## 1 Introduction
### Prediction Experiment of Optimal Configuration of Plant Landscape
#### 2.3.5 Data Processing  
In previous studies, the standard deviation of speed and lateral displacement were generally used as indicators to measure the driving stability of cars [25; 26; 27]. Therefore, in order to assess the influence of different levels of vegetation on driver behavior, the standard deviation of the mean speed and lateral displacement of the drivers in each scene is chosen as a measure of driving stability. The smaller the standard deviation, the more stable the car is. The simulated driving test generates the driving trajectory of each subject. The trajectory is composed of nearly 10,000 test points, and the state of the vehicle reaching the test point is detected and recorded in real time. Figure 4 shows the UC-win/Road software simulation driving the data output interface and data processing. In this study, the speed (v) and lateral displacement (s) of the vehicle are selected as valid data to calculate the average speed and lateral displacement of all experimenters. Finally, the standard deviation of the average speed and the standard deviation of the average lateral displacement of the experimenters in each scene are calculated. The calculation formulas are as follows (Formulas (2)-(5)):  
\[\overline{V}=\frac{1}{n}.\sum^{n}\]  
Figure 7: The visual effect of a random six-module scene. 3.112. Since the model's F value is much greater than the critical value of the F test, it is considered that the influence of each factor on the response variable is significant. A larger F value indicates a more significant influence of the corresponding factor on the response variable. In the analysis of variance, a _p_-value less than 0.05 indicates a meaningful model. The influence of the four factors on the response variable (speed standard deviation) is significant. In Table 5, a _p_-value less than 0.0001 indicates that the influence of the four factors on the speed standard deviation is highly significant. \"Lack of Fit\" in the analysis of variance table refers to the situation where the model does not fit the data well. In Table 4, the \"Lack of Fit\" is not significant, indicating that the established model fits the data well. Based on these three pieces of data, it is evident that the BBD response surface method for predicting the optimal configuration of plant landscapes is valid. The predicted results for the optimal configuration of plant landscapes are reliable and can be applied to practical situations. Table 7 presents the analysis of variance for the standard deviation of lateral displacement. The results indicate that the model's F value is 4.5. Comparing this to the critical value of 3.112 from the F test, the model's F value is significantly greater, suggesting that the influence of each factor on the response variable is significant. When compared to the analysis of the variance table for the standard deviation of speed, it is evident that the four factors of plant landscapes have a greater impact on speed than on lateral displacement. In Table 7, a _p_-value less than 0.0001 also indicates a significant impact of the four factors on the standard deviation of lateral displacement. The \"Lack of Fit\" in the table is not significant, indicating that the lateral displacement model fits the data better, demonstrating the reasonableness of the data. Therefore, using the standard deviation of lateral displacement as a measure of vehicle stability is highly reasonable. Predicting the optimal configuration of plant landscape factors through the standard deviation of lateral displacement is reliable.