论文标题
ODE学习走路：基于ODE-NET的数据驱动建模
ODEs learn to walk: ODE-Net based data-driven modeling for crowd dynamics
论文作者
论文摘要
对于各种现实世界中的问题，预测行人人群的行为至关重要。数据驱动的建模旨在从观察到的数据中学习数学模型，是一个有前途的工具，可以构建可以对此类系统进行准确预测的模型。在这项工作中，我们提出了一种基于ODE-NET框架的数据驱动建模方法，用于构建人群动态的连续时间模型。我们讨论了将ODE-NET方法应用于此类问题的一些具有挑战性的问题，这些问题主要与基础人群系统的维度有关，我们建议通过将社交自力概念纳入ODE-NET框架中来解决这些问题。最后提供了申请示例，以证明所提出的方法的性能。
Predicting the behaviors of pedestrian crowds is of critical importance for a variety of real-world problems. Data driven modeling, which aims to learn the mathematical models from observed data, is a promising tool to construct models that can make accurate predictions of such systems. In this work, we present a data-driven modeling approach based on the ODE-Net framework, for constructing continuous-time models of crowd dynamics. We discuss some challenging issues in applying the ODE-Net method to such problems, which are primarily associated with the dimensionality of the underlying crowd system, and we propose to address these issues by incorporating the social-force concept in the ODE-Net framework. Finally application examples are provided to demonstrate the performance of the proposed method.
