The DOI matches its Crossref record; 190 of the 190 references it cites were found in a registry or an index.
Human motion trajectory prediction: a survey
Open accessAbstract
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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A Survey on Trajectory-Prediction Methods for Autonomous Driving
Huang, Y., Du, J., Yang, Z., Zhou, Z., Zhang, L., & Chen, H. (2022) · IEEE Transactions on Intelligent Vehicles · doi:10.1109/tiv.2022.3167103 · Cited by 677
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Yuan, Y., Weng, X., Ou, Y., & Kitani, K. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00967 · Cited by 631
Self-citationTransformer Networks for Trajectory Forecasting
Giuliari, F., Hasan, I., Cristani, M., & Galasso, F. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412190 · Cited by 572
Trace and Pace: Controllable Pedestrian Animation via Guided Trajectory Diffusion
Rempe, D., Luo, Z., Peng, X. B., Yuan, Y., Kitani, K., Kreis, K., Fidler, S., & Litany, O. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01322 · Cited by 97
Self-citationSEAN 2.0: Formalizing and Generating Social Situations for Robot Navigation
Tsoi, N., Xiang, A., Yu, P., Sohn, S. S., Schwartz, G., Ramesh, S., Hussein, M., Gupta, A. W., Kapadia, M., & Vazquez, M. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2022.3196783 · Cited by 44
Metadata
- Authors
- Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2020-06-07
- Volume
- 39
- Issue
- 8
- Pages
- 895-935
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W3033920763
- Cited by
- 658
- References
- 190
- FWCI
- 34.05
- Type
- journal article
- Language
- en
- Open access
- green
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