This content is not included in your SAE MOBILUS subscription, or you are not logged in.
Effects of the Feature Extraction from Road Surface Image for Road Induced Noise Prediction Using Artificial Intelligence
ISSN: 0148-7191, e-ISSN: 2688-3627
Published June 5, 2019 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Next generation vehicles driven by motor such as electric vehicles and fuel cell vehicles have no engine noise. Therefore the balance of interior noise is different from the vehicles driven by conventional combustion engine. In particular, road induced noise tends to be conspicuous in the low to middle vehicle speed range, therefore, technological development to reduce it is important task. The purpose of this research is to predict the road induced noise from the signals of sensors adopted for automatic driving for utilizing the prediction result as a reference signal to reduce road induced noise by active noise control (ANC). Using the monocular camera which is one of the simplest image sensors, the road induced noise is predicted from the road surface image ahead of the vehicle by machine learning. The effects to extract features (Histograms of Oriented Gradients (HOG) feature, autoencoder feature, Convolutional Neural Network (CNN) feature) from road surface images are evaluated by visualization result of t-SNE. From the features acquired by the above method, the frequency characteristics of the road induced noise are predicted using deep learning, which is generally considered to be high prediction accuracy as a machine learning method. For the eight kinds of road surface, we compared the road induced noise measured by the actual vehicle and predicted by using deep learning. The prediction result using extracted features of road surface image is more accurate than that using RGB value of road surface directly. This tendency accorded with a visualization result by t-distributed Stochastic Neighbor Embedding (t-SNE).
CitationNakamura, S., Komada, M., Matsumura, Y., Matsushita, K. et al., "Effects of the Feature Extraction from Road Surface Image for Road Induced Noise Prediction Using Artificial Intelligence," SAE Technical Paper 2019-01-1565, 2019, https://doi.org/10.4271/2019-01-1565.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
- Cao, Y., Wang, D., Zhao, T., Liu, X. et al. , “Electric Vehicle Interior Noise Contribution Analysis,” SAE Technical Paper 2016-01-1296, 2016, doi:10.4271/2016-01-1296.
- Shiozaki, H., Iwanaga, Y., Ito, H., and Takahashi, Y. , “Interior Noise Evaluation of Electric Vehicle: Noise Source Contribution Analysis,” SAE Technical Paper 2011-39-7229, 2011.
- Takuma, S. and Masaki, T. , “Active Suspension Control Considering Lateral Vehicle Dynamics due to Road Input at Different Vehicle Speed,” Transactions of the JSME (in Japanese) 78-786:446-461, 2012, doi:10.1299/kikaic.78.446.
- Zafeiropoulos, N., Ballatore, M., Moorhouse, A., and Mackay, A. , “Active Control of Structure-Borne Road Noise Based on the Separation of Front and Rear Structural Road Noise Related Dynamics,” SAE Int. J. Passeng. Cars - Mech. Syst. 8(3), 2015, doi:10.4271/2015-01-2222.
- Masashi, K., Yuichi, M., Ichiro, K., Eiji, N. et al. , “Real-Time Assignment Method of Resonance Frequency by Change of Coupling Stiffness for Improving Road Induced Noise,” ISMA2018 4283-4297, 2018.
- Deigmoeller, J., Einecke, N., Fuchs, O., and Janssen, H. , “Road Surface Scanning Using Stereo Cameras for Motorcycles,” SCITEPRESS, 2018, doi:10.5220/0006614805490554.
- Takayuki, O. , Deep Learning (Kodansha Ltd, 2015). ISBN:978-4-061-52902-1.
- Dalal, N. and Triggs, B. , “Histograms of Oriented Gradients for Human Detection,” Proc. Computer Vision and Pattern Recognition 1:886-893, 2005, doi:10.1109/CVPR.2005.177.
- van der Maaten, L. and Hinton, G. , “Visualizing High-Dimensional Data Using t-SNE,” Journal of Machine Learning Research 9:2579-2605, 2008.
- Zeiler, M.D. and Fergus, R. , “Visualizing and Understanding Convolutional Networks,” . In: Fleer D., Pajdla T., Schiele B., and Tuytelaars T., editors. Computer Vision-ECCV 2014. Lecture Notes in Computer Science. (Springer International Publishing, 2014), 818-833, doi:10.1007/978-3-319-10590-1_53.
- Mahendran, A. and Vedaldi, A. , “Understanding Deep Image Representations by Inverting Them,” in IEEE Conference on Computer Vision and Pattern Recognition, 5188-5196, 2015.
- Le, Q.V., Ranzato, M.A., Monga, R., Devin, M. et al. , “Building High-Level Features Using Large Scale Unsupervised Learning,” in International Conference on Machine Learning, 2012, doi:10.1109/ICASSP.2013.6639343.
- Kento, S., Keisuke, I., and Masashi, K. , “Variation Analysis for Engine Starting Vibration Based on Machine Learning,” Society of Automotive Engineers of Japan, Inc., Scientific Lecture Proceeding, No. 110-18, 2018.