  Accurately forecasting the future movements of surrounding vehicles is
essential for safe and efficient operations of autonomous driving cars. This
task is difficult because a vehicle's moving trajectory is greatly determined
by its driver's intention, which is often hard to estimate. By leveraging
attention mechanisms along with long short-term memory (LSTM) networks, this
work learns the relation between a driver's intention and the vehicle's
changing positions relative to road infrastructures, and uses it to guide the
prediction. Different from other state-of-the-art solutions, our work treats
the on-road lanes as non-Euclidean structures, unfolds the vehicle's moving
history to form a spatio-temporal graph, and uses methods from Graph Neural
Networks to solve the problem. Not only is our approach a pioneering attempt in
using non-Euclidean methods to process static environmental features around a
predicted object, our model also outperforms other state-of-the-art models in
several metrics. The practicability and interpretability analysis of the model
shows great potential for large-scale deployment in various autonomous driving
systems in addition to our own.
