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

Links: arxiv, video, project page, code, bibtex

Abtract

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 ~19.5% and on the ETH/UCY benchmark by ~40.8%.

Video

A shorter summary version is also available:

Bibtex

@inproceedings{mangalam2020journey,
    title={It Is Not the Journey but the Destination: Endpoint Conditioned Trajectory Prediction},
    author={Karttikeya Mangalam and Harshayu Girase and Shreyas Agarwal and
        Kuan-Hui Lee and Ehsan Adeli and Jitendra Malik and Adrien Gaidon},
    booktitle={ECCV},
    year={2020},
}