## 5 Experimental Results
### Road Surface Grade Identification Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Speed (km/h)** & **Training Accuracy (\%)** & **Test Accuracy (\%)** \\ \hline
5 & 96.1772 & 96.0802 \\
10 & 96.1772 & 95.7407 \\
15 & 94.5767 & 94.2284 \\
20 & 93.1283 & 92.7623 \\
25 & 96.2632 & 96.1883 \\
30 & 94.5701 & 94.2438 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Classifier performance comparison at different speeds. The smoothed PSD curve was then fitted using the least-square method. The original PSD, smoothed PSD, and fitted curves were plotted in the same figure for visual comparison and analysis, providing data support for extracting the roads' spectral features. In the specified frequency range, the average PSD can be calculated using the following formula:  
\[G(i)=\frac{[(n_{L}+0.5)\cdot B_{e}-n_{l}(i)]G(n_{L})}{n_{h}(i)-n_{l}(i)}+\frac{ \sum_{j=n_{L}+1}^{n_{H}-1}G(j)\cdot B_{e}}{n_{h}(i)-n_{l}(i)}+\frac{[n_{h}(i)-( n_{H}+0.5)\cdot B_{e}]G(n_{H})}{n_{h}(i)-n_{l}(i)} \tag{21}\]  
where \(n_{H}=INT(\frac{n_{h}(i)}{B_{e}}+0.5)\), \(n_{L}=INT\Big{(}\frac{n_{l}(i)}{B_{e}}+0.5\Big{)}\), \(G_{s}(i)\) represents the power spectral density under the smoothed bandwidth conditions, and \(n_{l}\) and \(n_{h}\) represent the lower and upper cutoff frequencies for the frequency spectrum analysis. The smoothed PSD function was fitted using the least-square method. The data-fitting range was between spatial frequencies of 0.011 m\({}^{-1}\) and 2.83 m\({}^{-1}\), and the fitted curves are shown in Figure 11. A comparison with standards suggests that the cement road surface corresponds to a B-grade road surface, while the mining truck road surface corresponds to a D-grade road surface. Figure 10: Road elevation information: (**a**) mining truck road; (**b**) cement road. Figure 9: Experimental equipment and installation positions. Vibration sensors were used to capture the vertical acceleration signals of the vehicle on different road surfaces (Figure 12). Because the vehicle traveled different distances on each road surface, the acceleration signals were processed using a unified method to ensure comparability. Twelve key feature indicators were extracted. In constructing the dataset, 10 continuous 10 s samples were selected from each road's signals, and 1 s sub-samples were used for testing, resulting in 100 groups for classification. The pre-processed testing data were input into the trained algorithmic model, and the road surface grade identification results from the model were compared with the actual road surface conditions to verify the model's accuracy in real-world scenarios. Based on the experimental data shown in Figure 13, under the 10 km/h driving speed condition, the road surface grade identification system achieved a classification accuracy of 82% for the mining truck road surface, with approximately 82% of the samples correctly classified as D-grade. The remaining samples were misclassified as F-grade, C-grade, and B-grade at rates of 5%, 12%, and 1%, respectively. For the cement road surface, 85% of the samples were correctly classified as B-grade, while about 11% and 4% were misclassified as C-grade and A-grade, respectively. The final identification errors were all controlled within 20%, ensuring an accuracy rate of over 80%. Figure 11: Road elevation-fitting curves: (**a**) mining truck road; (**b**) cement road. Figure 12: Vibration acceleration responses: (**a**) mining truck road; (**b**) cement road.