Hierarchical Cooperation and Load Balancing for Scalable Autonomous Vehicle Routing in Multi-Access Edge Computing Environment

Michael I.C. Wang, Charles H.P. Wen, H. Jonathan Chao

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

When Connected Autonomous Vehicles (CAVs) request routing in a driving environment with Autonomous Intersection Management (AIM) systems, a routing planner collects the demands and optimizes the routes in a coordinated manner to reduce the overall travel times. In practice, however, the routing demands are massive, especially in a large-scale traffic network. As a result, the centralized routing planner fails to scale out to accommodate the growing requests, causing a severe scalability issue. This paper presents a holistic solution for scalable CAV routing by enabling hierarchical cooperation and load balancing in the Multi-Access Edge Computing (MEC) environment. The proposed system cooperatively plans CAV routes and dynamically balances loads in MECs to handle massive requests. According to the experiments, our system has a 15.68X higher routing capacity than the centralized routing system, and load balancing reduces 14.51% computation time of routing. The experiments show that our system is scalable for massive autonomous vehicle routing.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Vehicular Technology
DOIs
StateAccepted/In press - 2023

Keywords

  • Autonomous vehicles
  • Delays
  • Load management
  • Real-time systems
  • Routing
  • Scalability
  • Vehicle routing

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