The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Multi-Agent Tensor Fusion for Contextual Trajectory Prediction
Open accessAbstract
Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.
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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
Human motion trajectory prediction: a survey
Rudenko, A., Palmieri, L., Herman, M., Kitani, K. M., Gavrila, D. M., & Arras, K. O. (2020) · The International Journal of Robotics Research · doi:10.1177/0278364920917446 · Cited by 658
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
CoverNet: Multimodal Behavior Prediction Using Trajectory Sets
Phan-Minh, T., Grigore, E. C., Boulton, F. A., Beijbom, O., & Wolff, E. M. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01408 · Cited by 438
Metadata
- Authors
- Zhao, T., Xu, Y., Monfort, M., Choi, W., Baker, C., Zhao, Y., Wang, Y., & Wu, Y. N.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 12118-12126
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2940129212
- Cited by
- 477
- References
- 25
- FWCI
- 58.66
- Type
- conference paper
- Language
- en
- Open access
- green
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- Zhao, T., Xu, Y., Monfort, M., Choi, W., Baker, C., Zhao, Y., Wang, Y., & Wu, Y. N. (2019). Multi-Agent Tensor Fusion for Contextual Trajectory Prediction. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.01240
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