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

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12347)

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 \({\sim }20.9\%\) and on the ETH/UCY benchmark by \({\sim }40.8\%\) (Code available at project homepage: https://karttikeya.github.io/publication/htf/).

Keywords

Multimodal trajectory prediction Social pooling 

Supplementary material

504434_1_En_45_MOESM1_ESM.pptx (24.7 mb)
Supplementary material 1 (pptx 25268 KB)

Supplementary material 2 (mp4 1288 KB)

Supplementary material 3 (mp4 539 KB)

504434_1_En_45_MOESM4_ESM.pdf (23.5 mb)
Supplementary material 4 (pdf 24093 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of CaliforniaBerkeleyUSA
  2. 2.Toyota Research InstituteAnn ArborUSA
  3. 3.Stanford UniversityStanfordUSA

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