Scenario Generation

Scenario generation focuses on creating realistic and diverse simulated situations for testing and evaluating complex systems, particularly in autonomous driving, robotics, and power systems. Current research emphasizes leveraging machine learning models, including variational autoencoders, diffusion models, and large language models, often in conjunction with reinforcement learning, to generate scenarios that capture intricate spatiotemporal dependencies and human-like behaviors. This field is crucial for improving the safety and reliability of autonomous systems and for optimizing the performance of complex systems under uncertainty, impacting both scientific understanding and real-world applications.