A node trust evaluation method of vehicle-road-cloud collaborative system based on federated learning

Wang, Denghui; Yi, Yuping; Yan, Sheng; Wan, Na; Zhao, Junhui

doi:10.1016/j.adhoc.2022.103013

Cited by 17 publications

(3 citation statements)

References 25 publications

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“…For example, by analyzing a physical battery's operating conditions and environment, it can be examined whether there are dangers such as overcharging and over-discharging. Then, virtual simulation can be FIGURE 11 Vehicle-road-cloud collaborative control system architecture [143]. carried out through the digital twin battery in the cloud computing platform to analyze the damage and harm severity caused by extreme working conditions to the inside of the battery and analyze the evolution of its safety boundary.…”

Section: Temperature Prediction Based On Vehicle-cloud Collaborationmentioning

confidence: 99%

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

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Remaining driving range (RDR) research has continued to consistently evolve with the development of electric vehicles (EVs). Accurate RDR prediction is a promising approach to alleviate distance anxiety when power battery technology is not yet fully matured. This paper first introduces the research motivation of RDR prediction, summarizes the previous research progress, and classifies the influencing factors of RDR. Second, conduct research and analysis on the physical model of EVs, mainly including battery and vehicle models. Based on the physical model, the energy flow problem of EVs is analyzed and discussed. Third, four key challenges of RDR prediction are summarized: battery state estimation, driving behavior classification and recognition, driving condition prediction and speed prediction, and RDR calculation method. Finally, given the four challenges faced by RDR, a driving range prediction method based on vehicle‐cloud collaboration is proposed, which combines the advantages of cloud computing and machine learning to provide further research trends.

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Section: Temperature Prediction Based On Vehicle-cloud Collaborationmentioning

confidence: 99%

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Mei

Karimi

Huang

et al. 2023

IET Control Theory & Appl

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“…While the ledger is used as a permissioned blockchain to store and distribute detection alerts of attacks, several ML models were analysed (e.g., MLP, Decision Trees, Random Forest, SVM) in the context of federated learning. Wang et al [160] likewise investigated the possibility of applying federated learning in the context of heterogeneous VANET trust environments. Behavioural modelling was also considered by Ma et al [161], which entailed a Long Short-Term Memory (LSTM) NN to build a baseline model trained on QoS trust properties to detect similarities between the real and modelled IoT devices.…”

Section: G Machine Learning Modelsmentioning

confidence: 99%

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

Lenard,

Collen,

Benyahya

et al. 2023

IEEE Access

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“…The "vehicle-road-cloud collaboration" [1] centered around intelligent connected AD provides a constructive framework for the governance of road traffic. The operation of intelligent connected AVs necessitates the collaborative coordination of elements such as dedicated road networks for AD, cloud-based control and dispatch systems, and intelligent vehicle sensor perception.…”

Section: Introductionmentioning

confidence: 99%

Autonomous driving road network generation and evaluation based on generative artificial intelligence

Ye,

Li,

Yang

et al. 2024

International Conference on Smart Transportation and City Engineering (STCE 2023)

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The increasing number of vehicles has led to issues such as traffic congestion, air pollution, and energy consumption. Intelligent connected autonomous driving (AD) offers a solution to these transportation challenges, but it requires cooperation among dedicated AD road networks, connected cloud-based control and dispatch systems, and intelligent vehicle sensing. To harness the potential of AD, it is crucial to consider the requirements of autonomous vehicles (AVs) when planning road networks. Well-designed road networks can enhance travel experiences, reduce energy consumption, and promote urban sustainability. In order to meet the needs of AD while ensuring the operational efficiency of urban transportation networks, this paper introduces a road network evaluation framework that encompasses both static and dynamic assessment criteria, following a detailed exploration and analysis of various road network generation methods. This evaluation framework addresses the deficiency in existing road network generation methods, which lack dynamic assessment of traffic networks. To validate the proposed approach, we have chosen a road network generation method based on conditional generative adversarial network (CGAN) as an application example. Through carefully designed experiments, we confirm the feasibility and effectiveness of the proposed method for generating and evaluating road networks tailored for AD scenarios.

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A node trust evaluation method of vehicle-road-cloud collaborative system based on federated learning

Cited by 17 publications

References 25 publications

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Remaining driving range prediction for electric vehicles: Key challenges and outlook

Exploring Trust Modeling and Management Techniques in the Context of Distributed Wireless Networks: A Literature Review

Autonomous driving road network generation and evaluation based on generative artificial intelligence

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