# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 3 Scenario Generation Methods
### Adaptive Test Scenario Generation Methods  
To overcome the limitations of non-adaptive test scenarios, the development of adaptive test scenarios has become a key research focus in recent years. An adaptive test scenario for AVs involves the autonomous learning and decision-making of surrounding elements, such as other vehicles, pedestrians, and animals, based on the real-time situation. In these scenarios, the entities within the test environment possess some degree of intelligence and learning capabilities, with their movements and actions generated autonomously through models, algorithms, or learning processes. The behavior of these elements can be influenced by external factors, such as the actions of other vehicles, traffic conditions, and obstacles.  
Consider a two-lane road scenario where AV and BV are traveling on separate lanes. A cut-in scenario occurs when the AV attempts to change into the lane occupied by the BV. This is illustrated in Figure 5.  
In a non-adaptive scenario, the BV is typically assumed to be traveling at a constant speed. Consequently, the BV's motion is easily predictable and so this test does not really test the AV's decision-making ability [40]. The assumption that the BV travels at a constant speed throughout the process is a very strong one and is very unrealistic.  
In order to overcome the limitations of non-adaptive scenarios described above, adaptive scenarios need to be used. In such scenarios, the environment the AV experiences becomes more dynamic. Using a cut-in scenario as an example, the strong assumption that the speed of the BV remains constant will be removed in an adaptive scenario. In other  
Figure 5: Cut-in scenario representation.  
words, the BV has the freedom to drive at any speed within the legal speed limit of the road, and it can accelerate or decelerate at any time throughout the cut-in or lane-changing process. The only restriction is that BV remains within the speed limit, which allows for a more controlled and predictable testing environment for the AV.  
Since adaptive scenarios involve more variables than non-adaptive ones, they are more difficult to design. Appropriate designing of adaptive test scenarios should be governed by the purpose of the tests. For our example, the purpose is to test whether the AV can adaptively perform lane-changing operations based on changes in the BV's behavior. Passing this test could entail one of three possible outcomes--the AV cut-in in front of the BV, the AV changed lane behind the BV, and the lane-changing was abandoned if it was not safe enough to do so. An additional criterion for determining success could be that the position of the AV relative to the BV after the lane change is above a certain safety threshold. This would be a more comprehensive and realistic test for this ODD.  
In subsequent subsections, we will focus on the literature on adaptive scenarios. Adaptive test scenario generation methods can be categorized into three categories. These are reinforcement-learning-based methods, importance-sampling-based methods, and imitation-learning-based methods.