arXiv:2605.31210v1 Announce Type: cross Abstract: 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 moti
This research addresses a known limitation in data-driven crowd simulation models, specifically high collision rates, indicating a maturation of AI techniques for complex, dynamic environments.
Improved crowd movement simulation can significantly enhance pedestrian safety, optimize urban planning, and inform the design of autonomous systems operating in human environments.
The ability to accurately simulate and predict collision avoidance in dense crowds will lead to more reliable and safer deployments of AI in public spaces, from smart cities to robotics.
- · Smart city developers
- · Urban planners
- · Robotics companies
- · Safety management systems
- · Inefficient crowd management solutions
More accurate crowd simulations reduce risks in public events and infrastructure design.
This capability informs the development of more sophisticated navigation and interaction algorithms for autonomous vehicles and robots.
Advanced understanding of crowd dynamics could influence social engineering and complex systems modeling in unexpected ways.
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Read at arXiv cs.AI