Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction
- Authors
- Kim, H.; Kim, D.; Kim, G.; Cho, J.; Huh, K.
- Issue Date
- 2020
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Citation
- IEEE Intelligent Vehicles Symposium, Proceedings, pp.1720 - 1725
- Indexed
- SCOPUS
- Journal Title
- IEEE Intelligent Vehicles Symposium, Proceedings
- Start Page
- 1720
- End Page
- 1725
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/3767
- DOI
- 10.1109/IV47402.2020.9304741
- Abstract
- This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multihead attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - 서울 공과대학 > 서울 미래자동차공학과 > 1. Journal Articles
![qrcode](https://api.qrserver.com/v1/create-qr-code/?size=55x55&data=https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/3767)
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.