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.

翻译：准确和及时地预测代理人邻居的未来路径是自动应用避免碰撞的核心。常规方法,例如基于LSTM的模型,在预测中花费大量计算成本,特别是长序列预测。为了支持更高效和准确的轨迹预测,我们提议了一个新型的CNN空间时空图框架GreaphTCN,该模型将空间互动模拟为社会图表,并捕捉与经修改的时空脉动网络的时空相互作用。与传统模型不同,我们模型的空间和时空模型都是在每一个当地时间窗口中计算。因此,可以同时进行,效率要高得多,同时与最佳方法的准确性相匹配。实验结果证实,与各种轨迹预测基准数据集的最新模型相比,我们的模型在效率和准确性两方面都取得了更好的业绩。