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

Siya Bao; Masao Yanagisawa; Nozomu Togawa

doi:10.1109/ICCE.2018.8326207

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

Siya Bao, Masao Yanagisawa, Nozomu Togawa

School of Fundamental Science and Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1-6
Number of pages	6
Volume	2018-January
ISBN (Electronic)	9781538630259
DOIs	https://doi.org/10.1109/ICCE.2018.8326207
Publication status	Published - 2018 Mar 26
Event	2018 IEEE International Conference on Consumer Electronics, ICCE 2018 - Las Vegas, United States Duration: 2018 Jan 12 → 2018 Jan 14

Other

Other	2018 IEEE International Conference on Consumer Electronics, ICCE 2018
Country	United States
City	Las Vegas
Period	18/1/12 → 18/1/14

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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10.1109/ICCE.2018.8326207

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Road-illuminance level inference across road networks based on Bayesian analysis. / Bao, Siya ; Yanagisawa, Masao ; Togawa, Nozomu.

2018 IEEE International Conference on Consumer Electronics, ICCE 2018. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2018 IEEE International Conference on Consumer Electronics, ICCE 2018, Las Vegas, United States, 18/1/12. https://doi.org/10.1109/ICCE.2018.8326207

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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.",

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N2 - 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.

AB - 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.

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

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Keywords

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