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

    研究成果: Conference contribution

    抄録

    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.

    元の言語English
    ホスト出版物のタイトル2018 IEEE International Conference on Consumer Electronics, ICCE 2018
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ1-6
    ページ数6
    2018-January
    ISBN(電子版)9781538630259
    DOI
    出版物ステータスPublished - 2018 3 26
    イベント2018 IEEE International Conference on Consumer Electronics, ICCE 2018 - Las Vegas, United States
    継続期間: 2018 1 122018 1 14

    Other

    Other2018 IEEE International Conference on Consumer Electronics, ICCE 2018
    United States
    Las Vegas
    期間18/1/1218/1/14

    Fingerprint

    Learning systems

    ASJC Scopus subject areas

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

    これを引用

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

    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. 巻 2018-January Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6.

    研究成果: Conference contribution

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