  Pedestrian trajectory prediction is a critical yet challenging task,
especially for crowded scenes. We suggest that introducing an attention
mechanism to infer the importance of different neighbors is critical for
accurate trajectory prediction in scenes with varying crowd size. In this work,
we propose a novel method, AVGCN, for trajectory prediction utilizing graph
convolutional networks (GCN) based on human attention (A denotes attention, V
denotes visual field constraints). First, we train an attention network that
estimates the importance of neighboring pedestrians, using gaze data collected
as subjects perform a bird's eye view crowd navigation task. Then, we
incorporate the learned attention weights modulated by constraints on the
pedestrian's visual field into a trajectory prediction network that uses a GCN
to aggregate information from neighbors efficiently. AVGCN also considers the
stochastic nature of pedestrian trajectories by taking advantage of variational
trajectory prediction. Our approach achieves state-of-the-art performance on
several trajectory prediction benchmarks, and the lowest average prediction
error over all considered benchmarks.
