Trajectory-based data Forwarding with future Neighbor Prediction in autonomous driving vehicular environments

Chi En Chang, Yu Yuan Lin, Kuo-Feng Ssu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

In VANETs, vehicle-to-vehicle communication is a challenge due to the fast topology changing, frequent disconnections, and highly variable traffic densities. This paper describes a Trajectory-based data Forwarding scheme with future Neighbor Prediction (TFNP) in autonomous driving vehicular environments. The most important characteristic of the environments is that the prediction for future position of vehicles could be sufficiently accurate. With the trajectory information of all autonomous vehicles, the future neighbors of each vehicle can be properly identified. The packet forwarding ability of the future neighbors is evaluated and forwarding sequences are thus constructed. When an autonomous vehicle needs to send data, it will transmit packets based on the constructed forwarding path. The simulation results demonstrate the effectiveness and efficiency of the developed scheme in the autonomous driving vehicular environments.

Original languageEnglish
Title of host publicationProceedings of the 40th Annual IEEE Conference on Local Computer Networks, LCN 2015
EditorsSalil Kanhere, Soumaya Cherkaoui, Jens Tolle
PublisherIEEE Computer Society
Pages884-892
Number of pages9
ISBN (Electronic)9781467367738
DOIs
Publication statusPublished - 2015 Dec 24
Event40th Annual IEEE Conference on Local Computer Networks, LCN 2015 - Clearwater Beach, United States
Duration: 2015 Oct 262015 Oct 29

Publication series

NameProceedings - Conference on Local Computer Networks, LCN
Volume2015-December

Other

Other40th Annual IEEE Conference on Local Computer Networks, LCN 2015
CountryUnited States
CityClearwater Beach
Period15-10-2615-10-29

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

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