It is not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
Aamir Hassan, October 5, 2020, 4:00-5:00pm CDT
This talk will review a recently published paper by Mangalam et al., with the following abstract.
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 diversity and 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%