Human trajectory forecasting with multiple socially interacting agents is of critical importance for autonomous navigation in human environments, e.g., for self-driving cars and social robots. In this work, we present Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant trajectory endpoints to assist in long-range multi-modal trajectory prediction. A novel nonlocal social pooling layer enables PECNet to infer diverse yet socially compliant trajectories. Additionally, we present a simple "truncationtrick" for improving few-shot multi-modal trajectory prediction performance. We show that PECNet improves state-of-the-art performance on the Stanford Drone trajectory prediction benchmark by ∼ 19.5% and on the ETH/UCY benchmark by ∼ 40.8%.
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