異種エージェントの軌道予測：小規模・不均衡データセットにおける性能分析
Trajectory prediction for heterogeneous agents: A performance analysis on small and imbalanced datasets
ロボットや自動運転の軌道予測において、エージェントのクラスラベルを条件に用いる手法を2つのデータセットで比較し、データが少ない・クラス不均衡な状況ではパターンベース手法が優位であることを示した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
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著者: Tiago Rodrigues de Almeida, Yufei Zhu, Andrey Rudenko, Tomasz P. Kucner, Johannes A. Stork, Martin Magnusson, Achim J. Lilienthal
分類: cs.RO, cs.LG
原文アブストラクト
Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently and avoid collisions. The dynamics of those agents strongly depends on their tasks, roles, or observable labels. Class-conditioned motion prediction is thus an appealing way to reduce forecast uncertainty and get more accurate predictions for heterogeneous agents. However, this is hardly explored in the prior art, especially for mobile robots and in limited data applications. In this paper, we analyse different class-conditioned trajectory prediction methods on two datasets. We propose a set of conditional pattern-based and efficient deep learning-based baselines, and evaluate their performance on robotics and outdoors datasets (TH\"OR-MAGNI and Stanford Drone Dataset). Our experiments show that all methods improve accuracy in most of the settings when considering class labels. More importantly, we observe that there are significant differences when learning from imbalanced datasets, or in new environments where sufficient data is not available. In particular, we find that deep learning methods perform better on balanced datasets, but in applications with limited data, e.g., cold start of a robot in a new environment, or imbalanced classes, pattern-based methods may be preferable.
