论文标题
通过神经运动消息传递的协作运动预测
Collaborative Motion Prediction via Neural Motion Message Passing
论文作者
论文摘要
运动预测对于自动驾驶汽车和社会机器人来说是必不可少的，并且具有挑战性。运动预测的一个挑战是对交通行为者之间的相互作用进行建模，这可以彼此合作，以避免碰撞或形式组。为了应对这一挑战，我们提出了神经运动消息传递（NMMP），以明确模拟与参与者之间的定向相互作用的相互作用并学习表示形式。根据提议的NMMP，我们设计了两个设置的运动预测系统：行人设置以及关节行人和车辆设置。这两个系统都共享一个共同的模式：我们使用单个分支来对单个演员的行为和交互分支进行建模，以模拟参与者之间的相互作用，而与不同的包装器则处理不同的输入格式和特征。实验结果表明，这两个系统在几种现有基准上都优于先前的最新方法。此外，我们为互动学习提供了解释性。
Motion prediction is essential and challenging for autonomous vehicles and social robots. One challenge of motion prediction is to model the interaction among traffic actors, which could cooperate with each other to avoid collisions or form groups. To address this challenge, we propose neural motion message passing (NMMP) to explicitly model the interaction and learn representations for directed interactions between actors. Based on the proposed NMMP, we design the motion prediction systems for two settings: the pedestrian setting and the joint pedestrian and vehicle setting. Both systems share a common pattern: we use an individual branch to model the behavior of a single actor and an interactive branch to model the interaction between actors, while with different wrappers to handle the varied input formats and characteristics. The experimental results show that both systems outperform the previous state-of-the-art methods on several existing benchmarks. Besides, we provide interpretability for interaction learning.
