Dynamic Combination of Crowd Steering Policies Based on Context. (24th May 2022)

• Record Type: Journal Article
• Authors: Cabrero‐Daniel, B.; Marques, R.; Hoyet, L.; Pettré, J.; Blat, J.
• Is Part Of: Computer graphics forum. Volume 41:Number 2(2022)
• Page Start: 209
• Page End: 219
• Publication Date: 2022-05-24
• Subjects: CCS Concepts -- Computing methodologies → Simulation evaluation -- Motion path planning -- Agent / discrete models -- Multi‐agent systems; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14469
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: 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.