IoV and blockchain-enabled driving guidance strategy in complex traffic environment

Yuchuan Fu, Changle Li, Tom H. Luan, Yao Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Diversified traffic participants and complex traffic environment (e.g., roadblocks or road damage exist) challenge the decision-making accuracy of a single connected and autonomous vehicle (CAV) due to its limited sensing and computing capabilities. Using Internet of Vehicles (IoV) to share driving rules between CAVs can break limitations of a single CAV, but at the same time may cause privacy and safety issues. To tackle this problem, this paper proposes to combine IoV and blockchain technologies to form an efficient and accurate autonomous guidance strategy. Specifically, we first use reinforcement learning for driving decision learning, and give the corresponding driving rule extraction method. Then, an architecture combining IoV and blockchain is designed to ensure secure driving rule sharing. Finally, the shared rules will form an effective autonomous driving guidance strategy through driving rules selection and action selection. Extensive simulation proves that the proposed strategy performs well in complex traffic environment, mainly in terms of accuracy, safety, and robustness.

Original languageEnglish
Pages (from-to)230-243
Number of pages14
JournalChina Communications
Volume20
Issue number12
DOIs
StatePublished - 1 Dec 2023

Keywords

  • autonomous driving guidance
  • blockchain
  • communication range
  • Internet of Vehicles
  • reinforcement learning

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