Interaction Aware Trajectory Prediction of Surrounding Vehicles with Interaction Network and Deep Ensemble
- Title
- Interaction Aware Trajectory Prediction of Surrounding Vehicles with Interaction Network and Deep Ensemble
- Author
- 허건수
- Issue Date
- 2020-11
- Publisher
- IEEE
- Citation
- 2020 IEEE Intelligent Vehicles Symposium (IV), page. 1714-1719
- Abstract
- For the path planning of autonomous vehicles, it is important to predict the future trajectory of the surrounding vehicles. However, predicting future trajectory is difficult because it needs to consider the invisible interaction between the vehicles in a dynamic driving environment. In this paper, a new approach, which considers the interaction between surrounding vehicles, is proposed for accurate prediction of the future trajectory. The proposed method provides continuous predicted trajectories over time in the longitudinal and lateral directions, respectively. The deep ensemble technique is also used to predict the uncertainty of the estimated trajectory. This paper performs the training and verification of the algorithm using NGSIM dataset, which is the vehicle driving data obtained through actual vehicle driving.
- URI
- https://ieeexplore.ieee.org/document/9304713https://repository.hanyang.ac.kr/handle/20.500.11754/172500
- ISBN
- 978-1-7281-6673-5; 978-1-7281-6674-2; 978-1-7281-6672-8
- ISSN
- 2642-7214; 1931-0587
- DOI
- 10.1109/IV47402.2020.9304713
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > AUTOMOTIVE ENGINEERING(미래자동차공학과) > Articles
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