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      Human motion trajectory prediction: a survey

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          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.

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          On Information and Sufficiency

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            Are we ready for autonomous driving? The KITTI vision benchmark suite

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              Sampling-based algorithms for optimal motion planning

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                The International Journal of Robotics Research
                The International Journal of Robotics Research
                SAGE Publications
                0278-3649
                1741-3176
                July 2020
                June 07 2020
                July 2020
                : 39
                : 8
                : 895-935
                Affiliations
                [1 ]Robert Bosch GmbH, Corporate Research, Germany
                [2 ]Mobile Robotics and Olfaction Lab, Örebro University, Sweden
                [3 ]Bosch Center for Artificial Intelligence, Germany
                [4 ]Carnegie Mellon University, USA
                [5 ]Intelligent Vehicles group, TU Delft, The Netherlands
                Article
                10.1177/0278364920917446
                fbc2b737-cdec-40bf-aecc-ec238ec175e5
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

                History

                Quantitative & Systems biology,Biophysics
                Quantitative & Systems biology, Biophysics

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