A Dynamic Evolution Method for Autonomous Vehicle Groups in a Highway Scene

Jiu Jun Cheng, Ming Dong Ju, Meng Chu Zhou, Cong Liu, Shang Ce Gao, Abdullah Abusorrah, Chang Jun Jiang

Research output: Contribution to journalArticlepeer-review

Abstract

Vehicle groups that are composed of autonomous vehicles can increase the perception range of vehicles, and their dynamic evolution can provide guidance for the operation of autonomous vehicles. Most existing studies on vehicle group formation neither propose a standard vehicle group model, nor consider vehicle mobility and dynamic topology of vehicle groups. Instead they focus on detecting dynamic evolution without predicting it. This work proposes a dynamic evolution method for autonomous vehicle groups. It first defines five vehicle states and their transitions. Then, it proposes an autonomous vehicle group formation method based on vehicle states and formulates an autonomous vehicle group model. Next, it uses meta vehicle group sequences to manage vehicle groups at different times. Finally, it gives detection and prediction methods of vehicle group dynamic evolution. Extensive simulation results show that the proposed method can be used to establish interconnection among autonomous vehicle nodes, detect dynamic evolution characteristics inside a vehicle group precisely, and predict dynamic evolution trends of vehicle groups effectively.

Original languageEnglish (US)
JournalIEEE Internet of Things Journal
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

Keywords

  • Autonomous vehicle
  • Autonomous vehicles
  • Heuristic algorithms
  • Internet of Things
  • Power system dynamics
  • Road transportation
  • Topology
  • Vehicle dynamics
  • autonomous vehicle group
  • dynamic evolution
  • highway scene.

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