# Rainy Environment Identification Based on Channel State Information for Autonomous Vehicles
## 5 Simulation Analysis
### Model Evaluation
This approach ensured consistency in experimental setup across different models while also allowing each model to fully utilize its strengths in handling specific classification tasks. Through this comparison, we can more comprehensively assess the performance of the proposed CNN model in vehicular environment identification, as well as its relative advantages compared to other advanced models. Since the aforementioned classic models are designed to receive two-dimensional (2D) inputs to accommodate shape sizes, a 2D channel matrix is considered in the input features rather than a one-dimensional (1D) channel vector. Therefore, the dataset is rearranged from 1D to 2D, with the formula as follows:  
\[ \begin{array}{c}H_{\text{2D}}=\text{Diag}(H_{\text{1D}})\\ \left[\text{$\imath$=$}\right]\quad\left[\text{$\imath$=$}\right]\quad\left[ \text{$\imath$=$}\right]\quad\end{array}  \tag{18}\]  
where Diag is a diagonal matrix and \(H_{\text{2D}}\) and \(H_{\text{1D}}\) represent the channel matrix and the corresponding channel vector, respectively. The coefficients of these matrices and vectors are composed of CSI values, with n being 128, reflecting the number of CSI values estimated for each packet. Table 4 provides a comparative analysis of the CNN model proposed in this paper with existing classic models in terms of test accuracy and the time required for environmental identification. The data from the table show that, compared to other models, our model has a significant advantage in prediction time. Specifically, the prediction times for ResNet50, Xception, and InceptionV3 are about 700 \(\upmu\)s, while DenseNet201 and MobileNetV2 have prediction times of 1417 \(\upmu\)s and 349 \(\upmu\)s, respectively. In contrast, the prediction time for our study's model is only 43.82 \(\upmu\)s, which is significantly lower than the aforementioned models. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Accuracy (\%)** & **Prediction Time (\(\upmu\)s)** \\ \hline Our Model & 95.7 & 43.82 \\ ResNet50 [30] & 86.7 & 762 \\ Xception [31] & 90.5 & 709 \\ InceptionV3 [32] & 88.3 & 698 \\ DenseNet201 [33] & 91.2 & 1417 \\ MobileNetV2 [34] & 76.3 & 349 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between our model and state-of-the-art alternatives. Figure 10: The confusion matrix of the SVM model. In terms of overall test accuracy, as shown in Table 4, the proposed model achieved an accuracy rate of 95.7%. This rate is higher than the test accuracies achieved by ResNet50, Xception, InceptionV3, DenseNet201, and MobileNetV2. In summary, to validate the effectiveness of the proposed CNN method, we constructed a dataset that includes five types of rainy vehicular environments. Through extensive testing at different Signal-to-Noise Ratio (SNR) levels, our CNN model demonstrated outstanding performance in recognizing different rainy environments, with an accuracy rate of up to 95.7%. The proposed CNN model not only shows significant advantages in computational efficiency and identification accuracy but also proves the potential of our method in practical applications, providing a safer and more reliable driving parameter adjustment strategy for intelligent vehicles in rainy conditions.