環境予測のための注意機構拡張ConvLSTM
Attention Augmented ConvLSTM for Environment Prediction
占有グリッドの時空間予測において、移動物体のぼやけや消失を抑えるため、時間注意機構と自己注意機構をConvLSTMに組み込んだ2つの手法を提案し、KITTIとWaymoで性能向上を示した。
著者: Bernard Lange, Masha Itkina, Mykel J. Kochenderfer
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Safe and proactive planning in robotic systems generally requires accurate predictions of the environment. Prior work on environment prediction applied video frame prediction techniques to bird's-eye view environment representations, such as occupancy grids. ConvLSTM-based frameworks used previously often result in significant blurring and vanishing of moving objects, thus hindering their applicability for use in safety-critical applications. In this work, we propose two extensions to the ConvLSTM to address these issues. We present the Temporal Attention Augmented ConvLSTM (TAAConvLSTM) and Self-Attention Augmented ConvLSTM (SAAConvLSTM) frameworks for spatiotemporal occupancy prediction, and demonstrate improved performance over baseline architectures on the real-world KITTI and Waymo datasets.
