## 5 Validation and Results
### Method Validation
#### 5.3.3 Verification Results  
Table 2 presents the results of the correlation analysis between complexity and risk, as well as between complexity and efficiency. The correlation coefficient between complexity and risk is 0.736, indicating a strong positive correlation. Additionally, the \(p\)-value significance is 0.000, confirming that this correlation is statistically significant. Simultaneously, the correlation coefficient between complexity and efficiency is \(-0.912\), indicating a strong negative correlation. The \(p\)-value significance of 0.000 further confirms that this negative correlation is statistically significant. These results clearly demonstrate that as the scenario complexity increases, the risk associated with autonomous driving rises and efficiency declines. This indicates a high correlation between the outcomes of the scenario complexity assessment and the autonomous driving performance evaluation, thereby verifying the effectiveness of our evaluation method. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Mean** & **Std** & **Min** & **Median** & **Max** \\ \hline Complexity & 0.285 & 0.153 & 0.020 & 0.258 & 0.550 \\ Risk & 0.461 & 0.057 & 0.285 & 0.452 & 0.675 \\ Efficiency & 1.88 & 0.231 & 1.160 & 1.931 & 2.355 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistical results of the validation set. The regression analysis results presented in Table 3 confirm a significant positive correlation between scenario complexity and the risk of automated driving. The regression coefficient is 0.201, indicating that for every unit increase in complexity, the risk increases by 0.201 units. The goodness-of-fit value, 0.543, suggests that the model accounts for 54.3% of the variance in risk. The results of the regression analysis in Figure 11 further elucidate the reasons behind the low values observed. While the overall trend indicates that risk increases with rising complexity, there is a noticeable point at which complexity reaches a certain threshold and then abruptly decreases. This sudden change leads to more divergent results, consequently affecting the model's goodness of fit. This phenomenon occurs when the system, confronted with complex scenarios, adopts a conservative strategy, which consequently leads to a low-risk assessment. Figure 12 displays key frame snapshots from two distinct test cases. In Figure 12a, under conditions of low complexity, Apollo adopts an aggressive strategy to secure a lane, demonstrating its capability to effectively manage simple vehicle interactions and enhance traffic efficiency through competitive maneuvers. Conversely, Figure 12b depicts a scenario where Apollo, facing high complexity, opts to hesitate, allowing other vehicles to pass before proceeding, thus prioritizing safety. The regression analysis results presented in Table 4 further substantiate a significant negative correlation between scenario complexity and the efficiency of autonomous driving. The regression coefficient is \(-1.255\), indicating that for every unit increase in complexity, efficiency is expected to decrease by 1.255 units. The goodness of fit value of 0.832 demonstrates that the complexity provides a robust explanation for variations in efficiency. Figure 13 illustrates the regression analysis results concerning the relationship \begin{table}
\begin{tabular}{c c c} \hline \hline
**Risk** & **Efficiency** \\ \hline r & 0.736 & \(-0.912\) \\ p & 0.000 & 0.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Correlation between complexity and risk as well as efficiency. Figure 11: Regression analysis results for complexity and risk. between scenario complexity and autonomous driving efficiency. The graph clearly shows a significant decrease in efficiency as complexity increases. This trend is consistent with the statistical data from the regression analysis, further confirming the strong negative correlation between scenario complexity and autonomous driving efficiency.