根据效用和个人空间的动态步幅调整
Dynamic Stride Length Adaptation According to Utility And Personal Space
摘要：
当行人在狭窄的角落或密集的人群中穿行时，他们会调整速度和步幅。他们试图避免碰撞，并保持他们的个人空间。最先进的行人运动模型在密集的人群中自动降低速度，因为没有行人可以去的空间。然而，步幅长度及其正确的适应性很少被考虑。这导致影响宏观观察参数的伪影，例如瓶颈前面的密度，并通过此影响流动。因此，建模步幅适应对于提高行人模型的预测能力是重要的。为了实现这一点，我们重新制定的问题作为一个优化问题的磁盘周围的行人。每个行人寻求的位置是最有吸引力的平衡目标之间的搜索目标，个人空间的需要和需要保持距离的障碍。对空间的需求是根据心理学的研究结果来建模的，这些研究结果定义了一个人周围的区域，当被侵犯时，会引起不安。其结果是一个全自动的调整，允许通过有意义的社会参数进行校准，并给出视觉上自然的结果，与测量的实验数据非常吻合。
英文摘要：
Pedestrians adjust both speed and stride length when they navigate difficult situations such as tight corners or dense crowds. They try to avoid collisions and to preserve their personal space. State-of-the-art pedestrian motion models automatically reduce speed in dense crowds simply because there is no space where the pedestrians could go. The stride length and its correct adaptation, however, are rarely considered. This leads to artefacts that impact macroscopic observation parameters such as densities in front of bottlenecks and, through this, flow. Hence modelling stride adaptation is important to increase the predictive power of pedestrian models. To achieve this we reformulate the problem as an optimisation problem on a disk around the pedestrian. Each pedestrian seeks the position that is most attractive in a sense of balanced goals between the search for targets, the need for individual space and the need to keep a distance from obstacles. The need for space is modelled according to findings from psychology defining zones around a person that, when invaded, cause unease. The result is a fully automatic adjustment that allows calibration through meaningful social parameters and that gives visually natural results with an excellent fit to measured experimental data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
