2026-04-13T06:43:28Zhttps://recercat.cat/oai/request

oai:recercat.cat:10230/589772025-12-21T18:03:01Zcom_2072_6col_2072_452952

00925njm 22002777a 4500 dc Cabrero Daniel, Beatriz author Marques, Ricardo author Hoyet, Ludovic author Pettré, Julien author Blat, Josep author 2024-02-07T07:09:29Z 2022 Simulating crowds requires controlling a very large number of trajectories of characters and is usually performed using crowd steering algorithms. The question of choosing the right algorithm with the right parameter values is of crucial importance given the large impact on the quality of results. In this paper, we study the performance of a number of steering policies (i.e., simulation algorithm and its parameters) in a variety of contexts, resorting to an existing quality function able to automatically evaluate simulation results. This analysis allows us to map contexts to the performance of steering policies. Based on this mapping, we demonstrate that distributing the best performing policies among characters improves the resulting simulations. Furthermore, we also propose a solution to dynamically adjust the policies, for each agent independently and while the simulation is running, based on the local context each agent is currently in. We demonstrate significant improvements of simulation results compared to previous work that would optimize parameters once for the whole simulation, or pick an optimized, but unique and static, policy for a given global simulation context. CCS Concepts Motion path planning Agent / discrete models Multi-agent systems Dynamic combination of crowd steering policies based on context