Heterogeneous trajectory forecasting is critical for intelligent
transportation systems, while it is challenging because of the difficulty for
modeling the complex interaction relations among the heterogeneous road agents
as well as their agent-environment constraint. In this work, we propose a risk
and scene graph learning method for trajectory forecasting of heterogeneous
road agents, which consists of a Heterogeneous Risk Graph (HRG) and a
Hierarchical Scene Graph (HSG) from the aspects of agent category and their
movable semantic regions. HRG groups each kind of road agents and calculates
their interaction adjacency matrix based on an effective collision risk metric.
HSG of driving scene is modeled by inferring the relationship between road
agents and road semantic layout aligned by the road scene grammar. Based on
this formulation, we can obtain an effective trajectory forecasting in driving
situations, and superior performance to other state-of-the-art approaches is
demonstrated by exhaustive experiments on the nuScenes, ApolloScape, and
Argoverse datasets.

本文提出了一种利用风险和场景图学习方法来预测异构道路代理轨迹的方法，该方法包括异构风险图和层次场景图，具有在驾驶情况下更有效的轨迹预测，并且在 nuScenes，ApolloScape 和 Argoverse 数据集上展示了优越的性能。

通过风险和场景图学习进行异构轨迹预测

Heterogeneous Trajectory Forecasting via Risk and Scene Graph Learning

We present a visually grounded hierarchical planning algorithm for
long-horizon manipulation tasks. Our algorithm offers a joint framework of
neuro-symbolic task planning and low-level motion generation conditioned on the
specified goal. At the core of our approach is a two-level scene graph
representation, namely geometric scene graph and symbolic scene graph. This
hierarchical representation serves as a structured, object-centric abstraction
of manipulation scenes. Our model uses graph neural networks to process these
scene graphs for predicting high-level task plans and low-level motions. We
demonstrate that our method scales to long-horizon tasks and generalizes well
to novel task goals. We validate our method in a kitchen storage task in both
physical simulation and the real world. Our experiments show that our method
achieved over 70% success rate and nearly 90% of subgoal completion rate on the
real robot while being four orders of magnitude faster in computation time
compared to standard search-based task-and-motion planner.

我们提出了一种基于视觉的分层规划算法，利用神经符号任务规划和基于低级运动产生的联合框架，以指定目标为条件。该算法使用两级场景图形式来表示操作场景，即几何场景图和符号场景图，并使用图形神经网络来为高层任务规划和低层动作生成处理这些场景图。我们在物理模拟和现实世界中的厨房储物任务中验证了我们的方法，并证明我们的方法比标准的搜索任务和运动规划器运行速度高四个数量级。