论文标题
空间 - 周期性块和LSTM网络，用于行人轨迹预测
Spatial-Temporal Block and LSTM Network for Pedestrian Trajectories Prediction
论文作者
论文摘要
行人轨迹预测对于避免自主驾驶碰撞至关重要。但是，由于社会力量和混乱的场景，这一预测是一个具有挑战性的问题。这种人类和人类空间相互作用导致许多社会合理的轨迹。在本文中，我们提出了一种基于LSTM的新型算法。我们通过考虑将图形卷积网络和时间卷积网络结合起来以从行人中提取特征的静态场景和行人来解决问题。场景中的每个行人都被视为节点，我们可以通过图形嵌入获得每个节点及其邻域之间的关系。正是LSTM编码关系，以便我们的模型同时预测人群场景中的节点轨迹。为了有效预测多个可能的未来轨迹，我们进一步引入了时空卷积块，以使网络灵活。在两个公共数据集（即ETH和UCY）上的实验结果证明了我们提出的ST块的有效性，我们在人类轨迹预测中实现了最新的方法。
Pedestrian trajectory prediction is a critical to avoid autonomous driving collision. But this prediction is a challenging problem due to social forces and cluttered scenes. Such human-human and human-space interactions lead to many socially plausible trajectories. In this paper, we propose a novel LSTM-based algorithm. We tackle the problem by considering the static scene and pedestrian which combine the Graph Convolutional Networks and Temporal Convolutional Networks to extract features from pedestrians. Each pedestrian in the scene is regarded as a node, and we can obtain the relationship between each node and its neighborhoods by graph embedding. It is LSTM that encode the relationship so that our model predicts nodes trajectories in crowd scenarios simultaneously. To effectively predict multiple possible future trajectories, we further introduce Spatio-Temporal Convolutional Block to make the network flexible. Experimental results on two public datasets, i.e. ETH and UCY, demonstrate the effectiveness of our proposed ST-Block and we achieve state-of-the-art approaches in human trajectory prediction.
