Human motion prediction is important for mobile service robots and intelligent vehicles to operate safely and smoothly around people. The more accurate predictions are, particularly over extended periods of time, the better a system can, e.g., assess collision risks and plan ahead. In this paper, we propose to exploit maps of dynamics (MoDs, a class of general representations of place-dependent spatial motion patterns, learned from prior observations) for long-term human motion prediction (LHMP). We present a new MoD-informed human motion prediction approach, named CLiFF-LHMP, which is data efficient, explainable, and insensitive to errors from an upstream tracking system. Our approach uses CLiFF-map, a specific MoD trained with human motion data recorded in the same environment. We bias a constant velocity prediction with samples from the CLiFF-map to generate multi-modal trajectory predictions. In two public datasets we show that this algorithm outperforms the state of the art for predictions over very extended periods of time, achieving 45% more accurate prediction performance at 50s compared to the baseline.

翻译：人体运动预测对于移动服务机器人和智能车辆在人类周围安全平稳运行至关重要。预测越准确，尤其是在长时间跨度内，系统就能更好地评估碰撞风险并规划前瞻。本文提出利用动态地图（MoDs，一种从先验观测中学习到的位置依赖空间运动模式的通用表示类）进行长期人体运动预测（LHMP）。我们提出了一种新的基于MoD的人体运动预测方法，命名为CLiFF-LHMP，该方法具有数据高效、可解释性强且对上游跟踪系统的误差不敏感的特点。我们的方法使用CLiFF-map（一种通过在同一环境中记录的人体运动数据训练的特定MoD），将常速预测与CLiFF-map的样本进行偏置，以生成多模态轨迹预测。在两个公开数据集上，我们展示了该算法在超长时间跨度内的预测性能优于现有技术，在50秒时相比基线实现了45%更准确的预测表现。