# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
## 4 Results  
### Risk and Complexity of Testing Scenario Assessment Based on SF-ANP  
Fuzzy-ANP is a good quantitative evaluation method by virtue of its negative impact, suppressing subjective human thinking and hesitant decisions. Since there is no predefined set value and no good objective calculation method for evaluating scenarios with autonomous vehicles, the use of experienced researchers' judgment methods is an effective solution. In order to realize our comprehensive evaluation of accident scenarios in autonomous vehicles testing, using the hierarchical structure and Fuzzy-ANP established above, we chose the two more important indicators of risk and complexity in scenario testing as the evaluation objectives. At the same time, the risk and complexity of the test scenarios are affected by the variability of each scenario element. Therefore, we adopted different SF-ANP structures to determine the relationship between sub-elements and elements. In this study, the control layer comprised complexity and risk, according to expert evaluations, to determine whether the elements affect each other. Then, the SF-ANP structure of the testing scenario was constructed, as shown in Figures 1 and 2. \(A\to B\) means A is affected by B and \(A\leftrightarrow B\) means that A and B influence each other, while circular arcs denote interdependencies within the same element. We generated two types of scoring table by combining and averaging the scores from seven experts. The seven experts include three engineers and four researchers working in the field of road traffic and traffic safety. The weights were set according to the number of years of work and research experience and the level of education. Each scoring table includes a pairwise comparison matrix for elements based on five distinct criteria, as well as a comparison of the importance of sub-elements within each element, where an arbitrary sub-element is used as a sub-criterion. In total, there are 92 comparison matrices. For illustration purposes, Table 4 shows the relationships among the elements when scenario complexity is the objective, with the ego vehicle serving as the criterion. Table 5 illustrates the level of influence between the elements and the ego vehicle, with scenario risk level as the main goal. Tables 6 and 7 present the pairwise comparison matrices for sub-elements within the road, where the behavior of the ego vehicle is used as a sub-criterion. Figure 1: ANP construct (scenario complexity). Figure 2: ANP construct (scenario risk). Prior to normalizing the matrix, the rationality of the scoring table needed to be confirmed. Super Decision software(v2.10) was created by [PERSON] and his wife [PERSON] in Pittsburgh, USA.And it was employed to conduct consistency testing by inputting the SI of the scoring table. The analysis revealed that all the obtained comparison matrices had values below 0.1, indicating high consistency. Consequently, it could be concluded that the scoring tables met the fundamental requirements. The \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**C11 Ego Vehicle** & **C21 Road** & **C22 Road Surface** & **C23 Road Slope** & **C24 The Number** & **C25 Road** \\
**Behavior** & **Segment Type** & **Conditions** & **C26 Road Slope** & **of Lanes** & **Leveling** \\ \hline C21 Road & [0.5, 0.4, 0.4] 1 & [0.7, 0.3, 0.2] 5 & [0.8, 0.2, 0.1] 7 & [0.6, 0.4, 0.3] 3 & [0.7, 0.3, 0.2] 5 \\ Segment Type & [0.3, 0.7, 0.2] 1/5 & [0.5, 0.4, 0.4] 1 & [0.6, 0.4, 0.3] 3 & [0.4, 0.6, 0.3] 1/3 & [0.5, 0.4, 0.4] 1 \\ C22 Road Surface & [0.3, 0.7, 0.2] 1/5 & [0.5, 0.4, 0.4] 1 & [0.6, 0.4, 0.3] 3 & [0.3, 0.7, 0.2] 1/5 & [0.4, 0.6, 0.3] 1/3 \\ Conditions & [0.4, 0.6, 0.3] 1/3 & [0.6, 0.4, 0.3] 3 & [0.7, 0.3, 0.2] 5 & [0.5, 0.4, 0.4] 1 & [0.7, 0.3, 0.2] 5 \\ C25 Road Leveling & [0.3, 0.7, 0.2] 1/5 & [0.5, 0.4, 0.4] 1 & [0.6, 0.4, 0.3] 3 & [0.3, 0.7, 0.2] 1/5 & [0.5, 0.4, 0.4] 1 \\ \hline CR & & & 0.0467 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Scenario complexity comparison matrix (ego vehicle behavior–road).