research-article

Eco-CSAS: A Safe and Eco-Friendly Speed Advisory System for Autonomous Vehicle Platoon Using Consortium Blockchain

Authors:

Long ChengAuthors Info & Claims

IEEE Transactions on Intelligent Transportation Systems, Volume 24, Issue 7

Pages 7802 - 7812

https://doi.org/10.1109/TITS.2022.3232851

Published: 01 July 2023 Publication History

Abstract

Future worldwide 6G research will drive the evolution of emerging intelligent control technologies, such as intelligent speed advisory systems (ISA), to a more advanced generation. As a special type of ISA, consensus-based speed advisory systems (CSAS) can be widely used to recommend a consensus speed for a vehicle platoon, enabling minimizing energy consumption or emissions over a planned route. Recently, speed recommendation services that protect data privacy (i.e., how to obtain an optimal speed in a privacy-preserving way) have drawn tremendous attention. However, current approaches could still encounter service trust issues with central servers and the malicious behavior of vehicles. Furthermore, existing research lacks considering road safety constraints (i.e., safe distance between adjacent vehicles and road speed limits) that are essential for the practical deployment of CSAS. To address the above issues, this paper proposes Eco-CSAS, a safe and eco-friendly consensus speed advisory system using blockchain. We formulate an optimization problem subject to the minimum following distance and maximum road speed limit to minimize the energy consumption of the automatic vehicle platoon. In addition, we introduce a consortium blockchain and cryptographic primitives to ensure service trust and data privacy. We implement the system on the Hyperledger platform, and experimental results show that the system can achieve speed recommendations in a trustworthy and privacy-preserving manner while ensuring a secure platoon.

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Cited By

Chang XLiu YHuang LLi JLiang YLi SSun Y(2023)Blockchain Threat Intelligence Knowledge Graph Alignment via Graph Convolutional NetworksProceedings of the 2023 International Conference on Information Education and Artificial Intelligence10.1145/3660043.3660119(421-430)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3660043.3660119
Dong HZhuang WWu GLi ZYin GSong Z(2023)Overtaking-Enabled Eco-Approach Control at Signalized Intersections for Connected and Automated VehiclesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332802225:5(4527-4539)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3328022

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cover image IEEE Transactions on Intelligent Transportation Systems

IEEE Transactions on Intelligent Transportation Systems Volume 24, Issue 7

July 2023

1120 pages

ISSN:1524-9050

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1558-0016 © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 July 2023

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Cited By

Chang XLiu YHuang LLi JLiang YLi SSun Y(2023)Blockchain Threat Intelligence Knowledge Graph Alignment via Graph Convolutional NetworksProceedings of the 2023 International Conference on Information Education and Artificial Intelligence10.1145/3660043.3660119(421-430)Online publication date: 22-Dec-2023
https://dl.acm.org/doi/10.1145/3660043.3660119
Dong HZhuang WWu GLi ZYin GSong Z(2023)Overtaking-Enabled Eco-Approach Control at Signalized Intersections for Connected and Automated VehiclesIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.332802225:5(4527-4539)Online publication date: 7-Nov-2023
https://dl.acm.org/doi/10.1109/TITS.2023.3328022

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