Paper

Technical Report for Real-Time Certified Probabilistic Pedestrian Forecasting

Jacobs, Henry O, Johnson-Roberson, Matthew, Vasudevan, Ram, Hughes, Owen K

2017

Overview

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 paper 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 considerably higher than existing state of the art approaches over long time horizons.

• Publisher: Cornell University Library, arXiv.org
• Subject: Algorithms / Differential equations / Forecasting / Ordinary differential equations / Pedestrians / Predictions / Real time