交差点における歩行者軌道予測のための運動プリミティブの逐次学習
Incremental Learning of Motion Primitives for Pedestrian Trajectory Prediction at Intersections
歩行者の軌道を運動プリミティブに分割し、新データが得られるたびに類似プリミティブを統合しつつ新規パターンを追加する逐次学習法を提案。単一・複数交差点の実験でオフライン学習より予測誤差を低減し、モデルサイズの増加も抑えた。
著者: Golnaz Habibi, Nikita Japuria, Jonathan P. How
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
This paper presents a novel incremental learning algorithm for pedestrian motion prediction, with the ability to improve the learned model over time when data is incrementally available. In this setup, trajectories are modeled as simple segments called motion primitives. Transitions between motion primitives are modeled as Gaussian Processes. When new data is available, the motion primitives learned from the new data are compared with the previous ones by measuring the inner product of the motion primitive vectors. Similar motion primitives and transitions are fused and novel motion primitives are added to capture newly observed behaviors. The proposed approach is tested and compared with other baselines in intersection scenarios where the data is incrementally available either from a single intersection or from multiple intersections with different geometries. In both cases, our method incrementally learns motion patterns and outperforms the offline learning approach in terms of prediction errors. The results also show that the model size in our algorithm grows at a much lower rate than standard incremental learning, where newly learned motion primitives and transitions are simply accumulated over time.
