The neighborhood of interaction in human crowds is explained by visual information
The neighborhood of interaction in human crowds is explained by visual information
复制标题
人群中互动的邻域是通过视觉信息来解释的
DOI：
10.1167/jov.21.9.2840
复制
发表时间：
2021
影响因子：
1.8
通讯作者：
Dachner, Gregory
中科院分区：
文献类型：
--
作者：
Warren, William;Dachner, Gregory
大多数集体运动的模型都是基于相互作用邻域中其他物体的物理位置和速度。例如，在我们的物理模型（PRSB 2018，CDPS 2018）中，行人匹配邻居的平均前进方向和速度，并根据他们的距离进行加权：权重逐渐衰减到最近的邻居，并且在人群中更快。最近，我们开发了一种视觉模型，其中行人的航向和速度通过使邻居的平均角速度和光学扩展为零来控制，这是偏心率的正弦函数（VSS 2017，2019）。部分闭塞会按比例降低相邻影响（ICPA 2019）。在这里，我们使用这些模型来模拟没有自由参数的三个实验的数据。Exp.图1：参与者与12个邻居组成的虚拟人群一起“散步”。在每次试验中，邻居的子集（0，3，6，9或12）改变方向（±10）或速度（±0.3 m/s），并记录参与者的步行速度和方向。目视航向的RMSE为1.97，物理模型为2.08（BF= 1.90，目视的轶事证据）。速度的RMSE分别为0.063 m/s和0.064 m/s（BF= 2.43，目视轶事）。Exp. 2：虚拟人群的距离变化（2、4或6 m），一行（近、中、远）改变方向（±10）。抽穗期的RMSE分别为2.5和3.6（BF>> 100，对视觉起决定性作用）。Exp. 3：人类“群”（N= 10，16，20）一起行走2分钟试验。我们模拟了30个10秒的片段，用邻居的输入对一个参与者进行建模。抽穗期的RMSE分别为15和29（BF= 6，视觉显著）。值得注意的是，随着距离的逐渐衰减是由欧几里得定律解释的，而快速衰减是遮挡的附加效应。因此，相互作用的邻域由视觉信息解释，消除了显式的距离项。
Most models of collective motion are based on the physical positions and velocities of others in a neighborhood of interaction. For example, in our physical model (PRSB 2018, CDPS 2018) a pedestrian matches the average heading direction and speed of neighbors, weighted by their distance: weights decay gradually to the nearest neighbor, and more rapidly in the crowd. Recently we developed a visual model, in which a pedestrian’s heading and speed are controlled by nulling the average angular velocity and optical expansion of neighbors, which are sinusoidal functions of eccentricity (VSS 2017, 2019). Neighbor influence is proportionally reduced by partial occlusion (ICPA 2019). Here we use these models to simulate data from three experiments with no free parameters. Exp. 1: Participants “walked with” a virtual crowd of 12 neighbors. A subset of neighbors (0, 3, 6, 9, or 12) changed direction (±10) or speed (±0.3 m/s) on each trial, and the participant’s walking speed and heading were recorded. The RMSE of heading was 1.97 for the visual and 2.08 for the physical model (BF= 1.90, anecdotal evidence for visual). The RMSE of speed was 0.063 m/s and 0.064 m/s, respectively (BF= 2.43, anecdotal for visual). Exp. 2: The distance of the virtual crowd was varied (2, 4, or 6m), and one row (near, middle or far) changed direction (±10). The RMSE of heading was 2.5 and 3.6, respectively (BF>> 100, decisive for visual). Exp. 3: A human ‘swarm’(N= 10, 16, 20) walked together for 2min trials. We simulated thirty 10s segments, modeling one participant with input from neighbors. The RMSE of heading was 15 and 29, respectively (BF= 6, substantial for visual). Remarkably, the gradual decay with distance is explained by Euclid’s law, while the rapid decay is an additional effect of occlusion. The neighborhood of interaction is thus explained by visual information, eliminating explicit distance terms.