Narang Sahil, Best Andrew, Curtis Sean, Manocha Dinesh
Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina, USA.
PLoS One. 2015 Apr 13;10(4):e0117856. doi: 10.1371/journal.pone.0117856. eCollection 2015.
Pedestrian crowds often have been modeled as many-particle system including microscopic multi-agent simulators. One of the key challenges is to unearth governing principles that can model pedestrian movement, and use them to reproduce paths and behaviors that are frequently observed in human crowds. To that effect, we present a novel crowd simulation algorithm that generates pedestrian trajectories that exhibit the speed-density relationships expressed by the Fundamental Diagram. Our approach is based on biomechanical principles and psychological factors. The overall formulation results in better utilization of free space by the pedestrians and can be easily combined with well-known multi-agent simulation techniques with little computational overhead. We are able to generate human-like dense crowd behaviors in large indoor and outdoor environments and validate the results with captured real-world crowd trajectories.
行人人群通常被建模为多粒子系统，包括微观多智能体模拟器。其中一个关键挑战是挖掘能够模拟行人运动的控制原理，并利用这些原理重现人类人群中经常观察到的路径和行为。为此，我们提出了一种新颖的人群模拟算法，该算法生成的行人轨迹呈现出由基本图所表达的速度-密度关系。我们的方法基于生物力学原理和心理因素。整体公式化使得行人能够更好地利用自由空间，并且可以很容易地与著名的多智能体模拟技术相结合，计算开销很小。我们能够在大型室内和室外环境中生成类似人类的密集人群行为，并通过捕获的真实世界人群轨迹验证结果。
