Cooperative Ramp Merging Design and Field Implementation: A Digital Twin Approach Based on Vehicle-to-Cloud Communication

Liao, Xishun; Wang, Ziran; Zhao, Xuanpeng; Han, Kyungtae; Tiwari, Prashant; Barth, Matthew; Wu, Guoyuan

doi:10.1109/tits.2020.3045123

Cited by 68 publications

(25 citation statements)

References 22 publications

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“…Stem cells are cells with unlimited or immortal self-renewal capacity, which can produce at least one type, highly differentiated progeny cells, so they are called “universal cells” in the medical profession [ 9 – 11 ]. According to the classification of developmental stages, stem cells can be divided into embryonic stem cells and adult stem cells [ 12 – 14 ].…”

Section: Theories and Methodsmentioning

confidence: 99%

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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The current work aims to strengthen the research of segmentation, detection, and tracking methods of stem cell image in the fields of regenerative medicine and tissue damage restoration. Firstly, based on the relevant theories of stem cell image segmentation, digital twins (DTs), and lightweight deep learning, a new phase contrast microscope is introduced through the research of optical microscope. Secondly, the results of DTs method and phase contrast imaging principle are compared in stem cell image segmentation and detection. Finally, a lightweight deep learning model is introduced in the segmentation and tracking of stem cell image to observe the gray value and mean value before and after stem cell image movement and stem cell division. The results show that phase contrast microscope can increase the phase contrast and amplitude difference of stem cell image and solve the problem of stem cell image segmentation to a certain extent. The detection results of DTs method are compared with phase contrast imaging principle. It indicates that not only can DTs method make the image contour more accurate and clearer, but also its accuracy, recall, and F1 score are 0.038, 0.024, and 0.043 higher than those of the phase contrast imaging method. The lightweight deep learning model is applied to the segmentation and tracking of stem cell image. It is found that the gray value and mean value of stem cell image before and after movement and stem cell division do not change significantly. Hence, the application of DTs and lightweight deep learning methods in the segmentation, detection, and tracking of stem cell image has great reference significance for the development of biology and medicine.

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Section: Theories and Methodsmentioning

confidence: 99%

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning

Liu²,

Sun

2022

Computational Intelligence and Neuroscience

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“…As a promising technology of virtualization, CT constructs digital replicas for vehicles in the cyberspace. A cybertwin based approach that utilizes vehicle-to-cloud communication is proposed to realize cooperative ramp merging [19]. By monitoring the operation state of physical vehicles, several cybertwin-assisted schemes are proposed by combining computation capability and traffic data, so as to realize service offloading and traffic prediction [20], [21].…”

Section: Related Workmentioning

confidence: 99%

CyberChain: Cybertwin Empowered Blockchain for Lightweight and Privacy-Preserving Authentication in Internet of Vehicles

Chai

Leng

et al. 2022

IEEE Trans. Veh. Technol.

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Internet of Vehicles (IoVs) presents promising opportunities for vehicle to everything (V2X) applications, wherein authentication acts as the cornerstone to realize trustworthy vehicular context and to support advanced applications. However, existing authentication schemes mainly depend on centralized servers with both security and privacy issues. In this paper, we propose a CyberTwin (CT) empowered blockchain framework for authentication, namely CyberChain, to reduce both the communication and storage cost while maintaining vehicular privacy. By designing a blockchain system in the cyberspace, we decouple the consensus process from the physical world, so that the operation cost of blockchain can be reduced. A Privacy-Preserving Parallel Pedersen Commitment (P4C) algorithm is designed to protect the privacy of vehicles and accelerate the authentication process. To further enhance the operation efficiency of CyberChain, we propose a Diffused Practical Byzantine Fault Tolerance (DPBFT) mechanism to reach consensus in the cyberspace that can reduce consensus latency. The proposed cyberchain framework and the associated mechanisms are evaluated by qualitative analysis and simulations. The evaluation results demonstrated that the proposed cyberchain based framework significantly improves the authentication performance in terms of authentication latency, privacy, communication overhead and storage cost.

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“…As shown in Fig. 3 In this simulation, we set the time-variant communication delay based on the results of our previous field test regarding 4G LTE-based V2C (not standard C-V2X) communications [34]. Note that this communication technology normally has higher communication delays than DSRC or C-V2X, so we adopt it in the simulation as a stress test of our motion estimation methodology.…”

Section: Game Engine Simulation Environmentmentioning

confidence: 99%

Motion Estimation of Connected and Automated Vehicles under Communication Delay and Packet Loss of V2X Communications

Wang,

Han,

Han

2021

Preprint

Self Cite

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The emergence of the connected and automated vehicle (CAV) technology enables numerous advanced applications in our transportation system, benefiting our daily travels in terms of safety, mobility, and sustainability. However, vehicular communication technologies such as Dedicated Short-Range Communications (DSRC) or Cellular-Based Vehicle-to-Everything (C-V2X) communications unavoidably introduce issues like communication delay and packet loss, which will downgrade the performances of any CAV applications. In this study, we propose a consensus-based motion estimation methodology to estimate the vehicle motion when the vehicular communication environment is not ideal. This methodology is developed based on the consensus-based feedforward/feedback motion control algorithm, estimating the position and speed of a CAV in the presence of communication delay and packet loss. The simulation study is conducted in a traffic scenario of unsignalized intersections, where CAVs coordinate with each other through V2X communications and cross intersections without any full stop. Game engine-based human-in-the-loop simulation results shows the proposed motion estimation methodology can cap the position estimation error to ±0.5 m during periodic packet loss and time-variant communication delay.

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Cooperative Ramp Merging Design and Field Implementation: A Digital Twin Approach Based on Vehicle-to-Cloud Communication

Cited by 68 publications

References 22 publications

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning

Segmentation, Detection, and Tracking of Stem Cell Image by Digital Twins and Lightweight Deep Learning

CyberChain: Cybertwin Empowered Blockchain for Lightweight and Privacy-Preserving Authentication in Internet of Vehicles

Motion Estimation of Connected and Automated Vehicles under Communication Delay and Packet Loss of V2X Communications

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