Channel Spatio-Temporal Convolutional Network for Trajectory Prediction

Accurate and timely prediction of the future path of agents in the vicinity of an agent is the core of avoiding conflict in automated applications. The traditional method based on RNN model requires high computational cost in the process of prediction, especially for long series prediction. In order to obtain more efficient and accurate prediction trajectory, a channel spatio-temporal convolutional network framework, called CSTCN, is proposed in this paper. The framework models the spatial environment as a block of data input to the CSTCN and captures spatio-temporal interactions using an improved temporal convolutional network. Compared with the traditional model, the spatial and temporal modeling of the proposed model is calculated in each local time window so that it can be executed in parallel to obtain higher computational efficiency. Experimental results on 5 trajectory prediction benchmark datasets demonstrate that the proposed model is superior to other seven state-of-the-art models in both efficiency and accuracy.