An Autonomous Vehicle Group Cooperation Model in an Urban Scene

Guiyuan Yuan, Jiujun Cheng, Meng Chu Zhou, Sheng Cheng, Shangce Gao, Changjun Jiang, Abdullah Abusorrah

Research output: Contribution to journalArticlepeer-review

Abstract

Formulating a cooperative autonomous vehicle group is challenging in an urban scene that has complex road networks and diverse disturbance. Existing methods of vehicle cluster cooperation in a vehicular ad-hoc network cannot be applied to autonomous vehicles because the latter have different requirements for a vehicle group structure and communication quality. Existing studies focus on autonomous vehicle group cooperation in closed and highway scenes only. Their outcomes cannot be directly applied to an urban scene because of its complex road conditions, incomplete cooperation properties, and lack of a vehicle group size control strategy. In this work, we formulate a cooperation model for autonomous vehicle groups in such scene. First, we analyze cooperation criteria based on the non-colliding aggregate motion of flocks and deduce the connectivity, coupling, timeliness, evolvability, and adaptivity of a vehicle group, based on which we propose a cooperation model. Next, we solve our model by using a modified distributed evolutionary multi-objective optimization method, prove its convergence, and analyze its computational complexity. Finally, we conduct simulations on synthetic and real roads to show its performance in terms of average connectivity, coupling, timeliness, evolvability, and adaptivity of vehicle groups.

Original languageEnglish (US)
Pages (from-to)13852-13862
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number12
DOIs
StatePublished - Dec 1 2023

All Science Journal Classification (ASJC) codes

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Keywords

  • Urban scene
  • autonomous vehicle group
  • cooperation model
  • multi-objective optimization

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