Crowd simulations play a pivotal role in building design, influencing both
user experience and public safety. While traditional knowledge-driven models
have their merits, data-driven crowd simulation models promise to bring a new
dimension of realism to these simulations. However, most of the existing
data-driven models are designed for specific geometries, leading to poor
adaptability and applicability. A promising strategy for enhancing the
adaptability and realism of data-driven crowd simulation models is to
incorporate visual information, including the scenario geometry and pedestrian
locomotion. Consequently, this paper proposes a novel visual-information-driven
(VID) crowd simulation model. The VID model predicts the pedestrian velocity at
the next time step based on the prior social-visual information and motion data
of an individual. A radar-geometry-locomotion method is established to extract
the visual information of pedestrians. Moreover, a temporal convolutional
network (TCN)-based deep learning model, named social-visual TCN, is developed
for velocity prediction. The VID model is tested on three public pedestrian
motion datasets with distinct geometries, i.e., corridor, corner, and
T-junction. Both qualitative and quantitative metrics are employed to evaluate
the VID model, and the results highlight the improved adaptability of the model
across all three geometric scenarios. Overall, the proposed method demonstrates
effectiveness in enhancing the adaptability of data-driven crowd models.

本文提出了一种基于视觉信息的众包模拟模型（VID），该模型通过使用先前的社会视觉信息和个体的运动数据来预测行人下一时间步的速度。通过测试该模型在具有不同几何结构的三个公共行人运动数据集上，结果显示该模型在适应不同几何情景方面的能力得到了提升，证明了所提出方法在增强数据驱动人群模型的适应性方面的有效性。