It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction

Code

• github.com/HarshayuGirase/PECNet
  Officialpytorch★ 405
• github.com/harshayugirase/human-path-prediction
  pytorch★ 405
• github.com/inhwanbae/npsn
  pytorch★ 73
• github.com/zhanwei-z/g2ltraj
  pytorch★ 19

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 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: https://karttikeya.github.io/publication/htf/

Tasks

Benchmark Results

Reproductions