC case study integrates these microservices, demonstrating improved performance over traditional ACC by incorporating personalized driver behavior, vehicle type, and environmental factors.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure 3

This figure shows a real-world demonstration of the cloud-based driver scoring system in Mountain View, CA. The system is used for user management and driver type classification. The image depicts a dashboard display inside a vehicle, showing scores for Safety (75), Comfort (83), Eco (97), and an Overall score (85). It also displays the current speed (16 mph) and indicators for 'Trip' and 'Cloud'. The 'CONNECTED CAR CLOUD DOWNLINK' banner suggests real-time data transfer from the cloud to the vehicle.

First Mention

Text: "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."

Context: This subsection discusses example microservices of the Human Digital Twin, including user management and driver type classification. It describes a web portal for data management and visualization, and how driver scores are calculated and used for classification.

Relevance: This figure demonstrates a practical application of the Human Digital Twin, showing how real-time driver scoring and feedback can be implemented using the MDT framework. It highlights the connection between data collected in the physical space and its use for user management and driver classification in the digital space.

Critique
Visual Aspects
  • The figure could benefit from a clearer explanation of how the scores are calculated. Adding a brief description of the underlying metrics and algorithms used by the MOVESTAR model would be helpful.
  • The visual design of the dashboard could be improved to make it more intuitive and user-friendly. For example, using color-coding to represent different score ranges could enhance readability.
  • The figure could show different examples of driver classifications (e.g., reckless, intermediate, competent) to illustrate how the scores are used for categorization.
Analytical Aspects
  • The figure could provide more context about how the driver scores are used for user management. For example, explaining how different user groups (viewer, supervisor, admin) interact with the scoring system would be helpful.
  • The figure could discuss the implications of driver classification for personalized services. For example, how might the system adapt its behavior based on the driver's classification?
  • The figure could address potential privacy concerns related to collecting and storing driver data. Explaining how the system ensures data security and anonymity would be important.
Numeric Data
  • Safety Score: 75
  • Comfort Score: 83
  • Eco Score: 97
  • Overall Score: 85
  • Speed: 16 mph
figure 4

This figure shows the user management page on the web portal of the MDT. It displays the user's driver risk class (reckless, intermediate, or competent) using a circular gauge chart. In this specific instance, the driver is classified as 'Intermediate'. The page also lists past trips with details like start and end times, vehicle name, total distance, and completion status. Clickable links (in light blue) provide access to more details about each trip.

First Mention

Text: "An example classification result is visualized on the Web portal (currently shown as “Competent” in Fig. 4)"

Context: This subsection describes how driver scores are calculated in real-time and used for driver type classification. It explains how the classification result is visualized on the web portal and how historical data can be accessed.

Relevance: This figure illustrates the user management and driver classification aspects of the Human Digital Twin. It shows how the MDT framework stores and presents driver data, including risk assessment and trip history, enabling personalized services and user management functionalities.

Critique
Visual Aspects
  • The circular gauge chart could be more visually informative. Using color-coding to represent different risk levels (e.g., red for reckless, yellow for intermediate, green for competent) would enhance its impact.
  • The table of past trips could benefit from visual cues, such as color-coding or icons, to highlight important information like long trips or incomplete trips.
  • The figure could include a brief explanation of the information available through the clickable links. This would give users a better understanding of the level of detail provided.
Analytical Aspects
  • The figure could explain how the driver risk class is determined. Describing the underlying algorithms and data used for classification would be helpful.
  • The figure could discuss how the trip history data is used for personalized services. For example, how might the system use this data to provide customized recommendations or feedback?
  • The figure could address data privacy concerns. Explaining how the system protects user data and ensures anonymity would be important.
Numeric Data
figure 5

This figure shows a human-in-the-loop simulation of a cloud-based Advanced Driver-Assistance System (ADAS). The simulation uses AWS for cloud computing, the Unity game engine for creating the virtual driving environment, and a Logitech G29 Driving Force steering wheel and pedals for human input. The image displays the in-car view of the simulation, along with two panels showing the data exchange between Unity and AWS. A smaller inset shows a person using the steering wheel, highlighting the 'human-in-the-loop' aspect. The 'Unity-AWS Uplink Message' panel likely shows the data being sent from the simulation to the cloud, while the 'AWS-Unity Downlink Message' panel shows the information being sent back from the cloud to the simulation, such as control commands or guidance information.

First Mention

Text: "As 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]."

Context: This subsection describes how a cloud-based ADAS works within the Vehicle Digital Twin. It explains how data is collected from a simulated vehicle in Unity, sent to the cloud (AWS) for processing, and then returned to the simulation as guidance information. The figure illustrates this process and the human driver's interaction with the simulation.

