Real-time navigation in dense human environments is a challenging problem in
robotics. Most existing path planners fail to account for the dynamics of
pedestrians because introducing time as an additional dimension in search space
is computationally prohibitive. Alternatively, most local motion planners only
address imminent collision avoidance and fail to offer long-term optimality. In
this work, we present an approach, called Dynamic Channels, to solve this
global to local quandary. Our method combines the high-level topological path
planning with low-level motion planning into a complete pipeline. By
formulating the path planning problem as graph searching in the triangulation
space, our planner is able to explicitly reason about the obstacle dynamics and
capture the environmental change efficiently. We evaluate efficiency and
performance of our approach on public pedestrian datasets and compare it to a
state-of-the-art planning algorithm for dynamic obstacle avoidance.

Dynamic Channels 是一种在机器人密集人类环境中进行实时导航的新方法，能够通过在三角空间中基于图搜索来解决路径规划问题，考虑障碍物动态并高效地捕捉环境变化，具备较高的效率和性能，并可用于动态障碍物避免。

动态通道：人群导航规划框架

Dynamic Channel: A Planning Framework for Crowd Navigation

We develop predictive models of pedestrian dynamics by encoding the coupled
nature of multi-pedestrian interaction using game theory, and deep
learning-based visual analysis to estimate person-specific behavior parameters.
Building predictive models for multi-pedestrian interactions however, is very
challenging due to two reasons: (1) the dynamics of interaction are complex
interdependent processes, where the predicted behavior of one pedestrian can
affect the actions taken by others and (2) dynamics are variable depending on
an individuals physical characteristics (e.g., an older person may walk slowly
while the younger person may walk faster). To address these challenges, we (1)
utilize concepts from game theory to model the interdependent decision making
process of multiple pedestrians and (2) use visual classifiers to learn a
mapping from pedestrian appearance to behavior parameters. We evaluate our
proposed model on several public multiple pedestrian interaction video
datasets. Results show that our strategic planning model explains human
interactions 25% better when compared to state-of-the-art methods.

本研究开发了一种预测多个行人之间交互的模型，采用了博弈论和基于深度学习的视觉分析相结合的方法，可以更好地解释人类的互动，相比现有技术的最先进方法提升 25% 的预测精度。

虚拟博弈预测行人交互动态

Forecasting Interactive Dynamics of Pedestrians with Fictitious Play

Human crowds often bear a striking resemblance to interacting particle
systems, and this has prompted many researchers to describe pedestrian dynamics
in terms of interaction forces and potential energies. The correct quantitative
form of this interaction, however, has remained an open question. Here, we
introduce a novel statistical-mechanical approach to directly measure the
interaction energy between pedestrians. This analysis, when applied to a large
collection of human motion data, reveals a simple power law interaction that is
based not on the physical separation between pedestrians but on their projected
time to a potential future collision, and is therefore fundamentally
anticipatory in nature. Remarkably, this simple law is able to describe human
interactions across a wide variety of situations, speeds and densities. We
further show, through simulations, that the interaction law we identify is
sufficient to reproduce many known crowd phenomena.

研究人员通过统计力学方法测量步行者之间的相互作用能量，发现一种基于未来潜在碰撞时间的幂律相互作用规律，其可以精确描述在各种情况下的人类群体互动现象。

行人相互作用的普适幂律

Universal power law governing pedestrian interactions

A kind of fluid dynamic description for the collective movement of
pedestrians is developed on the basis of a Boltzmann-like gaskinetic model. The
differences between these pedestrian specific equations and those for ordinary
fluids are worked out, for example concerning the mechanism of relaxation to
equilibrium, the role of ``pressure'', the special influence of internal
friction and the origin of ``temperature''. Some interesting results are
derived that can be compared to real situations, for example the development of
walking lanes and of pedestrian jams, the propagation of waves, and the
behavior on a dance floor. Possible applications of the model to town- and
traffic-planning are outlined.

本篇论文基于 Boltzmann-like gaskinetic model 的理论，发展了一种描述行人集体运动的流体动力学描述。此论文得到了一些与现实情况相应的有趣结果，并提出了将该模型应用于城镇和交通规划中的可能性。