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

• URL: http://arxiv.org/abs/2004.02025v3
• Date: Sat, 18 Jul 2020 21:33:55 GMT
• Title: It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction
• Authors: Karttikeya Mangalam, Harshayu Girase, Shreyas Agarwal, Kuan-Hui Lee, Ehsan Adeli, Jitendra Malik, Adrien Gaidon
• Abstract summary: We present Predicted Conditioned Network (PECNet) for flexible human trajectory prediction. PECNet infers distant endpoints to assist in long-range multi-modal trajectory prediction. 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%.
• Score: 59.027152973975575
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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/

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