Traffic Scene Generation

Traffic scene generation focuses on computationally creating realistic and diverse simulated traffic environments, primarily to aid in the development and testing of autonomous driving systems. Current research emphasizes leveraging deep learning models, particularly diffusion models and transformers, often combined with large language models for enhanced controllability and scene diversity based on textual descriptions or other input conditions. These advancements aim to overcome limitations in existing methods by generating more realistic, varied, and controllable scenarios, ultimately improving the safety and reliability of autonomous vehicles through more robust training and testing. The resulting synthetic datasets also offer valuable resources for broader research in computer vision and AI.