# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
## 2 Literature Review
(2020) argued that scenario complexity consists of two main aspects: road semantic complexity and traffic element complexity. Road semantic complexity considers factors like road type (urban, highway, rural), scenario type (e.g., intersections, tunnels), and challenging conditions (e.g., curves, night driving). Traffic element complexity is measured using a matrix that tracks the distance and angle of the ego vehicle relative to other traffic elements ([PERSON] et al., 2018). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Models** & **Contributions** & **Gaps** \\ \hline Five-layer ([PERSON] et al., 2013) & Proposing a generalized scenario hierarchy & Excludes the state of the ego vehicle; too little description of interactions in traffic \\ \hline Refined Five-layer ([PERSON] et al., 2019) & Elements were modified based on the original structure & Excludes the state of the ego vehicle, with some scenario-level interactions \\ \hline Six-layer ([PERSON] et al., 2018) & With a new layer of digital communication & Excludes the state of the ego vehicle; content features interactions \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristics of the models. The potential field method is often used to assess the complexity of traffic scenes by analyzing scenario elements. [PERSON] et al. (2015) used this method combined with hierarchical analysis to quantify the complexity of traffic environments, but they did not account for road conditions. Similarly, [PERSON] et al. (2016) applied the potential field method with a hierarchical approach to evaluate road models within a driver-vehicle-road framework, but they also overlooked critical elements like weather conditions. [PERSON] et al. (2017) introduced a method to quantify scenario complexity by using a feasible trajectory that incorporates current vehicle dynamics constraints and entropy as a measure. This approach adjusts the travel domain based on the presence of other traffic participants, defining scenario complexity as the total entropy of all vehicles in the scene. However, the potential field method, when viewed from the perspective of the ego vehicle, primarily focuses on the vehicle's dynamic characteristics and fails to adequately account for interactions between traffic elements, leading to an incomplete quantification of testing scenarios. Multi-Criteria Decision Making (MCDM) is a useful method for making decisions that involve multiple criteria and trade-offs ([PERSON] et al., 2018, 2019). The assessment of complexity and risk in testing scenarios for autonomous vehicles can be seen as a multi-criteria decision problem. The Analytical Network Process (ANP) helps manage interdependencies between different criteria and elements, such as those in traffic models ([PERSON] et al., 2018). By incorporating fuzzy sets into ANP, the subjectivity in evaluations can be minimized. Fuzzy sets extend Boolean logic to handle uncertainties in real-world decisions ([PERSON] et al., 2018). Several types of fuzzy sets exist, including type 2, intuitionistic, Pythagorean, neutrosophic, and hesitant fuzzy sets, with each offering different ways to deal with uncertainty and hesitation in decision-making ([PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018; [PERSON] et al., 2018). Spherical Fuzzy sets (SFs) account for hesitation by setting a limit on the sum of squared membership, non-membership, and hesitation values ([PERSON] et al., 2018). [PERSON] et al. (2018) used SFs, along with DEMATEL, ANP, and VIKOR, to assess risks in automated driving systems, while [PERSON] et al. (2018) applied Fuzzy-ANP to evaluate a water supply network. These studies show that Fuzzy-ANP is effective in evaluating the relationships between elements in testing scenarios. Differing from the traditional five-layer model, we considered adding vehicle state information and various pieces of interaction information based on multiple modified models to realize the construction of scenario elements serving the simulation test.