  Trajectory prediction plays a vital role in automotive radar systems,
facilitating precise tracking and decision-making in autonomous driving.
Generative adversarial networks with the ability to learn a distribution over
future trajectories tend to predict out-of-distribution samples, which
typically occurs when the distribution of forthcoming paths comprises a blend
of various manifolds that may be disconnected. To address this issue, we
propose a trajectory prediction framework, which can capture the social
interaction variations and model disconnected manifolds of pedestrian
trajectories. Our framework is based on a fused spatiotemporal graph to better
model the complex interactions of pedestrians in a scene, and a multi-generator
architecture that incorporates a flexible generator selector network on
generated trajectories to learn a distribution over multiple generators. We
show that our framework achieves state-of-the-art performance compared with
several baselines on different challenging datasets.
