@article{Kim01022015,
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}, 
title = {BRVO: Predicting pedestrian trajectories using velocity-space reasoning},
volume = {34}, 
number = {2}, 
pages = {201-217}, 
year = {2015}, 
doi = {10.1177/0278364914555543}, 
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.}, 
URL = {http://ijr.sagepub.com/content/34/2/201.abstract}, 
eprint = {http://ijr.sagepub.com/content/34/2/201.full.pdf+html}, 
journal = {The International Journal of Robotics Research} 
}