BRVO: Predicting pedestrian trajectories using velocity-space reasoning

Sujeong Kim, Stephen J. Guy, Wenxi Liu, David Wilkie, Rynson W.H. Lau, Ming C. Lin, Dinesh Manocha

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)201-217
Number of pages17
JournalInternational Journal of Robotics Research
Volume34
Issue number2
DOIs
StatePublished - Mar 3 2015

Bibliographical note

Funding Information:
This research is supported in part by the ARO (contract W911NF-10-1-0506), the NSF (awards 0917040, 0904990, 100057 and 1117127) and Intel.

Keywords

  • Collision avoidance
  • Multi-agent simulation
  • Trajectory prediction

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