Real-Time Certified Probabilistic Pedestrian Forecasting
Henry O. Jacobs, Owen K. Hughes, Matthew Johnson-Roberson, Ram Vasudevan
Abstract
The success of autonomous systems will depend upon their ability to safely navigate human-centric environments. This motivates the need for a real-time, probabilistic forecasting algorithm for pedestrians, cyclists, and other agents since these predictions will form a necessary step in assessing the risk of any action. This letter presents a novel approach to probabilistic forecasting for pedestrians based on weighted sums of ordinary differential equations that are learned from historical trajectory information within a fixed scene. The resulting algorithm is embarrassingly parallel and is able to work at real-time speeds using a naive Python implementation. The quality of predicted locations of agents generated by the proposed algorithm is validated on a variety of examples and is considerably higher than existing state of the art approaches over long time horizons.
BibTeX
@inproceedings{ral2017_realtimecertifie,
title = {Real-Time Certified Probabilistic Pedestrian Forecasting},
author = {Henry O. Jacobs and Owen K. Hughes and Matthew Johnson-Roberson and Ram Vasudevan},
booktitle = {RA-L 2017},
year = {2017}
}