Human motion prediction is essential for the safe and smooth operation of mobile service robots and intelligent vehicles around people. Commonly used neural network-based approaches often require large amounts of complete trajectories to represent motion dynamics in complex semantically-rich spaces. This requirement may complicate deployment of physical systems in new environments, especially when the data is being collected online from onboard sensors. In this paper we explore a data-efficient alternative using maps of dynamics (MoD) to represent place-dependent multi-modal spatial motion patterns, learned from prior observations. Our approach can perform efficient human motion prediction in the long-term perspective of up to 60 seconds. We quantitatively evaluate its accuracy with limited amount of training data in comparison to an LSTM-based baseline, and qualitatively show that the predicted trajectories reflect the natural semantic properties of the environment, e.g. the locations of short- and long-term goals, navigation in narrow passages, around obstacles, etc.

翻译：人体运动预测对于移动服务机器人和智能车辆在人群中安全平稳运行至关重要。常用的基于神经网络的方法通常需要大量完整轨迹来表示复杂语义空间中的运动动态。这一要求可能会增加物理系统在新环境中的部署难度，尤其是在从机载传感器在线收集数据的情况下。本文探索了一种数据高效的替代方案，利用动力学地图（MoD）表示基于位置的多模态空间运动模式，该模式从先前的观测中学习得到。我们的方法能够在长达60秒的长期视角下进行高效的人体运动预测。我们定量评估了该方法在有限训练数据下的准确性，并与基于LSTM的基线方法进行了对比；同时定性展示了预测轨迹反映了环境的自然语义特性，例如短期和长期目标点的位置、狭窄通道中的导航行为以及绕行障碍物等场景。