Print Email Facebook Twitter Human motion trajectory prediction Title Human motion trajectory prediction: a survey Author Rudenko, Andrey (Robert Bosch GmbH; Orebro University) Palmieri, Luigi (Robert Bosch GmbH) Herman, Michael (Bosch Center for Artificial Intelligence) Kitani, Kris M. (Carnegie Mellon University) Gavrila, D. (TU Delft Intelligent Vehicles) Arras, Kai O. (Robert Bosch GmbH) Date 2020 Abstract With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand, and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots, and advanced surveillance systems. This article provides a survey of human motion trajectory prediction. We review, analyze, and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research. Subject autonomous drivingmotion predictionreviewroboticsSurveyvideo surveillance To reference this document use: http://resolver.tudelft.nl/uuid:25219210-dad0-4ccf-a029-21dbc0ab8df4 DOI https://doi.org/10.1177/0278364920917446 ISSN 0278-3649 Source The International Journal of Robotics Research, 39 (8), 895-935 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type review Rights © 2020 Andrey Rudenko, Luigi Palmieri, Michael Herman, Kris M. Kitani, D. Gavrila, Kai O. Arras Files PDF rudenko2019_human_motion_ ... iction.pdf 2.87 MB Close viewer /islandora/object/uuid:25219210-dad0-4ccf-a029-21dbc0ab8df4/datastream/OBJ/view