论文标题
多代理轨迹预测和模糊查询的关注
Multi-agent Trajectory Prediction with Fuzzy Query Attention
论文作者
论文摘要
在众多领域，例如流量预测，行人跟踪和路径规划等许多领域，具有多个代理和实体的场景的轨迹预测是一个具有挑战性的问题。我们提出了一个一般体系结构，以应对这一挑战，该挑战模拟了运动的至关重要的动态偏见，即惯性，相对运动，意图和相互作用。具体而言，我们提出了一个关系模型，以灵活地模拟不同环境中代理之间的相互作用。由于众所周知，人类决策本质上是模糊的，因此我们模型的核心是一种新颖的注意机制，它通过做出连续值（模糊）决策并学习相应的响应来建模相互作用。我们的体系结构表明，在人群轨迹，美国高速公路交通，NBA体育数据和物理数据集等不同领域中现有的最新预测模型上表现出了显着的性能提高。我们还提供消融和增强，以了解我们模型中决策过程和收益的来源。
Trajectory prediction for scenes with multiple agents and entities is a challenging problem in numerous domains such as traffic prediction, pedestrian tracking and path planning. We present a general architecture to address this challenge which models the crucial inductive biases of motion, namely, inertia, relative motion, intents and interactions. Specifically, we propose a relational model to flexibly model interactions between agents in diverse environments. Since it is well-known that human decision making is fuzzy by nature, at the core of our model lies a novel attention mechanism which models interactions by making continuous-valued (fuzzy) decisions and learning the corresponding responses. Our architecture demonstrates significant performance gains over existing state-of-the-art predictive models in diverse domains such as human crowd trajectories, US freeway traffic, NBA sports data and physics datasets. We also present ablations and augmentations to understand the decision-making process and the source of gains in our model.
