Simulation of collision avoidance behavior in crowd movement by data-driven approach
Crowd movement simulation is essential for pedestrian safety management and facility layout optimization. Data-driven models enhance trajectory prediction accuracy under Euclidean metrics, yet they suffer from excessively high collision rates, especially in bidirectional and multidirectional flows. In this paper, we establish a novel data-driven crowd simulation model that incorporates the pedestrian collision mechanism into the loss function to reduce collisions. A new lateral-acceleration-based collision loss function and a Voronoi-based motion feature extraction approach are proposed. The model is based on a Generative Adversarial Network (GAN) architecture and is termed CPGAN (Collisi...
| Authors | Xuanwen Liang, Eric Wai Ming Lee |
|---|---|
| Primary category | cs.RO |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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