The DOI matches its Crossref record; 96 of the 96 references it cites were found in a registry or an index.
Human Trajectory Forecasting in Crowds: A Deep Learning Perspective
Open accessAbstract
Since the past few decades, human trajectory forecasting has been a field of active research owing to its numerous real-world applications: evacuation situation analysis, deployment of intelligent transport systems, traffic operations, to name a few. In this work, we cast the problem of human trajectory forecasting as learning a representation of human social interactions. Early works handcrafted this representation based on domain knowledge. However, social interactions in crowded environments are not only diverse but often subtle. Recently, deep learning methods have outperformed their handcrafted counterparts, as they learn about human-human interactions in a more generic data-driven fashion. In this work, we present an in-depth analysis of existing deep learning-based methods for modelling social interactions. We propose two domain-knowledge inspired data-driven methods to effectively capture these social interactions. To objectively compare the performance of these interaction-based forecasting models, we develop a large scale interaction-centric benchmarkTrajNet++, a significant yet missing component in the field of human trajectory forecasting. We propose novel performance metrics that evaluate the ability of a model to output socially acceptable trajectories. Experiments on TrajNet++ validate the need for our proposed metrics, and our method outperforms competitive baselines on both real-world and synthetic datasets.
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On Estimation of a Probability Density Function and Mode
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Cao, J., Pang, J., Weng, X., Khirodkar, R., & Kitani, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00934 · Cited by 1,115
Principles and Guidelines for Evaluating Social Robot Navigation Algorithms
Francis, A., Pérez-D’Arpino, C., Li, C., Xia, F., Alahi, A., Alami, R., Bera, A., Biswas, A., Biswas, J., Chandra, R., et al. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3700599 · Cited by 107
Self-citationDetecting 32 Pedestrian Attributes for Autonomous Vehicles
Mordan, T., Cord, M., Perez, P., & Alahi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3107587 · Cited by 36
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Metadata
- Authors
- Kothari, P., Kreiss, S., & Alahi, A.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-07
- Volume
- 23
- Issue
- 7
- Pages
- 7386-7400
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3038351891
- Cited by
- 267
- References
- 96
- FWCI
- 24.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
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