Flexible Multi-Generator Model with Fused Spatiotemporal Graph for Trajectory Prediction
This addresses trajectory prediction for autonomous driving, but it appears incremental as it builds on existing generative adversarial network methods.
The paper tackles the problem of predicting out-of-distribution samples in pedestrian trajectory prediction by proposing a framework that models disconnected manifolds and social interactions, achieving state-of-the-art performance on challenging datasets.
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.