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초록
Tire-pavement Interaction Noise (TPIN) is noise caused by interactions between rolling tires and road surfaces. After measuring the TPIN using a microphone, transform TPIN to images using continuous wavelet transform (CWT). The transformed images are used to classify the driving road and tire using convolutional neural network (CNN). The Road and tire classification network using CNN is required for braking systems in autonomous vehicle. The CNN in this paper can classify snow road, asphalt road, and two types of tires with over 97% accuracy. © ICA 2022.All rights reserved
키워드
Autonomous vehicle; Continuous wavelet transform; Deep Learning; Noise Signal; Road and tire Interaction
- 제목
- Classification Of Road Pattern Based on Deep Learning of Big Acoustic Data
- 저자
- Lee, Sang Kwon; Yoo, Jinhwan; Lee, Chang Hun; Yoon, Youngsam; Lee, Jaehun; Yum, Kiho; Hwang, Seong-Uk
- 발행일
- 2022
- 유형
- Conference paper
- 저널명
- Proceedings of the International Congress on Acoustics