  An effective understanding of the contextual environment and accurate motion
forecasting of surrounding agents is crucial for the development of autonomous
vehicles and social mobile robots. This task is challenging since the behavior
of an autonomous agent is not only affected by its own intention, but also by
the static environment and surrounding dynamically interacting agents. Previous
works focused on utilizing the spatial and temporal information in time domain
while not sufficiently taking advantage of the cues in frequency domain. To
this end, we propose a Spectral Temporal Graph Neural Network (SpecTGNN), which
can capture inter-agent correlations and temporal dependency simultaneously in
frequency domain in addition to time domain. SpecTGNN operates on both an agent
graph with dynamic state information and an environment graph with the features
extracted from context images in two streams. The model integrates graph
Fourier transform, spectral graph convolution and temporal gated convolution to
encode history information and forecast future trajectories. Moreover, we
incorporate a multi-head spatio-temporal attention mechanism to mitigate the
effect of error propagation in a long time horizon. We demonstrate the
performance of SpecTGNN on two public trajectory prediction benchmark datasets,
which achieves state-of-the-art performance in terms of prediction accuracy.
