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

• URL: http://arxiv.org/abs/2311.02835v1
• Date: Mon, 6 Nov 2023 02:46:05 GMT
• Title: Flexible Multi-Generator Model with Fused Spatiotemporal Graph for Trajectory Prediction
• Authors: Peiyuan Zhu, Fengxia Han and Hao Deng
• Abstract summary: Trajectory prediction plays a vital role in automotive radar systems. Generative adversarial networks with the ability to learn a distribution over future trajectories tend to predict out-of-distribution samples. We propose a trajectory prediction framework, which can capture the social interaction and model disconnected variations of pedestrian trajectories.
• Score: 2.1638817206926855
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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