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This paper introduces a collaborative crowd simulation framework that combines a small, fast model trained on incomplete trajectory data with a large model for simulation correction in complex scenarios. Drawing inspiration from dual-process decision-making, the framework uses the small model for efficient handling of familiar situations and the large model to refine behaviors in unfamiliar environments by leveraging past successful and failed experiences. Experiments show improved simulation accuracy with missing data and enhanced cross-scene generalization.
Crowd simulations get a boost in accuracy and generalization by combining the speed of small models with the reasoning of large models, even when training data is incomplete.
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.
