论文标题
注意环境预测的弯曲
Attention Augmented ConvLSTM for Environment Prediction
论文作者
论文摘要
机器人系统中的安全和主动计划通常需要对环境的准确预测。对环境预测的先前工作应用了视频框架预测技术，以查看环境表示，例如占用网格。以前使用的基于Convlstm的框架通常会导致移动物体的显着模糊和消失，从而阻碍其适用于安全至关重要的应用。在这项工作中，我们提出了两次扩展，以解决这些问题。我们介绍了暂时的注意增强Convlstm（Taaconvlstm）和自我注意力的增强Convlstm（Saaconvlstm）框架，以进行时空占用率预测，并在现实Wordi Kitti和Waymo Patatasets的基线体系结构上表现出改善的性能。
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.
