Modeling the impact of interaction on pedestrian group motion

Mobile social robots aimed at interacting with and assisting humans in pedestrian areas need to understand the dynamics of pedestrian social interaction. In this work, we investigate the effect of interaction on pedestrian group motion by defining three motion models to represent (1) interpersonal-distance, (2) relative orientation and (3) absolute difference of velocities; and model them using a dataset of 12000+ pedestrian trajectories recorded in uncontrolled settings. Our contributions include: (i) Demonstrating that interaction has a prominent effect on the empirical distributions of the proposed joint motion attributes, where increasing levels of interaction lead to more regular behavior (ii) Developing analytic motion models of such distributions and reflect the effect of interaction on model parameters (iii) Detecting the social groups in a crowd with almost perfect accuracy utilizing the proposed models, despite the constant flow direction in the environment which causes unrelated pedestrians to move in a correlated way, and thus makes group recognition more difficult (iv) Estimating the level of intensity with considerable rates utilizing the proposed models旨在在行人区域与人类交互并提供协助的移动社交机器人，需要理解行人社交互动的动态特性。在本研究中，我们通过定义三种运动模型分别表征（1）人际距离、（2）相对朝向以及（3）速度绝对差值，以此探究社交互动对行人群体运动的影响；并利用在非受控环境中录制的12000余条行人轨迹数据集对上述模型进行建模。本研究的贡献包括：（i）验证了社交互动对所提出的联合运动属性的经验分布具有显著影响，且互动强度越高，行人行为越规整；（ii）构建了此类分布的解析运动模型，并揭示了社交互动对模型参数的影响机制；（iii）尽管环境中存在持续的人流方向，会导致无关行人的运动呈现相关性，进而增加群体识别难度，但利用所提模型仍可实现近乎完美的人群社交群体检测准确率；（iv）借助所提模型可实现较高精度的互动强度等级估计。