论文标题
DAG-NET：双专注于轨迹预测的图形神经网络
DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
论文作者
论文摘要
对于自动驾驶汽车或社会机器人等几种可能的应用，理解人类运动行为是一项关键任务，通常对于所有自主代理必须在以人为中心的环境中导航的设置。这是非平凡的，因为人类运动本质上是多模式的：鉴于人类运动路径的历史，人们将来有许多合理的方式。此外，人们的活动通常是由目标驱动的，例如到达特定位置或与环境互动。我们通过提出一种新的经常性生成模型来解决上述方面，该模型考虑了单一代理的未来目标和不同代理之间的互动。该模型利用了一个基于双重注意力的图形神经网络，以收集有关不同代理之间相互影响的信息，并将其与有关代理可能未来目标的数据集成在一起。我们的建议足以将其应用于不同的方案：该模型在城市环境和体育应用程序中都能达到最新的结果。
Understanding human motion behaviour is a critical task for several possible applications like self-driving cars or social robots, and in general for all those settings where an autonomous agent has to navigate inside a human-centric environment. This is non-trivial because human motion is inherently multi-modal: given a history of human motion paths, there are many plausible ways by which people could move in the future. Additionally, people activities are often driven by goals, e.g. reaching particular locations or interacting with the environment. We address the aforementioned aspects by proposing a new recurrent generative model that considers both single agents' future goals and interactions between different agents. The model exploits a double attention-based graph neural network to collect information about the mutual influences among different agents and to integrate it with data about agents' possible future objectives. Our proposal is general enough to be applied to different scenarios: the model achieves state-of-the-art results in both urban environments and also in sports applications.
