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

Jiujun Cheng, Mingdong Ju, Mengchu Zhou, Cong Liu, Shangce Gao, Abdullah Abusorrah, Changjun Jiang

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


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)
Pages (from-to)1445-1457
Number of pages13
JournalIEEE Internet of Things Journal
Issue number2
StatePublished - Jan 15 2022

All Science Journal Classification (ASJC) codes

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


  • Autonomous vehicle group
  • autonomous vehicle
  • dynamic evolution
  • highway scene


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