# A Survey of Scenario Generation for Automated Vehicle Testing and Validation
## 3 Scenario Generation Methods
### Non-Adaptive Test Scenario Generation Methods
#### 3.1.2 Data-Driven Generation Methods  
Data-driven methods refer to using real-world driving data, such as traffic accident records and vehicle sensor data, to generate test scenarios. Typically, big data analytics are employed to extract valuable scenario insights from large datasets.  
The article in [21] introduced a framework called \"Accelerated Deployment\" designed for the safe and efficient testing of AVs that are not yet in production on public streets. This approach involved strategically selecting testing environments based on metrics such as safety risks, learning potential, and deployment costs. Accelerated evaluation, as discussed in [22; 23], involves increasing the frequency of observed failures through an importance sampling mechanism [24], which adjusts the sampling distribution to \"skew\" the evaluation scenarios. Furthermore, the criticality of a scenario was assessed by analyzing the AV's capability to avoid risk events in various deployment settings. The probability of the AV encountering or engaging in a risk event at any given moment was quantified using the cumulative intensity function.  
The work in [25] proposed a neural autoregressive model to generate realistic test scenarios. Based on the current state of the AV and a high-definition map of its surrounding environment, the ConvLSTM [26] architecture, an extension of the LSTM [27], was used to sequentially generate the trajectories and driving velocities for vehicles, pedestrians, and cyclists. The modeling process begins by classifying participants into categories using neural networks [28] to identify their respective types. Convolutional neural networks (CNNs) [29] are then used to model locations based on these categories. For vehicles and cyclists, directed bounding box modeling is applied, followed by velocity modeling to complete the representation of other traffic participants, excluding the AV itself.  
The study in [30] proposed an editing framework based on reinforcement learning [31], which considers scenario generation as an optimization problem, aimed at identifying the most risky scenario within realistic constraints. This approach utilizes deep reinforcement learning (DRL) to edit scenarios dynamically. The state space represents the scenario as unordered sets, incorporating a traffic agent feature matrix along with static environmental features. The reinforcement learning agent can perform various operations to modify scenarios, such as perturbing the trajectories of existing traffic agents, reconfiguring agent paths, introducing new agents, and marking undrivable regions. The reward function combines a risk model and a plausibility model. The risk model evaluates risk by calculating the number of feasible driving plans for autonomous vehicles in the scenario. The fewer plans, the higher the risk of the scenario. The plausibility model ensures the generated scenarios align with real-world constraints using conditional variational autoencoders (CVAE) [32], penalizing unrealistic data.In addition, the policy network uses a graph neural network (GNN) [33] to calculate the embedding of the scenario, and then outputs the next editing action. The reinforcement learning algorithm was optimized using proximal policy optimization (PPO) [34] to generate diverse security-critical scenarios.