Road-illuminance level inference across road networks based on Bayesian analysis

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

Abstract

This paper proposes a road-illuminance level inference method based on the naive Bayesian analysis. We investigate quantities and types of road lights and landmarks with a large set of roads in real environments and reorganize them into two safety classes, safe or unsafe, with seven road attributes. Then we carry out data learning using three types of datasets according to different groups of the road attributes. Experimental results demonstrate that the proposed method successfully classifies a set of roads with seven attributes into safe ones and unsafe ones with the accuracy of more than 85%, which is superior to other machine-learning based methods and a manual-based method.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Consumer Electronics, ICCE 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-6
Number of pages6
Volume2018-January
ISBN (Electronic)9781538630259
DOIs
Publication statusPublished - 2018 Mar 26
Event2018 IEEE International Conference on Consumer Electronics, ICCE 2018 - Las Vegas, United States
Duration: 2018 Jan 122018 Jan 14

Other

Other2018 IEEE International Conference on Consumer Electronics, ICCE 2018
CountryUnited States
CityLas Vegas
Period18/1/1218/1/14

Keywords

  • landmark
  • naive Bayesian analysis
  • road light
  • road-illuminance level
  • safety

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Electrical and Electronic Engineering
  • Media Technology

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  • Cite this

    Bao, S., Yanagisawa, M., & Togawa, N. (2018). Road-illuminance level inference across road networks based on Bayesian analysis. In 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 (Vol. 2018-January, pp. 1-6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE.2018.8326207