深層学習による路面画像を用いた路面入力騒音の予測  [in Japanese] Road Induced Noise Prediction using Deep Learning from Road Surface Images  [in Japanese]

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Author(s)

Abstract

アクティブ・セミアクティブ制御により路面入力騒音を低減するための参照信号として,自動運転に用いられる単眼カメラから得られる路面画像から路面入力騒音のピーク周波数を推定する.3種類の画像認識技術(HOG,CNN,autoencoder)を用いて路面画像から特徴量を抽出し機械学習により推定した.

The purpose of this research is to predict both the frequency and peak level of road induced noise from the signals of monocular camera adopted for automatic driving without newly adding sensors for utilizing the prediction result as a reference signal to reduce road induced noise by ANC. Using the monocular camera, both the frequency and peak level of road induced noise is predicted by machine learning from the road surface features which are extracted from road surface images by three image recognition techniques (HOG, CNN, autoencoder).

Journal

  • Transactions of Society of Automotive Engineers of Japan

    Transactions of Society of Automotive Engineers of Japan 50(5), 1421-1426, 2019

    Society of Automotive Engineers of Japan

Codes

  • NII Article ID (NAID)
    130007711621
  • NII NACSIS-CAT ID (NCID)
    AA1260263X
  • Text Lang
    JPN
  • ISSN
    0287-8321
  • NDL Article ID
    030058790
  • NDL Call No.
    YH247-1690
  • Data Source
    NDL  J-STAGE 
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