@article{ref1,
title="BRVO: Predicting pedestrian trajectories using velocity-space reasoning",
journal="International journal of robotic research",
year="2015",
author="Kim, Sujeong and Guy, Stephen J. and Liu, Wenxi and Wilkie, David and Lau, Rynson W. H. and Lin, Ming C. and Manocha, Dinesh",
volume="34",
number="2",
pages="201-217",
abstract="We introduce a novel, online method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction and collision-free navigation. Our formulation uses velocity obstacles to model the trajectory of each moving pedestrian in a robot's environment and improves the motion model by adaptively learning relevant parameters based on sensor data. The resulting motion model for each agent is computed using statistical inferencing techniques, including a combination of ensemble Kalman filters and a maximum-likelihood estimation algorithm. This allows a robot to learn individual motion parameters for every agent in the scene at interactive rates. We highlight the performance of our motion prediction method in real-world crowded scenarios, compare its performance with prior techniques, and demonstrate the improved accuracy of the predicted trajectories. We also adapt our approach for collision-free robot navigation among pedestrians based on noisy data and highlight the results in our simulator.<p /> <p>Language: en</p>",
language="en",
issn="0278-3649",
doi="10.1177/0278364914555543",
url="http://dx.doi.org/10.1177/0278364914555543"
}