Pedestrian trajectory prediction is essential for developing safe autonomous driving systems. Such trajectories depend on various contextual cues, among which surrounding objects.

This work proposes the first pedestrian trajectory prediction method in the 2D on-board domain that models interactions between the pedestrian and surrounding static- and dynamic- contextual objects using a graph-based approach. Our two-stream model separately encodes past motion history and interactions. The encoded information from both streams is fused and decoded to generate future pedestrian trajectories. The interactions are modeled using spatial graphs, which are temporally connected using a Gated Recurrent Unit. The graph nodes represent the pedestrian and contextual objects, and the edges represent the interaction importance between nodes.

In experiments on the PIE and JAAD_full dataset, it is shown that our graph-based interaction-aware trajectory prediction method outperforms all considered baselines on nearly all metrics. Moreover, the performance gain on JAAD_full is most significant for the close-by pedestrians. Finally, modeling the interactions with all considered contextual objects, i.e. vehicles, crosswalks, and traffic lights, improves trajectory prediction performance most compared to only using a subset of these objects.