DATraj: A Dynamic Graph Attention Based Model for Social-Aware Pedestrian Trajectory Prediction

Accurately forecasting the future paths of numerous agents is vital for the efficacy of autonomous systems. In crowded scenarios such as sidewalks, subways and airports, pedestrians instinctively modify their motion pattern in response to the environmental context and social consensus like preserving personal space and circumventing physical contact. Thus, the task to predict future pedestrian trajectory presents considerable challenges owing to the complex interaction among agents and the inherent uncertainty in predicting each agent's subsequent actions. Inspired by the recent success of Graph Neural Networks (GNN), a model named DATraj is introduced for predicting pedestrian trajectory. DATraj first uses a temporal encoder composed of attention mechanism to capture the spatial-temporal dynamics of pedestrians. The encoder can learn the motion pattern and subtle movement of pedestrian in the crowded scenario. Graph Attention Networks (GAT) is used in many models to catch social interaction between individuals. However common GATs compute a static attention: the ranking of the attention scores is unconditioned on the query node. DATraj implement the global interaction parts using the improved dynamic attention which every query uniquely prioritizes the attention coefficients correlated with the keys, this provides a much better robustness to noise. Experiments show that our trajectory prediction model achieves better performance on several public datasets.