## 5 Experimental Results  
### Road Surface Grade Identification Results  
The performance of the LSTM neural network classifier was evaluated and trained using Matlab software under different speed conditions. The classification accuracies of the \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Road Level** & \(\mathbf{w_{1}}\) & \(\mathbf{w_{2}}\) & \(\mathbf{w_{3}}\) \\ \hline
1 & B & 0.6 & 0.2 & 0.2 \\
2 & D & 0.5 & 0.2 & 0.3 \\
3 & F & 0.35 & 0.3 & 0.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Road surface optimization’s weight coefficients. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Initial Value** & **Lower Limit** & **Upper Limit** \\ \hline \(c_{1}\) & 1 & 0.5 & 1.4 \\ \(c_{2}\) & 10 & 5 & 15 \\ \(c_{3}\) & 40 & 20 & 60 \\ \(K_{bs}\) & 100 & 50 & 150 \\ \(K_{bc}\) & 100 & 50 & 150 \\ \(K_{le}\) & 0.1 & 0.05 & 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Initial values and search range of the parameters to be optimized. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Class B** & **Class D** & **Class F** \\ \hline \(c_{1}\) & 0.8746 & 0.9269 & 1.2657 \\ \(c_{2}\) & 9.8945 & 14.6889 & 6.5005 \\ \(c_{3}\) & 59.9877 & 59.9945 & 59.9736 \\ \(K_{bs}\) & 67.9604 & 74.3670 & 88.0823 \\ \(K_{bsc}\) & 109.0741 & 105.1122 & 112.6086 \\ \(K_{le}\) & 0.1017 & 0.1280 & 0.0661 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Optimized control parameters for the different road surface grades. training and testing sets at various speeds are shown in Table 9. From the experimental results, it can be observed that the LSTM classifier demonstrates high classification accuracy rates and stability at various speeds, with an accuracy rate exceeding 92%. This paper conducted an experimental study based on a real vehicle's road surface data collection to assess the efficacy of the suggested classification model. The experimental vehicle was operated under various typical road conditions. A laser displacement sensor was used to capture the road elevation data, while accelerometers recorded the vehicle's vibrational responses. The road surface types selected for the experiment included a mining truck road surface and a cement road surface (see Figure 8). The experimental vehicle traveled at a constant speed of 10 km/h on selected road segments.The main experimental equipment included an auxiliary transport vehicle (manufacturer: Inner Mongolia Bixuan Machinery Manufacturing Co., Ltd., Ordos, China), two sets of HWT605 accelerometers (manufacturer: WitMotion Shenzhen Co., Ltd., Shenzhen, China), one BGL-235 NZ laser displacement sensor (manufacturer: Boyi Jingke Technology Co., Ltd., Shenzhen, China), and a computer for data storage and processing. One accelerometer was mounted at the vehicle's center of gravity, and the other was fixed on the suspension arm of the left front wheel. The laser displacement sensor was installed on the bottom of the front driver's cabin. Figure 9 shows the installation positions. This study constructed a road surface grade evaluation system. Using single-wheel rut road spectrum measurement technology, the test road sections were systematically analyzed. The displacement data collected for the two road surfaces were adjusted (such as subtracting the initial value, taking the inverse, and unit conversion), resulting in the road elevation information for both experimental scenes, as shown in Figure 10. The raw data were first converted to the correct format, and outliers were removed. Then, polynomial fitting was used to remove the trend term and ensure the data stability. FFT was employed to calculate the PSD, which allowed the analysis of the vibration signal's energy distribution. Figure 8: Experimental scenes: (**a**) mining truck road surface; (**b**) cement road.