Distributed Learning for Vehicle Routing Decision in Software Defined Internet of Vehicles

Authors:
Publisher website
Google Scholar
Abstract
With the increasing number of vehicles, the traffic congestion is becoming more and more serious. In order to alleviate such a problem, this article considers transmission and inference delay of cloud centralized computing in the software defined Internet of Vehicles (SDIoV), and builds a new SDIoV architecture based on edge intelligence, for supporting real-time vehicle routing decision through distributed multi-agent reinforcement learning model. Then, a software defined device collaboration optimization method is designed to improve the efficiency of distributed training. Combined with multi-agent reinforcement learning, a distributed-learning-based vehicle routing decision algorithm (DLRD) is proposed to adaptively adjust vehicle routing online. The performed simulations show that the DLRD can successfully realize real-time routing decision for vehicles and alleviate traffic congestion with the dynamic changes of the road environment.
Funding Information
  • Liaoning Province Higher Education Innovative Talent Support Program
  • Fundamental Research Funds for the Central Universities (DUT19JC22, DUT18RC(3)069)
  • National Natural Science Foundation of China (U1908214)
  • Italian MIUR, PRIN 2017 Project Fluidware (CUP H24I17000070001)
  • National Key Research and Development Program of China (2020YFB170458, 2018YFB1600600)

This publication has 36 references indexed in Scilit: