Interaction Aware Trajectory Prediction of Surrounding Vehicles with Interaction Network and Deep Ensemble
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Min, K. | - |
dc.contributor.author | Kim, H. | - |
dc.contributor.author | Park, J. | - |
dc.contributor.author | Kim, D. | - |
dc.contributor.author | Huh, K. | - |
dc.date.accessioned | 2021-07-30T05:13:51Z | - |
dc.date.available | 2021-07-30T05:13:51Z | - |
dc.date.created | 2021-05-11 | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/3770 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Interaction Aware Trajectory Prediction of Surrounding Vehicles with Interaction Network and Deep Ensemble | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Huh, K. | - |
dc.identifier.doi | 10.1109/IV47402.2020.9304713 | - |
dc.identifier.scopusid | 2-s2.0-85099885178 | - |
dc.identifier.bibliographicCitation | IEEE Intelligent Vehicles Symposium, Proceedings, pp.1714 - 1719 | - |
dc.relation.isPartOf | IEEE Intelligent Vehicles Symposium, Proceedings | - |
dc.citation.title | IEEE Intelligent Vehicles Symposium, Proceedings | - |
dc.citation.startPage | 1714 | - |
dc.citation.endPage | 1719 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Automobile drivers | - |
dc.subject.keywordPlus | Forecasting | - |
dc.subject.keywordPlus | Trajectories | - |
dc.subject.keywordPlus | Uncertainty analysis | - |
dc.subject.keywordPlus | Vehicles | - |
dc.subject.keywordPlus | Accurate prediction | - |
dc.subject.keywordPlus | Driving environment | - |
dc.subject.keywordPlus | Ensemble techniques | - |
dc.subject.keywordPlus | Interaction networks | - |
dc.subject.keywordPlus | Lateral directions | - |
dc.subject.keywordPlus | New approaches | - |
dc.subject.keywordPlus | Trajectory prediction | - |
dc.subject.keywordPlus | Intelligent vehicle highway systems | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.