GraphTCN: Spatio-Temporal Interaction Modeling for Human Trajectory Prediction
This work addresses trajectory prediction for autonomous systems, offering incremental improvements in efficiency and accuracy over existing methods.
The paper tackles the problem of predicting human trajectories for autonomous collision avoidance by proposing GraphTCN, a CNN-based spatial-temporal graph framework that models interactions within local time windows. The model achieves better efficiency and accuracy compared to state-of-the-art methods on benchmark datasets.
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.