Predicting pedestrian motion trajectories is crucial for path planning and
motion control of autonomous vehicles. Accurately forecasting crowd
trajectories is challenging due to the uncertain nature of human motions in
different environments. For training, recent deep learning-based prediction
approaches mainly utilize information like trajectory history and interactions
between pedestrians, among others. This can limit the prediction performance
across various scenarios since the discrepancies between training datasets have
not been properly incorporated. To overcome this limitation, this paper
proposes a graph transformer structure to improve prediction performance,
capturing the differences between the various sites and scenarios contained in
the datasets. In particular, a self-attention mechanism and a domain adaption
module have been designed to improve the generalization ability of the model.
Moreover, an additional metric considering cross-dataset sequences is
introduced for training and performance evaluation purposes. The proposed
framework is validated and compared against existing methods using popular
public datasets, i.e., ETH and UCY. Experimental results demonstrate the
improved performance of our proposed scheme.

该论文提出了一个图变换器结构来改进预测性能，捕捉数据集中不同场所和情景之间的差异，并设计了一种自注意机制和域适应模块来提高模型的泛化能力。此外，还引入了一种考虑跨数据集序列的额外指标用于训练和性能评估目的。使用 ETH 和 UCY 等流行的公共数据集验证和比较了所提出的框架，实验结果表明了我们提出的方案的改进性能。

基于知识感知的图形转换器用于行人轨迹预测

Knowledge-aware Graph Transformer for Pedestrian Trajectory Prediction

Accurate weather forecasting holds significant importance to human
activities. Currently, there are two paradigms for weather forecasting:
Numerical Weather Prediction (NWP) and Deep Learning-based Prediction (DLP).
NWP utilizes atmospheric physics for weather modeling but suffers from poor
data utilization and high computational costs, while DLP can learn weather
patterns from vast amounts of data directly but struggles to incorporate
physical laws. Both paradigms possess their respective strengths and
weaknesses, and are incompatible, because physical laws adopted in NWP describe
the relationship between coordinates and meteorological variables, while DLP
directly learns the relationships between meteorological variables without
consideration of coordinates. To address these problems, we introduce the
DeepPhysiNet framework, incorporating physical laws into deep learning models
for accurate and continuous weather system modeling. First, we construct
physics networks based on multilayer perceptrons (MLPs) for individual
meteorological variable, such as temperature, pressure, and wind speed. Physics
networks establish relationships between variables and coordinates by taking
coordinates as input and producing variable values as output. The physical laws
in the form of Partial Differential Equations (PDEs) can be incorporated as a
part of loss function. Next, we construct hyper-networks based on deep learning
methods to directly learn weather patterns from a large amount of
meteorological data. The output of hyper-networks constitutes a part of the
weights for the physics networks. Experimental results demonstrate that, upon
successful integration of physical laws, DeepPhysiNet can accomplish multiple
tasks simultaneously, not only enhancing forecast accuracy but also obtaining
continuous spatiotemporal resolution results, which is unattainable by either
the NWP or DLP.

将物理定律与深度学习模型相结合的 DeepPhysiNet 框架可以实现准确连续的天气系统建模，增强预测精度并获得连续的时空分辨率结果。