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To address the importance of fine\u2010grained vehicle interaction modelling in vehicle trajectory prediction, the present work proposes an integrated vehicle trajectory prediction model that combines the multihead attention mechanism with long short\u2010term memory (IMA\u2010LSTM) in multivehicle interaction scenarios. Compared to existing studies, a dedicated feature extraction module including both individual features and interactive features is designed and sophisticated multihead attention mechanism is applied with LSTM framework to capture the variation of spatial\u2010temporal interactions between vehicles. The performance of the proposed model in different scenarios is examined using both the high\u2010D and the NGSIM dataset through comprehensive comparison experiments. The results indicate that the proposed IMA\u2010LSTM model presents great improvement in vehicle trajectory prediction performance in different scenarios compared to the model that does not consider multivehicle interaction features. Moreover, it outperforms other existing models in 3\u20135s prediction horizons and such superiority is more evident in left lane\u2010changing (LLC) scenarios than lane\u2010keeping (LK) and right lane\u2010changing (RLC) scenarios. The outcomes fully address the importance of fine\u2010grained interactive feature modelling in improving vehicle trajectory performance in complex multivehicle interaction scenarios and could further contribute to more refined traffic safety and traffic efficiency management.<\/jats:p>","DOI":"10.1155\/2024\/3058863","type":"journal-article","created":{"date-parts":[[2024,6,30]],"date-time":"2024-06-30T07:36:06Z","timestamp":1719732966000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["IMA\u2010LSTM: An Interaction\u2010Based Model Combining Multihead Attention with LSTM for Trajectory Prediction in Multivehicle Interaction Scenario"],"prefix":"10.1155","volume":"2024","author":[{"given":"Xiaohong","family":"Yin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingpeng","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4856-5161","authenticated-orcid":false,"given":"Tian","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gaoyao","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qihua","family":"Zhan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,6,30]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2020.2968618"},{"key":"e_1_2_9_2_2","first-page":"257","article-title":"Vehicle trajectory prediction for automated driving based on temporal convolution networks[C]\/\/2022 WRC Symposium on Advanced Robotics and Automation (WRC SARA)","author":"Li D.","year":"2022","journal-title":"IEEE"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.comcom.2020.03.041"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2017.04.425"},{"key":"e_1_2_9_5_2","doi-asserted-by":"crossref","unstructured":"AmmounS.andNashashibiF. 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