# Risk and Complexity Assessment of Autonomous Vehicle Testing Scenarios
## 2 Literature Review  
Researchers have been working to create challenging testing scenarios for AVs. [PERSON] et al. [12] highlighted the need to shift from focusing on individual scenarios to evaluating overall safety. They discussed two methods: one that covers a wide range of scenarios and another that focuses on extreme, rare cases. [PERSON] et al. [10] stressed the importance of defining what makes a scenario critical to build a comprehensive testing library. They proposed a framework that considers both the difficulty of maneuvers and how often they occur, allowing for diverse testing conditions and performance metrics. Comparingbroad coverage scenarios with extreme cases, [PERSON] et al. (2012) found that broader scenarios are more effective in testing AV safety, leading to a preference for this approach. The PEGASUS project introduced a five-layer model for testing scenarios ([PERSON] et al., 2013). Layer 1 covers road features like lane markings and geometry. Layer 2 includes traffic infrastructure such as traffic lights and barriers. Layer 3 integrates these with time, defining a standard day. Layer 4 focuses on traffic participants and their interactions, while Layer 5 considers environmental factors like weather and lighting. [PERSON] et al. (2019) refined this model, suggesting that Layer 2 should only include physical objects interacting with the road, considering traffic rules. Layer 4 was revised to detail specific maneuvers, and Layer 5 emphasized the environment's impact on these interactions. Recognizing the need for digital communication, [PERSON] et al. (2020) added a sixth layer for vehicle-to-everything (V2X) communication and digital maps. The characteristics of the different models are shown in Table 1.  
The previously discussed models do not fully address the state of the autonomous vehicles (AVs) themselves. It is important to consider how AVs interact with their traffic environments, accounting for the elements surrounding them. To address this gap, [PERSON] et al. (2017) expanded the six-layer model of testing scenarios to include the ego vehicle, which is the AV being tested. In contrast, the National Highway Traffic Safety Administration (NHTSA) proposed a four-element framework for scenario testing that focuses on the perspective of the ego vehicle within the AV testing framework ([PERSON] et al., 2018). This framework encompasses tactical maneuvers, which are control-related tasks, and the Operational Design Domain (ODD) elements, which define the specific conditions under which an AV system is designed to operate, including roadway type, speed limits, lighting, weather, and other constraints. It also includes Object and Event Detection and Response (OEDR), which involves the AV's ability to monitor its environment and respond appropriately to objects and events, as well as failure mode behaviors that consider how the AV handles incomplete or incorrect information. Furthermore, [PERSON] et al. (2017) combined static infrastructure information with dynamic environmental data to create a graph-based representation of the local environment, incorporating details about the AV's own state and enriching the scenario with task-specific information. This approach includes various aspects of the state space, such as the vehicle's position, speed, and steering; topological information like lane markings, traffic rules, and signals; and the presence of other road participants, including vehicles and obstacles, within the AV's field of view ([PERSON] et al., 2018; [PERSON] et al., 2019; [PERSON] et al., 2020). The selection of scenario elements for evaluation in scenario tests is influenced by various factors, including scenario complexity. [PERSON] et al. (2020) divided scenario elements into three categories: static, dynamic, and environmental. Static elements cover physical structures like road signs and signals, while environmental elements include conditions such as rain, snow, fog, and dust. [PERSON] et al.