BRVO: Predicting pedestrian trajectories using velocity-space reasoning. (February 2015)

• Record Type: Journal Article
• Authors: Kim, Sujeong; Guy, Stephen J.; Liu, Wenxi; Wilkie, David; Lau, Rynson W.H.; Lin, Ming C.; Manocha, Dinesh
• Is Part Of: International journal of robotics research. Volume 34:Number 2(2015:Feb.)
• Page Start: 201
• Page End: 217
• Publication Date: 2015-02
• Subjects: Trajectory prediction -- multi-agent simulation -- collision avoidance; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364914555543
• Languages: English
• ISSNs: 0278-3649

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.