時空間ダイナミクスマップを用いた長期人間動作予測
Long-Term Human Motion Prediction Using Spatio-Temporal Maps of Dynamics
環境の時空間的な動きパターンを符号化したダイナミクスマップを活用し、最大60秒先までの人間の動作を予測する枠組みを提案。実世界データセットで学習ベース手法を上回る精度を達成。
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著者: Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner, Achim J. Lilienthal, Martin Magnusson
分類: cs.RO
原文アブストラクト
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
