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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Predicting Pedestrian Trajectories Using Velocity-Space Reasoning
verfasst von : Sujeong Kim, Stephen J. Guy, Wenxi Liu, Rynson W. H. Lau, Ming C. Lin, Dinesh Manocha
Erschienen in: Algorithmic Foundations of Robotics X
Verlag: Springer Berlin Heidelberg
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We introduce a novel method to predict pedestrian trajectories using agent-based velocity-space reasoning for improved human-robot interaction. This formulation models the trajectory of each moving pedestrian in a robot’s environment using velocity obstacles and learns the simulation parameters based on tracked data. The resulting motion model for each agent is computed using statistical inferencing techniques from noisy data. This includes the combination of Ensemble Kalman filters and maximum likelihood estimation algorithm to learn individual motion parameters at interactive rates. We highlight the performance of our motion model in real-world crowded scenarios. We also compare its performance with prior techniques and demonstrate improved accuracy in the predicted trajectories.