不完全な軌道データを用いた群衆シミュレーションのための協調的小規模・大規模モデル

Crowd simulation plays a crucial role in various domains, including entertainment, urban planning, and safety assessment. Data-driven methods offer significant advantages in simulating natural and diverse crowd behaviors, enabling highly realistic simulations. However, existing methods often face challenges due to incomplete trajectory data and limited generalization to unfamiliar scenarios. To address these limitations, we propose a novel crowd simulation framework based on the collaboration of a small model and a large model. Inspired by the dual-process decision-making mechanism in cognitive psychology, this framework enables efficient handling of familiar scenarios while leveraging the reasoning capabilities of large models in complex or unfamiliar environments. The small model, responsible for generating fast and reactive behaviors, is trained on real-world incomplete trajectory data to learn movement patterns. The large model, which performs simulation correction to refine failed behaviors, leverages past successful and failed experiences to enhance behavior generation in complex scenarios. Experimental results demonstrate that our framework significantly improves simulation accuracy in the presence of missing trajectory segments and enhances cross-scene generalization.