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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 overfuture trajectories tend to predict out-of-distribution samples, whichtypically occurs when the distribution of forthcoming paths comprises a blendof various manifolds that may be disconnected. To address this issue, wepropose a trajectory prediction framework, which can capture the socialinteraction variations and model disconnected manifolds of pedestriantrajectories. Our framework is based on a fused spatiotemporal graph to bettermodel the complex interactions of pedestrians in a scene, and a multi-generatorarchitecture that incorporates a flexible generator selector network ongenerated trajectories to learn a distribution over multiple generators. Weshow that our framework achieves state-of-the-art performance compared withseveral baselines on different challenging datasets.

Flexible Multi-Generator Model with Fused Spatiotemporal Graph for Trajectory Prediction