The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting
Open accessAbstract
Predicting accurate future trajectories of multiple agents is essential for autonomous systems but is challenging due to the complex interaction between agents and the uncertainty in each agent’s future behavior. Forecasting multi-agent trajectories requires modeling two key dimensions: (1) time dimension, where we model the influence of past agent states over future states; (2) social dimension, where we model how the state of each agent affects others. Most prior methods model these two dimensions separately, e.g., first using a temporal model to summarize features over time for each agent independently and then modeling the interaction of the summarized features with a social model. This approach is suboptimal since independent feature encoding over either the time or social dimension can result in a loss of information. Instead, we would prefer a method that allows an agent’s state at one time to directly affect another agent’s state at a future time. To this end, we propose a new Transformer, termed AgentFormer, that simultaneously models the time and social dimensions. The model leverages a sequence representation of multi-agent trajectories by flattening trajectory features across time and agents. Since standard attention operations disregard the agent identity of each element in the sequence, AgentFormer uses a novel agent-aware attention mechanism that preserves agent identities by attending to elements of the same agent differently than elements of other agents. Based on AgentFormer, we propose a stochastic multi-agent trajectory prediction model that can attend to features of any agent at any previous timestep when inferring an agent’s future position. The latent intent of all agents is also jointly modeled, allowing the stochasticity in one agent’s behavior to affect other agents. Extensive experiments show that our method significantly improves the state of the art on well-established pedestrian and autonomous driving datasets.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Cited by
Planning-oriented Autonomous Driving
Hu, Y., Yang, J., Chen, L., Li, K., Sima, C., Zhu, X., Chai, S., Du, S., Lin, T., Wang, W., et al. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.01712 · Cited by 931
From Goals, Waypoints & Paths To Long Term Human Trajectory Forecasting
Mangalam, K., An, Y., Girase, H., & Malik, J. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.01495 · Cited by 281
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-citationScene Transformer: A unified architecture for predicting multiple agent trajectories
Ngiam, J., Caine, B., Vasudevan, V., Zhang, Z., Chiang, H.-T. L., Ling, J., Roelofs, R., Bewley, A., Liu, C., Venugopal, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.08417 · Cited by 84
Metadata
- Authors
- Yuan, Y., Weng, X., Ou, Y., & Kitani, K.
- Venue
- 2021 IEEE/CVF International Conference on Computer Vision (ICCV)
- Publisher
- IEEE
- Published
- 2021-10
- Pages
- 9793-9803
- OpenAlex
- W3139491754
- Cited by
- 631
- References
- 64
- FWCI
- 87.18
- Type
- conference paper
- Language
- en
- Open access
- green
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- Yuan, Y., Weng, X., Ou, Y., & Kitani, K. (2021). AgentFormer: Agent-Aware Transformers for Socio-Temporal Multi-Agent Forecasting. 2021 IEEE/CVF International Conference on Computer Vision (ICCV). https://doi.org/10.1109/iccv48922.2021.00967
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