# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 3 Scenario Generation Methods
### Non-Adaptive Test Scenario Generation Methods
#### 3.1.1 Knowledge-Based Generation Method  
The first widely recognized method for generating test scenarios was knowledge-based generation, which involves utilizing existing knowledge and data to create scenarios. The knowledge and data can originate from various sources, such as traffic regulations, road conditions, vehicle behaviors, environmental factors, accident records, and other related knowledge domains.  
The study by [16] used big data technology [17] to generate functional scenarios, using human natural language descriptions to define the generated scenarios. They performed text weight analysis on crash descriptions from accident records to extract high-frequency keywords related to crash locations, traffic operations, maneuvers, and triggering events,  
Figure 4: Taxonomy of scenario generation methods.  
which were then combined to construct new test scenarios. However, since [16] only generated functional scenarios, non-player characters (NPCs) in the scenario such as background vehicles (BVs), did not respond to the AV's behavior. Additionally, the scope of the generated scenarios was narrow, concentrating on crashes at main roads regulated by traffic lights, specifically at intersections and their approach links. Furthermore, all generated scenarios were crash-related, significantly limiting their utility for comprehensive and effective testing of autonomous vehicle performance.  
In [18], a Markov decision process (MDP) [19] framework combined with deep reinforcement learning [20] was employed to generate test scenarios. Static scenario elements that included parameters such as the number of lanes, road curvature, and traffic density, collectively defined the physical layout of the scenario. The movement trajectories of BVs were generated through the use of an intelligent driver model (IDM) and a MOBIL lane-changing model. The IDM ensured safe following distances by modeling acceleration based on headway and relative velocity, while the MOBIL evaluated the safety and incentive criteria for lane changes. However, the trajectories did not change in response to the behavior of the AV. The study used the highway-environment simulation package to create the simulation environment, as this package enables the simulation of various driving tasks, such as lane changing, parking, and navigating intersections. To introduce interference, the researchers increased the traffic density and added randomization factors, training an autonomous agent in this environment. In addition, a baseline model was trained in a conventional environment to compare its performance with the model trained under hazardous conditions.  
Both articles used domain knowledge to generate scenarios, but they differed in their approaches. The study by [18] primarily focused on dangerous driving behaviors generated through simulations and experiments, highlighting the system's adaptive capabilities. In contrast, ref. [16] relied on big data technology and existing accident data to generate scenarios, drawing on the extraction of historical knowledge.