Relevance: This figure is important because it visually demonstrates how the Vehicle Digital Twin can be used to develop and test cloud-based ADAS functionalities. It shows the interaction between the simulation (representing the physical vehicle), the cloud platform (AWS), and the human driver, highlighting the real-time data exchange and control loop.

Critique
Visual Aspects
  • The content of the 'Uplink' and 'Downlink' message panels is difficult to read and understand. Displaying a few key data points with clear labels would be more informative.
  • The inset showing the driver interaction is too small. A larger image or a separate image focusing on the driver's interaction with the steering wheel and pedals would be more impactful.
  • The figure could benefit from visual cues to indicate the flow of data between the simulation, the cloud, and the driver. Arrows or color-coding could be used to highlight this flow.
Analytical Aspects
  • The figure doesn't clearly explain what types of data are being exchanged between Unity and AWS. Adding labels or annotations to indicate the data types (e.g., vehicle speed, steering angle, control commands) would improve understanding.
  • The figure doesn't show how the received downlink messages are used within the simulation. A visual representation of how these messages affect the simulated vehicle's behavior would be helpful.
  • The figure could be enhanced by showing how this simulation setup connects to the broader MDT framework. A simplified diagram linking the simulation to the Vehicle Digital Twin and other components of the MDT could provide valuable context.
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figure 9

This figure consists of two parts illustrating the real-world data collection process for the Personalized Adaptive Cruise Control (P-ACC) case study. Part (a) shows a Lexus LS, a mass-produced vehicle used in the study to collect real-world driving data. This emphasizes that the proposed framework is tested on a real car, not just in simulation. Part (b) shows a person driving the Lexus LS on Highway 237 in California. This image represents the 10-minute driving sessions used to gather Controller Area Network (CAN BUS) data, which is the vehicle's internal communication network. This data is then used to train and evaluate the P-ACC system.

First Mention

Text: "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."

Context: This subsection describes the real-world testing of the P-ACC system. It explains that data was collected from a Lexus LS vehicle during 10-minute drives on Highway 237 in California. The figure shows the vehicle and a driver inside, illustrating the data collection process.

Relevance: This figure is important because it grounds the P-ACC case study in real-world data. It shows the actual vehicle and driving scenario used to collect the CAN BUS data, which is crucial for training and validating the personalized driving models used in the P-ACC system.

Critique
Visual Aspects
  • Part (a) could be more informative by showing the sensors used for data collection, such as cameras or radar. Highlighting these sensors would visually connect the physical vehicle to the data being collected.
  • Part (b) could benefit from a map inset showing the section of Highway 237 where the data was collected. This would provide geographical context.
  • The images could be larger and of higher resolution to provide more detail.
Analytical Aspects
  • The figure caption could explain why a 10-minute driving session was chosen for data collection. Is this duration representative of typical driving behavior? What are the trade-offs of using shorter or longer sessions?
  • The figure could be enhanced by briefly describing the 15 data fields collected from the CAN BUS. Listing a few key data points would provide more insight into the data used for model training.
  • The figure could connect the data collection process to the broader MDT framework. A simple diagram showing how the CAN BUS data flows from the vehicle to the edge layer and then to the cloud could be helpful.
Numeric Data
  • Trip Duration: 10 minutes
TABLE I

Table I presents a comparison of takeover percentages between the proposed Personalized Adaptive Cruise Control (P-ACC) and traditional ACC. Takeover percentage represents the proportion of time during a trip when the driver intervenes by using the accelerator or brake pedals because they feel uncomfortable with the system's performance. Lower takeover percentages indicate better system performance and greater driver comfort.

First Mention

Text: "The results are presented in Table I."

Context: This subsection discusses the results of a real-world end-to-end test comparing the proposed P-ACC with traditional ACC. The key metric is the 'takeover percentage,' which measures how often drivers feel the need to intervene and take control of the vehicle. The results demonstrate a significant reduction in takeover events with the P-ACC system.

Relevance: This table provides quantitative evidence of the P-ACC system's effectiveness compared to traditional ACC. It directly supports the claim that the MDT framework improves driver comfort and reduces the need for interventions.

