This work presents Predicted Endpoint Conditioned Network (PECNet) for flexible human trajectory prediction, which improves state-of-the-art performance on the Stanford Drone trajectory prediction benchmark and on the ETH/UCY benchmark by ~40.8%.
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 non-local social pooling layer enables PECNet to infer diverse yet socially compliant trajectories. Additionally, we present a simple "truncation-trick" 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 ~20.9% and on the ETH/UCY benchmark by ~40.8%. Project homepage: this https URL
Ehsan Adeli
3 papers
Harshayu Girase
1 papers
Shreya Agarwal
1 papers
Kuan-Hui Lee
1 papers