TY  - JOUR
PY  - 2015//
TI  - BRVO: Predicting pedestrian trajectories using velocity-space reasoning
JO  - International journal of robotic research
A1  - Kim, Sujeong
A1  - Guy, Stephen J.
A1  - Liu, Wenxi
A1  - Wilkie, David
A1  - Lau, Rynson W. H.
A1  - Lin, Ming C.
A1  - Manocha, Dinesh
SP  - 201
EP  - 217
VL  - 34
IS  - 2
N2  - 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>
LA  - en
SN  - 0278-3649
UR  - http://dx.doi.org/10.1177/0278364914555543
ID  - ref1
ER  -