Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866913058602876928 |
|---|---|
| author | Finet, Céline Martins, Stephane Da Silva Hayet, Jean-Bernard Karamouzas, Ioannis Amirian, Javad Hégarat-Mascle, Sylvie Le Pettré, Julien Aldea, Emanuel |
| author_facet | Finet, Céline Martins, Stephane Da Silva Hayet, Jean-Bernard Karamouzas, Ioannis Amirian, Javad Hégarat-Mascle, Sylvie Le Pettré, Julien Aldea, Emanuel |
| contents | With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions lies within hand's reach, with important implications in areas such as social robot navigation, autonomous driving, and crowd modeling. This survey reviews some of the most recent advancements in deep learning-based multi-agent trajectory prediction, focusing on studies published between 2020 and 2025. We categorize the existing methods based on their architectural design, their input representations, and their overall prediction strategies, placing a particular emphasis on models evaluated using the ETH/UCY benchmark. Furthermore, we highlight key challenges and future research directions in the field of multi-agent HTP. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review Finet, Céline Martins, Stephane Da Silva Hayet, Jean-Bernard Karamouzas, Ioannis Amirian, Javad Hégarat-Mascle, Sylvie Le Pettré, Julien Aldea, Emanuel Computer Vision and Pattern Recognition Machine Learning Robotics With the emergence of powerful data-driven methods in human trajectory prediction (HTP), gaining a finer understanding of multi-agent interactions lies within hand's reach, with important implications in areas such as social robot navigation, autonomous driving, and crowd modeling. This survey reviews some of the most recent advancements in deep learning-based multi-agent trajectory prediction, focusing on studies published between 2020 and 2025. We categorize the existing methods based on their architectural design, their input representations, and their overall prediction strategies, placing a particular emphasis on models evaluated using the ETH/UCY benchmark. Furthermore, we highlight key challenges and future research directions in the field of multi-agent HTP. |
| title | Recent Advances in Multi-Agent Human Trajectory Prediction: A Comprehensive Review |
| topic | Computer Vision and Pattern Recognition Machine Learning Robotics |
| url | https://arxiv.org/abs/2506.14831 |