Critique
Visual Aspects
  • Clearly label the units (percentage) in the column headers for P-ACC and ACC to avoid ambiguity.
  • Consider adding a visual representation of the data, such as a bar chart, to quickly convey the magnitude of the improvement.
  • Use a consistent number of decimal places for all percentage values to maintain visual consistency.
Analytical Aspects
  • Provide a brief explanation of how the takeover percentage is calculated (takeover time / overall activation time).
  • Discuss the statistical significance of the observed improvement, perhaps by including p-values or confidence intervals.
  • Consider including additional metrics, such as average takeover duration or frequency, to provide a more comprehensive performance comparison.
Numeric Data
  • Driver A - P-ACC: 4.8 %
  • Driver A - ACC: 27.3 %
  • Driver A - Improvement: 81.1 %
  • Driver B - P-ACC: 2.1 %
  • Driver B - ACC: 24.2 %
  • Driver B - Improvement: 91.4 %
  • Driver C - P-ACC: 3.5 %
  • Driver C - ACC: 25.8 %
  • Driver C - Improvement: 86.7 %
  • Average Improvement: 86.4 %
TABLE II

Table II shows the latency results for processing 10 minutes of CAN bus data and querying the data lakes. It breaks down the latency into three components: Uplink Latency (time to upload data to the cloud), Cloud Computing + Downlink Latency (time for processing in the cloud and downloading results), and Overall Latency (total time from data sampling to receiving results). The table presents these latencies for different data frequencies (0.5 Hz, 1 Hz, 5 Hz, and 10 Hz), showing how the increasing data volume affects the uplink latency while the cloud processing time remains relatively constant.

First Mention

Text: "The end-to-end latency results are shown in Table II"

Context: This subsection discusses the end-to-end latency of the P-ACC system, which is the time it takes for data to be sampled from the vehicle, processed in the cloud, and for the results to be sent back to the vehicle. The latency is broken down into uplink, cloud processing, and downlink components, and the results are presented for different data sampling frequencies.

Relevance: This table provides insights into the system's responsiveness and the time required for different stages of data processing. It's important for understanding the feasibility of real-time applications and identifying potential bottlenecks.

Critique
Visual Aspects
  • Clearly label the units (seconds and MB) in the column headers to avoid any confusion.
  • Consider using bold font or highlighting to emphasize the overall latency values, as they are the key takeaway.
  • Add a brief explanation of what CAN bus data is and why it's being used in this context.
Analytical Aspects
  • Explain the reason for the constant cloud computing and downlink latency despite increasing data frequency. Is it due to parallel processing or some other optimization?
  • Discuss the implications of the observed latencies for real-time applications. Are these latencies acceptable for safety-critical functions like adaptive cruise control?
  • Consider including the standard deviation or range of latency values to provide a measure of variability across multiple test runs.
Numeric Data
  • Data Frequency (0.5 Hz) - Uplink Data Size: 12 MB
  • Data Frequency (0.5 Hz) - Uplink Latency: 1.4 s
  • Data Frequency (0.5 Hz) - Cloud Computing + Downlink Latency: 16.1 s
  • Data Frequency (0.5 Hz) - Overall Latency: 17.5 s
  • Data Frequency (1 Hz) - Uplink Data Size: 24 MB
  • Data Frequency (1 Hz) - Uplink Latency: 2.9 s
  • Data Frequency (1 Hz) - Cloud Computing + Downlink Latency: 16.1 s
  • Data Frequency (1 Hz) - Overall Latency: 19 s
  • Data Frequency (5 Hz) - Uplink Data Size: 120 MB
  • Data Frequency (5 Hz) - Uplink Latency: 14.1 s
  • Data Frequency (5 Hz) - Cloud Computing + Downlink Latency: 16.1 s
  • Data Frequency (5 Hz) - Overall Latency: 30.2 s
  • Data Frequency (10 Hz) - Uplink Data Size: 240 MB
  • Data Frequency (10 Hz) - Uplink Latency: 21.1 s
  • Data Frequency (10 Hz) - Cloud Computing + Downlink Latency: 16.1 s
  • Data Frequency (10 Hz) - Overall Latency: 37.2 s

Future Challenges

Overview

This section discusses the future challenges in developing and implementing Digital Twin technology for connected vehicles, focusing on standardization, AI integration, cloud platform choices, and heterogeneous communication environments. It highlights the need for specific standards for the transportation domain, the importance of robust AI techniques for cloud-edge computing, the trade-offs between public and private cloud platforms, and the complexities of managing diverse communication technologies for connected vehicles.

Key Aspects

Strengths

Suggestions for Improvement

Conclusion

Overview

This conclusion summarizes the key contributions of the paper, reiterating the development of the Mobility Digital Twin (MDT) framework for connected vehicles using cloud and edge computing. It emphasizes the novelty of the framework, particularly its integration of cloud-edge computing with detailed microservices for connected vehicles. The paper highlights the use of AWS for the cloud-edge architecture and the successful implementation of a Personalized Adaptive Cruise Control (P-ACC) case study demonstrating the framework's effectiveness. Finally, it concludes by mentioning key considerations for Digital Twin framework design (scalability, reliability, security, cost, latency, and fidelity) and anticipates further research in the mobility domain.

Key Aspects

Strengths

Suggestions for Improvement

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