We observe that the human trajectory is not only forward predictable, but also backward predictable. Both forward and backward trajectories follow the same social norms and obey the same physical constraints with the only difference in their time directions. Based on this unique property, we develop a new approach, called reciprocal learning, for human trajectory prediction. Two networks, forward and backward prediction networks, are tightly coupled, satisfying the reciprocal constraint, which allows them to be jointly learned. Based on this constraint, we borrow the concept of adversarial attacks of deep neural networks, which iteratively modifies the input of the network to match the given or forced network output, and develop a new method for network prediction, called reciprocal attack for matched prediction. It further improves the prediction accuracy. Our experimental results on benchmark datasets demonstrate that our new method outperforms the state-of-the-art methods for human trajectory prediction.

本文提出了一种名为“对称学习”的方法，利用人类轨迹的前向和后向可预测性，开发了一种新的人类轨迹预测方法，利用正反两个网络进行联合学习。同时，借鉴了深度神经网络的对抗攻击思想，设计了一种新的“对称攻击”方法，能够进一步提高预测精度。实验结果表明，该方法优于现有的人类轨迹预测方法。

人类轨迹预测的循环学习网络