论文标题
GraphTCN：人类轨迹预测的时空相互作用建模
GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction
论文作者
论文摘要
准确和及时地预测代理人邻居的未来路径至关重要。常规方法，例如基于LSTM的模型，在预测中采取相当大的计算成本，尤其是对于长序列预测。为了支持更有效，更准确的轨迹预测，我们提出了一种基于CNN的新型时空图形框架GraphTCN，该图形将空间相互作用模拟为社交图，并捕获了与修改的时间卷积网络的时空相互作用。与常规模型相反，我们模型的空间和时间建模均在每个本地时间窗口中计算。因此，它可以以更高的效率并行执行，同时准确性与表现最好的方法相当。实验结果证实，与各种轨迹预测基准数据集中的最先进模型相比，我们的模型在效率和准确性方面都可以提高性能。
Predicting the future paths of an agent's neighbors accurately and in a timely manner is central to the autonomous applications for collision avoidance. Conventional approaches, e.g., LSTM-based models, take considerable computational costs in the prediction, especially for the long sequence prediction. To support more efficient and accurate trajectory predictions, we propose a novel CNN-based spatial-temporal graph framework GraphTCN, which models the spatial interactions as social graphs and captures the spatio-temporal interactions with a modified temporal convolutional network. In contrast to conventional models, both the spatial and temporal modeling of our model are computed within each local time window. Therefore, it can be executed in parallel for much higher efficiency, and meanwhile with accuracy comparable to best-performing approaches. Experimental results confirm that our model achieves better performance in terms of both efficiency and accuracy as compared with state-of-the-art models on various trajectory prediction benchmark datasets.
