Interactive trajectory prediction for autonomous driving based on Transformer
<p>Trajectory planning has undergone remarkable strides in recent times, especially in the behavior prediction of traffic participants. Given that strong coupling conditions such as pedestrians, vehicles, and roads restrict the interactive behavior of autonomous vehicles and other traffic part...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2025-02-01
|
| Series: | Mechanical Sciences |
| Online Access: | https://ms.copernicus.org/articles/16/87/2025/ms-16-87-2025.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832086535114063872 |
|---|---|
| author | R. Xu J. Li S. Zhang L. Li H. Li G. Ren X. Tang |
| author_facet | R. Xu J. Li S. Zhang L. Li H. Li G. Ren X. Tang |
| author_sort | R. Xu |
| collection | DOAJ |
| description | <p>Trajectory planning has undergone remarkable strides in recent times, especially in the behavior prediction of traffic participants. Given that strong coupling conditions such as pedestrians, vehicles, and roads restrict the interactive behavior of autonomous vehicles and other traffic participants, it has become critical to design a trajectory prediction algorithm based on traffic scenarios for autonomous-driving technology. In this paper, we propose a novel trajectory prediction algorithm based on Transformer networks, a data-driven method that ingeniously harnesses dual-input channels. The rationale underlying this approach lies in its seamless fusion of scene context modeling and multi-modal prediction within a neural network architecture. At the heart of this innovative framework resides the multi-headed attention mechanism, ingeniously deployed in both the agent attention layer and the scene attention layer. This finessing not only captures the profound interdependence between agents and their surroundings but also imbues the algorithm with a better real-time predictive prowess, enhancing computational efficiency. Eventually, substantial experiments with the Argoverse dataset will demonstrate improved trajectory accuracy, with the minimum average displacement error (MADE) and minimum final displacement error (MFDE) being reduced by 12 % and 31 %, respectively.</p> |
| format | Article |
| id | doaj-art-c05beaa009814c6e917c1b95f2a1d235 |
| institution | Kabale University |
| issn | 2191-9151 2191-916X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Mechanical Sciences |
| spelling | doaj-art-c05beaa009814c6e917c1b95f2a1d2352025-02-06T14:30:15ZengCopernicus PublicationsMechanical Sciences2191-91512191-916X2025-02-0116879710.5194/ms-16-87-2025Interactive trajectory prediction for autonomous driving based on TransformerR. Xu0J. Li1S. Zhang2L. Li3H. Li4G. Ren5X. Tang6School of Mechanotronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074, ChinaSchool of Mechanotronics and Vehicle Engineering, Chongqing Jiaotong University, Chongqing, 400074, ChinaSchool of Shipping and Naval Architecture, Chongqing Jiaotong University, Chongqing, 400074, ChinaInstitute of Agricultural Machinery, Chongqing Academy of Agricultural Sciences, Chongqing, 400074, ChinaSchool of Traffic and Transportation, Chongqing Jiaotong University, Chongqing, 400074, ChinaInstitute of Agricultural Machinery, Chongqing Academy of Agricultural Sciences, Chongqing, 400074, ChinaInstitute of Agricultural Machinery, Chongqing Academy of Agricultural Sciences, Chongqing, 400074, China<p>Trajectory planning has undergone remarkable strides in recent times, especially in the behavior prediction of traffic participants. Given that strong coupling conditions such as pedestrians, vehicles, and roads restrict the interactive behavior of autonomous vehicles and other traffic participants, it has become critical to design a trajectory prediction algorithm based on traffic scenarios for autonomous-driving technology. In this paper, we propose a novel trajectory prediction algorithm based on Transformer networks, a data-driven method that ingeniously harnesses dual-input channels. The rationale underlying this approach lies in its seamless fusion of scene context modeling and multi-modal prediction within a neural network architecture. At the heart of this innovative framework resides the multi-headed attention mechanism, ingeniously deployed in both the agent attention layer and the scene attention layer. This finessing not only captures the profound interdependence between agents and their surroundings but also imbues the algorithm with a better real-time predictive prowess, enhancing computational efficiency. Eventually, substantial experiments with the Argoverse dataset will demonstrate improved trajectory accuracy, with the minimum average displacement error (MADE) and minimum final displacement error (MFDE) being reduced by 12 % and 31 %, respectively.</p>https://ms.copernicus.org/articles/16/87/2025/ms-16-87-2025.pdf |
| spellingShingle | R. Xu J. Li S. Zhang L. Li H. Li G. Ren X. Tang Interactive trajectory prediction for autonomous driving based on Transformer Mechanical Sciences |
| title | Interactive trajectory prediction for autonomous driving based on Transformer |
| title_full | Interactive trajectory prediction for autonomous driving based on Transformer |
| title_fullStr | Interactive trajectory prediction for autonomous driving based on Transformer |
| title_full_unstemmed | Interactive trajectory prediction for autonomous driving based on Transformer |
| title_short | Interactive trajectory prediction for autonomous driving based on Transformer |
| title_sort | interactive trajectory prediction for autonomous driving based on transformer |
| url | https://ms.copernicus.org/articles/16/87/2025/ms-16-87-2025.pdf |
| work_keys_str_mv | AT rxu interactivetrajectorypredictionforautonomousdrivingbasedontransformer AT jli interactivetrajectorypredictionforautonomousdrivingbasedontransformer AT szhang interactivetrajectorypredictionforautonomousdrivingbasedontransformer AT lli interactivetrajectorypredictionforautonomousdrivingbasedontransformer AT hli interactivetrajectorypredictionforautonomousdrivingbasedontransformer AT gren interactivetrajectorypredictionforautonomousdrivingbasedontransformer AT xtang interactivetrajectorypredictionforautonomousdrivingbasedontransformer |