@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 robotí»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} }