We consider the problem of group interactions in urban driving.
State-of-the-art behavior planners for self-driving cars mostly consider each
single agent-to-agent interaction separately in a cost function in order to
find an optimal behavior for the ego agent, such as not colliding with any of
the other agents. In this paper, we develop risk shadowing, a situation
understanding method that allows us to go beyond single interactions by
analyzing group interactions between three agents. Concretely, the presented
method can find out which first other agent does not need to be considered in
the behavior planner of an ego agent, because this first other agent cannot
reach the ego agent due to a second other agent obstructing its way. In
experiments, we show that using risk shadowing as an upstream filter module for
a behavior planner allows to plan more decisive and comfortable driving
strategies than state of the art, given that safety is ensured in these cases.
The usability of the approach is demonstrated for different intersection
scenarios and longitudinal driving.

我们研究城市驾驶中的团体互动问题，提出了一种情景理解方法 —— 风险遮蔽，该方法通过分析三个代理之间的团体互动，使自动驾驶汽车能够制定更明确、更舒适的驾驶策略，同时确保安全性。

引入风险遮蔽以实现决策和舒适行为规划

Introducing Risk Shadowing For Decisive and Comfortable Behavior  Planning

Modeling the dynamics of people walking is a problem of long-standing
interest in computer vision. Many previous works involving pedestrian
trajectory prediction define a particular set of individual actions to
implicitly model group actions. In this paper, we present a novel architecture
named GP-Graph which has collective group representations for effective
pedestrian trajectory prediction in crowded environments, and is compatible
with all types of existing approaches. A key idea of GP-Graph is to model both
individual-wise and group-wise relations as graph representations. To do this,
GP-Graph first learns to assign each pedestrian into the most likely behavior
group. Using this assignment information, GP-Graph then forms both intra- and
inter-group interactions as graphs, accounting for human-human relations within
a group and group-group relations, respectively. To be specific, for the
intra-group interaction, we mask pedestrian graph edges out of an associated
group. We also propose group pooling&unpooling operations to represent a group
with multiple pedestrians as one graph node. Lastly, GP-Graph infers a
probability map for socially-acceptable future trajectories from the integrated
features of both group interactions. Moreover, we introduce a group-level
latent vector sampling to ensure collective inferences over a set of possible
future trajectories. Extensive experiments are conducted to validate the
effectiveness of our architecture, which demonstrates consistent performance
improvements with publicly available benchmarks. Code is publicly available at
this https URL

本文介绍了一种新颖的架构，称为 GP 图，它具有有效的集体群表示法来预测拥挤环境下人行路径，同时兼容所有现有方法。GP-Graph 模型了解了个体和群体之间的关系，通过图形表示来表征它们，为个人之间的关系和一组之间的关系建立了图形交互，并在这些交互中推断社交可接受的未来轨迹的概率图。

多模态轨迹预测中行人群体表示学习

Learning Pedestrian Group Representations for Multi-modal Trajectory  Prediction

Hypergraphs provide a natural way of representing group relations, whose
complexity motivates an extensive array of prior work to adopt some form of
abstraction and simplification of higher-order interactions. However, the
following question has yet to be addressed: How much abstraction of group
interactions is sufficient in solving a hypergraph task, and how different such
results become across datasets? This question, if properly answered, provides a
useful engineering guideline on how to trade off between complexity and
accuracy of solving a downstream task. To this end, we propose a method of
incrementally representing group interactions using a notion of n-projected
graph whose accumulation contains information on up to n-way interactions, and
quantify the accuracy of solving a task as n grows for various datasets. As a
downstream task, we consider hyperedge prediction, an extension of link
prediction, which is a canonical task for evaluating graph models. Through
experiments on 15 real-world datasets, we draw the following messages: (a)
Diminishing returns: small n is enough to achieve accuracy comparable with
near-perfect approximations, (b) Troubleshooter: as the task becomes more
challenging, larger n brings more benefit, and (c) Irreducibility: datasets
whose pairwise interactions do not tell much about higher-order interactions
lose much accuracy when reduced to pairwise abstractions.

通过实验得出以下结论：（a）小 n 足以实现精度与近乎完美近似值相当， (b) 当任务变得更加具有挑战性时，大的 n 会带来更多益处， (c) 对于那些成对交互不太能够说出高阶交互的数据集，在降为两两抽象时，将失去很多精度。