Conference item
Ensemble of pre-trained models for long-tailed trajectory prediction
- Abstract:
-
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. We show how, perhaps surprisingly, combining state-of-the-art deep learning models out-of-the-box (without retraining or finetuning) with a simple weighted average method can enhance the overall prediction. Indeed, while in general combining trajectory prediction models is not straightforward, this simple approach enhances performance by 10% over the best prediction model, especially in the long-tailed metrics. We show that this performance improvement holds on both the NuScenes and Argoverse datasets, and that these improvements are made across the dataset distribution.
- Publication status:
- Accepted
- Peer review status:
- Peer reviewed
Actions
Authors
- Funder identifier:
- https://ror.org/001aqnf71
- Grant:
- EP/W011344/1
- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/V000748/1
- Publisher:
- IEEE
- Acceptance date:
- 2025-07-01
- Event title:
- IEEE International Conference on Intelligent Transportation Systems (ITSC 2025)
- Event location:
- Gold Coast, Australia
- Event website:
- https://ieee-itsc.org/2025/
- Event start date:
- 2025-11-18
- Event end date:
- 2025-11-21
- Language:
-
English
- Pubs id:
-
2267968
- Local pid:
-
pubs:2267968
- Deposit date:
-
2025-08-04
Terms of use
- Copyright date:
- 2025
- Notes:
-
This paper has been accepted for the IEEE International Conference on Intelligent Transportation Systems (ITSC 2025), 18-21 November 2025, Gold Coast Australia.
The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
If you are the owner of this record, you can report an update to it here: Report update to this record