動力学マップを用いたデータ効率の良い長期人間運動予測
A Data-Efficient Approach for Long-Term Human Motion Prediction Using Maps of Dynamics
動力学マップ（MoD）を用いて、限られた学習データから最大60秒先までの人間の移動軌跡を予測する手法を提案し、LSTMベースラインと比較して精度と環境の意味的性質の反映を評価した。
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
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.
