This paper presents a novel data-driven crowd simulation method that can mimic the observed traffic of pedestrians in a given environment. Given a set of observed trajectories, we use a recent form of neural networks, Generative Adversarial Networks (GANs), to learn the properties of this set and generate new trajectories with similar properties. We define a way for simulated pedestrians (agents) to follow such a trajectory while handling local collision avoidance. As such, the system can generate a crowd that behaves similarly to observations, while still enabling real-time interactions between agents. Via experiments with real-world data, we show that our simulated trajectories preserve the statistical properties of their input. Our method simulates crowds in real time that resemble existing crowds, while also allowing insertion of extra agents, combination with other simulation methods, and user interaction.
翻译:本文展示了一种新的数据驱动人群模拟方法,可以模仿特定环境中行人观察到的行人流量。 在一组观测到的轨迹下, 我们使用最新的神经网络形式“ 基因反对网络 ” ( GANs) 来学习这个集的特性, 并生成具有类似特性的新轨迹。 我们定义了模拟行人( 试剂) 如何在进行本地避免碰撞时遵循这样的轨迹。 因此, 该系统可以产生一群与观测相似的人群, 同时也可以让代理商之间实时互动。 我们用真实世界数据进行虚拟轨迹实验, 我们显示模拟轨迹保存了他们输入的统计特性。 我们的方法模拟实时人群时与现有人群相似, 同时允许插入额外物剂, 与其他模拟方法相结合, 以及用户互动 。