用动态地图预测60秒内人类运动，提升机器人安全交互能力
Long-Term Human Motion Prediction Using Spatio-Temporal Maps of Dynamics
- 引入动态地图（MoDs）编码时空运动模式作为环境特征
- 在真实数据集上误差降低50%，时间感知版本精度最高
- 适合自动驾驶与人机交互场景，代码开源可复现
长期人类运动预测（LHMP）对自主机器人和车辆在共享环境中的安全高效运行至关重要。准确预测可用于运动规划、跟踪、人机交互与安全监控。本文利用动态地图（MoDs），将空间或时空运动模式编码为环境特征，实现长达60秒的LHMP。提出一种受MoD启发的框架，支持多种类型MoDs，并引入排序方法输出最可能轨迹，增强实际应用价值。进一步提出时间条件化MoD，捕捉不同时段的运动规律。在两个真实世界数据集上评估三种类型MoDs的实例化方法。实验表明，基于MoD的方法优于学习型方法，平均位移误差最多降低50%，时间条件化变体整体精度最高。项目代码已公开于https://github.com/test-bai-cpu/LHMP-with-MoDs.git。
原文摘要 · Abstract (English)
Long-term human motion prediction (LHMP) is important for the safe and efficient operation of autonomous robots and vehicles in environments shared with humans. Accurate predictions are important for applications including motion planning, tracking, human-robot interaction, and safety monitoring. In this paper, we exploit Maps of Dynamics (MoDs), which encode spatial or spatio-temporal motion patterns as environment features, to achieve LHMP for horizons of up to 60 seconds. We propose an MoD-informed LHMP framework that supports various types of MoDs and includes a ranking method to output the most likely predicted trajectory, improving practical utility in robotics. Further, a time-conditioned MoD is introduced to capture motion patterns that vary across different times of day. We evaluate MoD-LHMP instantiated with three types of MoDs. Experiments on two real-world datasets show that MoD-informed method outperforms learning-based ones, with up to 50\% improvement in average displacement error, and the time-conditioned variant achieves the highest accuracy overall. Project code is available at https://github.com/test-bai-cpu/LHMP-with-MoDs.git
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