Traffic Simulation

Traffic simulation aims to create realistic virtual environments for testing and analyzing traffic flow, particularly for autonomous vehicle development and transportation planning. Current research emphasizes developing highly controllable and realistic simulations using various techniques, including diffusion models, large language models (LLMs) for scenario generation and control, and deep learning architectures like transformers and graph neural networks to model agent behavior and interactions. These advancements are crucial for improving the safety and efficiency of autonomous vehicles and optimizing traffic management strategies in real-world settings.