@inproceedings{87af06c5f85345f5895f3724da5bd90c,
title = "Road-illuminance level inference across road networks based on Bayesian analysis",
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.",
keywords = "landmark, naive Bayesian analysis, road light, road-illuminance level, safety",
author = "Siya Bao and Masao Yanagisawa and Nozomu Togawa",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Consumer Electronics, ICCE 2018 ; Conference date: 12-01-2018 Through 14-01-2018",
year = "2018",
month = mar,
day = "26",
doi = "10.1109/ICCE.2018.8326207",
language = "English",
series = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1--6",
editor = "Mohanty, {Saraju P.} and Peter Corcoran and Hai Li and Anirban Sengupta and Jong-Hyouk Lee",
booktitle = "2018 IEEE International Conference on Consumer Electronics, ICCE 2018",
}