# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
## 4 Results
### Risk and Complexity of Testing Scenario Assessment Based on SF-ANP
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**C1 Ego Vehicle** & **C21 Road** & **C22 Road Surface** & **C23 Road Slope** & **C24 The Number** & **C25 Road** \\
**Behavior** & **Segment Type** & **Conditions** & **C23 Road Slope** & **of Lanes** & **Leveling** \\ \hline C21 Road & [0.5, 0.4, 0.4] 1 & [0.4, 0.6, 0.3] 1/3 & [0.7, 0.3, 0.2] 5 & [0.7, 0.3, 0.2] 5 & [0.7, 0.3, 0.2] 5 \\ C22 Road Surface & [0.6, 0.4, 0.3] 3 & [0.5, 0.4, 0.4] 1  
individual sub-criteria comparison matrix was made unambiguous by passing it through SWGM, as follows:  
\[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]  
\[s_{lrisk}=[16.1295,\ 19.8542,\ 10.2979,\ 7.7028,\ 10.2979]\]  
where \(s_{lcom}\) and \(s_{lrisk}\) were the calculated values, obtained after the defuzzification of five sub-elements regarding scenario complexity and scenario risk, respectively. The local weights were calculated using the following formulas:  
\[\bar{w}_{j}=\frac{DSF\big{(}s_{j}\big{)}}{\sum_{j=1}^{n}DSF\big{(}s_{j}\big{)}} \tag{30}\]  
\[L_{\text{com}}=[0.2767,0.1799,0.1369,0.2362,0.1702]^{\text{T}}\]  
\[L_{\text{risk}}=[0.2509,0.3089,0.1602\,0.1198,0.1602\ ]^{\text{T}}\]  
The individual criteria comparison matrix was made unambiguous by passing it through SWGM, as follows:  
\[s_{acom}=[5.5536,\ 15.4053,\ 14.0927,\ 17.2751,\ 12.4745]\]  
\[s_{arisk}=[1.5331,\ 15.0660,\ 13.2325,\ 15.0660,\ 17.2751]\]  
where \(s_{acom}\) and \(s_{arisk}\) represent the calculated values obtained after the defuzzification of five elements regarding scenario complexity and scenario risk, respectively. The weights among the elements were obtained based on the normalization method, as follows:  
\[A_{\text{com}}=[0.0857,0.2377,0.2175,0.2666,0.1925]^{\text{T}}\]  
\[A_{\text{risk}}=[0.0247,0.2423,0.2128,0.2423,0.2779]^{\text{T}}\]  
The weighted matrix was calculated with the unweighted supermatrix to obtain the weighted supermatrix; meanwhile, the required element weight values were obtained for the testing scenarios under different objectives, which were computed as shown in (31) and (32):  
\[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]0  
\[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]1  
\[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]2  
\[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]3  
Using the same steps, all the elements, along with the matrix of sub-elements, were calculated by SWGM, defuzzification, weight calculation, and normalized weighting to obtain the global weight matrix of the sub-elements. Finally, the power limit of the matrix was calculated using MATLAB and the version is R2020b. to obtain the final weight of all sub-elements in terms of risk and complexity, as shown in \[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]4  
The global weight is the relative contribution of the scenario sub-elements to scenario risk and scenario complexity and measures the importance weights of each scenario component to different objectives. The evaluation matrices under different objectives were constructed separately:  
\[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]5  
\[s_{lcom}=[18.1755,\ 11.8179,\ 8.9890,\ 15.5154,\ 11.1818]\]6  
where \(V_{1}\) is complexity; \(V_{2}\) is risk. Five evaluation levels were used, as follows: \(v_{11}\)--very simple; \(v_{12}\)--simple; \(v_{13}\)--basically simple; \(v_{14}\)--complex; \(v_{15}\)--very complex; \(v_{21}\)--very safe; \(v_{22}\)--safe; \(v_{23}\)--basically safe; \(v_{24}\)--dangerous; and \(v_{25}\)--very dangerous. According to a large amount of traffic data, the parameter ranges corresponding to the different evaluation levels for each element parameter in terms of risk and complexity were determined, which provided reference standards for element evaluation, as shown in the Appendix A. In order to facilitate the accurate expression of comparative evaluation results, the different evaluation levels were quantified and characterized by a score matrix \(P\).