Reasoning about human motion is an important prerequisite to safe and socially-aware robotic navigation. As a result, multi-agent behavior prediction has become a core component of modern human-robot interactive systems, such as self-driving cars. While there exist many methods for trajectory forecasting, most do not enforce dynamic constraints and do not account for environmental information (e.g., maps). Towards this end, we present Trajectron++, a modular, graph-structured recurrent model that forecasts the trajectories of a general number of diverse agents while incorporating agent dynamics and heterogeneous data (e.g., semantic maps). Trajectron++ is designed to be tightly integrated with robotic planning and control frameworks; for example, it can produce predictions that are optionally conditioned on ego-agent motion plans. We demonstrate its performance on several challenging real-world trajectory forecasting datasets, outperforming a wide array of state-of-the-art deterministic and generative methods.

中文翻译：

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Trajectron++：使用异构数据进行动态可行的轨迹预测

对人体运动的推理是安全和具有社会意识的机器人导航的重要先决条件。因此，多智能体行为预测已成为现代人机交互系统（如自动驾驶汽车）的核心组成部分。虽然存在许多轨迹预测方法，但大多数方法不强制执行动态约束，也不考虑环境信息（例如，地图）。为此，我们提出了 Trajectron++，这是一种模块化的、图结构的循环模型，可以在结合代理动态和异构数据（例如语义图）的同时预测一般数量的不同代理的轨迹。Trajectron++ 旨在与机器人规划和控制框架紧密集成；例如，它可以产生选择性地以自我代理运动计划为条件的预测。