Research on Vehicle Road Noise Prediction Based on AFW-LSTM
Abstract
:1. Introduction
1.1. Background
1.2. Research Status of Road Noise
1.3. Contributions and Structure
- In order to further improve the prediction accuracy of the model, an adaptive feature weight layer is added to the traditional neural network model, which effectively improves the accuracy of the model prediction results.
- In order to simultaneously predict the multi-frequency interior noise performance under different working conditions, LSTM, CNN-LSTM, AFW-CNN and AFW-LSTM methods are used to predict the multi-frequency interior noise.
- By analyzing the structure with large noise contribution in the road noise transmission path, the structure with significant influence is defined, the components and quantitative indicators of each level are determined, and the road noise decomposition framework is established.
2. Research Methods
2.1. LSTM Neural Network
2.2. AFW-LSTM Proposed
3. Data Collection and Processing
3.1. Analysis of Key Influencing Factors of Road Noise
3.2. Data Collection
3.3. Data Augmentation
4. Road Noise Prediction Model
4.1. Construction of AFW-LSTM Road Noise Model
4.2. AFW-LSTM Road Noise Model Prediction and Results Comparison
4.3. Verification of Prediction Results of AFW-LSTM Road Noise Model
5. Conclusions
- Based on the theory of road noise transfer path, the factors and paths that have significant influence on road noise such as steering knuckle, suspension and shock absorber are analyzed, and a two-level decomposition framework of road noise with key influencing factors as input and driver’s right ear noise as output is established. Based on the hierarchical structure of road noise, the road test is carried out, and 16 sets of complete road noise sample data are collected. The data set is enhanced by the CutMix method, where = 0.5, = 1.0, and 102 sets of sample data are generated.
- Based on the road noise hierarchical decomposition architecture, a data-driven model is built for the proposed AFW-LSTM method to reveal the quantitative relationship of the associated units in the hierarchical system. Data preprocessing involves data normalization, data set division, etc. The model training obtains the implicit mapping relationship between the levels of data and can obtain the data law of the frequency point analysis. At the same time, the prediction results of the data-driven model built by the four methods of LSTM, CNN-LSTM, AFW-CNN and AFW-LSTM are compared. The analysis results show that RMSE = 1.74 (dB), MAE = 2.6 (dB), = 0.924. Under the background of road noise, the prediction result of LSTM model is better than that of CNN prediction model, and the accuracy of LSTM model with AFW layer is better than that of single neural network model.
- The proposed method is tested by the sample data of the verification set obtained from the specific vehicle model. The results show that the predicted value is in good agreement with the true value, and the accuracy and robustness of the AFW-LSTM prediction model show obvious advantages. The RMSE = 1.84 (dB), MAE = 2.66 (dB), = 0.912 and other indicators obtained by the AFW-LSTM model are better than the models built by LSTM, CNN-LSTM, and AFW-CNN methods. Adaptive feature weight is conducive to the model to learn more important content, which can improve the efficiency and accuracy of the model to process data.
- The road noise prediction model only analyzes the influence of the suspension part on the driver’s right ear noise. The body part is still an important factor affecting the interior noise. In future research, the body parameters will be introduced to analyze the road noise problem. In addition, although the adaptive weight layer can enhance the network’s emphasis on key features under the premise of controllable actual engineering data and training, its comparative study with the current mainstream attention mechanism still needs to be further improved and expanded in the future.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Index | LSTM | CNN-LSTM | AFW-CNN | AFW-LSTM |
---|---|---|---|---|
RMSE (dB) | 1.91 | 1.90 | 1.76 | 1.74 |
MAE (dB) | 2.69 | 2.72 | 2.61 | 2.60 |
0.901 | 0.890 | 0.913 | 0.924 |
Index | LSTM | CNN-LSTM | AFW-CNN | AFW-LSTM |
---|---|---|---|---|
RMSE (dB) | 1.85 | 1.94 | 2.00 | 1.84 |
MAE (dB) | 2.67 | 2.73 | 2.77 | 2.66 |
0.891 | 0.871 | 0.908 | 0.912 |
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Ma, Y.; Dai, R.; Liu, T.; Liu, J.; Yang, S.; Wang, J. Research on Vehicle Road Noise Prediction Based on AFW-LSTM. Machines 2025, 13, 425. https://doi.org/10.3390/machines13050425
Ma Y, Dai R, Liu T, Liu J, Yang S, Wang J. Research on Vehicle Road Noise Prediction Based on AFW-LSTM. Machines. 2025; 13(5):425. https://doi.org/10.3390/machines13050425
Chicago/Turabian StyleMa, Yan, Ruxue Dai, Tao Liu, Jian Liu, Shukai Yang, and Jingjing Wang. 2025. "Research on Vehicle Road Noise Prediction Based on AFW-LSTM" Machines 13, no. 5: 425. https://doi.org/10.3390/machines13050425
APA StyleMa, Y., Dai, R., Liu, T., Liu, J., Yang, S., & Wang, J. (2025). Research on Vehicle Road Noise Prediction Based on AFW-LSTM. Machines, 13(5), 425. https://doi.org/10.3390/machines13050425