Index Coding of Point Cloud-Based Road Map Data for Autonomous Driving

Kai Fung Chu, Elmer R. Magsino, Wang Hei Ho, Chi Kin Chau

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

15 Citations (Scopus)

Abstract

Information exchange in a vehicular network between autonomous vehicles and the roadside infrastructure is important for improving road safety. These autonomous vehicles, equipped with a sensor suite, are capable of obtaining road map data that can be used to inform other vehicles and update the central road map repository through roadside units. The roadside infrastructure nodes act as local databases for distributing regional 3D road map data in form of point clouds to autonomous vehicles passing by. Since the vehicles might have various side information regarding the road network and traffic condition, minimizing the required number of transmissions to satisfy the demand of participating vehicles through network coding is an interesting research problem in road map data dissemination. In this paper, we propose the Road Map Data Encoding and Dissemination System (REDS) and evaluate its performance in a four-way junction scenario. It is based on index coding for broadcasting road map data from a centrally-managed roadside node to vehicles. REDS uses the data availability and demand knowledge for encoding and transmitting 3D point cloud road map data from different road segments. The data availability information helps prevent the transmission of duplicated road map data and provides the sets of side information in the index coding problem, while the data demand information further defines the message transmission priority based on the data demand of different road segments. Simulation results indicate that REDS reduces the average number of transmissions and transmitted point cloud data size by around 30% when the data availability probability is about 0.5 under random mobility in all simulated scenarios when compared to the traditional broadcasting approach.
Original languageEnglish
Title of host publication2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
PublisherIEEE
Volume2017-June
ISBN (Electronic)9781509059324
DOIs
Publication statusPublished - 14 Nov 2017
Event85th IEEE Vehicular Technology Conference, VTC Spring 2017 - Sydney, Australia
Duration: 4 Jun 20177 Jun 2017

Conference

Conference85th IEEE Vehicular Technology Conference, VTC Spring 2017
CountryAustralia
CitySydney
Period4/06/177/06/17

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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