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。

基于视觉信息的人群仿真模型研究 —— 基于时空卷积网络

Visual-information-driven model for crowd simulation using temporal  convolutional network

Single frame data contains finite information which limits the performance of
the existing vision-based multi-camera 3D object detection paradigms. For
fundamentally pushing the performance boundary in this area, a novel paradigm
dubbed BEVDet4D is proposed to lift the scalable BEVDet paradigm from the
spatial-only 3D space to the spatial-temporal 4D space. We upgrade the naive
BEVDet framework with a few modifications just for fusing the feature from the
previous frame with the corresponding one in the current frame. In this way,
with negligible additional computing budget, we enable BEVDet4D to access the
temporal cues by querying and comparing the two candidate features. Beyond
this, we simplify the task of velocity prediction by removing the factors of
ego-motion and time in the learning target. As a result, BEVDet4D with robust
generalization performance reduces the velocity error by up to -62.9%. This
makes the vision-based methods, for the first time, become comparable with
those relied on LiDAR or radar in this aspect. On challenge benchmark nuScenes,
we report a new record of 54.5% NDS with the high-performance configuration
dubbed BEVDet4D-Base, which surpasses the previous leading method BEVDet-Base
by +7.3% NDS. The source code is publicly available for further research at
this https URL .

提出一种名为 BEVDet4D 的新型监测方法，将视觉单帧数据提升至时空 4D 空间，通过对前一帧和当前帧候选特征的对比，使 BEVDet4D 能够访问时间线索，已达到与雷达检测方法相媲美的检测表现