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 | |
| 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 |
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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
Cite this
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
}
TY - GEN
T1 - Road-illuminance level inference across road networks based on Bayesian analysis
AU - Bao, Siya
AU - Yanagisawa, Masao
AU - Togawa, Nozomu
PY - 2018/3/26
Y1 - 2018/3/26
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.
KW - landmark
KW - naive Bayesian analysis
KW - road light
KW - road-illuminance level
KW - safety
UR - http://www.scopus.com/inward/record.url?scp=85048842571&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048842571&partnerID=8YFLogxK
U2 - 10.1109/ICCE.2018.8326207
DO - 10.1109/ICCE.2018.8326207
M3 - Conference contribution
VL - 2018-January
SP - 1
EP - 6
BT - 2018 IEEE International Conference on Consumer Electronics, ICCE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -