Transformerベースの行動クラスタリングとデータ駆動型到達可能性解析による歩行者運動予測
Pedestrian Motion Prediction Using Transformer-based Behavior Clustering and Data-Driven Reachability Analysis
Transformerエンコーダと階層的密度クラスタリングで歩行者の多様な行動パターンを自動抽出し、データ駆動型到達可能性解析と組み合わせて将来の歩行軌道を予測する手法を提案した。
著者: Kleio Fragkedaki, Frank J. Jiang, Karl H. Johansson, Jonas Mårtensson
分類: cs.CV, cs.RO, cs.SY, eess.SY
原文アブストラクト
In this work, we present a transformer-based framework for predicting future pedestrian states based on clustered historical trajectory data. In previous studies, researchers propose enhancing pedestrian trajectory predictions by using manually crafted labels to categorize pedestrian behaviors and intentions. However, these approaches often only capture a limited range of pedestrian behaviors and introduce human bias into the predictions. To alleviate the dependency on manually crafted labels, we utilize a transformer encoder coupled with hierarchical density-based clustering to automatically identify diverse behavior patterns, and use these clusters in data-driven reachability analysis. By using a transformer-based approach, we seek to enhance the representation of pedestrian trajectories and uncover characteristics or features that are subsequently used to group trajectories into different "behavior" clusters. We show that these behavior clusters can be used with data-driven reachability analysis, yielding an end-to-end data-driven approach to predicting the future motion of pedestrians. We train and evaluate our approach on a real pedestrian dataset, showcasing its effectiveness in forecasting pedestrian movements.
