{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T22:54:59Z","timestamp":1768344899173,"version":"3.49.0"},"reference-count":70,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T00:00:00Z","timestamp":1638748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Institute for Information and communications Technology Planning and Evaluation","award":["IITP-2021-2018-0-01426"],"award-info":[{"award-number":["IITP-2021-2018-0-01426"]}]},{"DOI":"10.13039\/501100003052","name":"Ministry of Trade, Industry and Energy","doi-asserted-by":"publisher","award":["20013794"],"award-info":[{"award-number":["20013794"]}],"id":[{"id":"10.13039\/501100003052","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>It is essential for autonomous vehicles at level 3 or higher to have the ability to predict the trajectories of surrounding vehicles to safely and effectively plan and drive along trajectories in complex traffic situations. However, predicting the future behavior of vehicles is a challenging issue because traffic vehicles each have different drivers with different driving tendencies and intentions and they interact with each other. This paper presents a Long Short-Term Memory (LSTM) encoder\u2013decoder model that utilizes an attention mechanism that focuses on certain information to predict vehicles\u2019 trajectories. The proposed model was trained using the Highway Drone (HighD) dataset, which is a high-precision, large-scale traffic dataset. We also compared this model to previous studies. Our model effectively predicted future trajectories by using an attention mechanism to manage the importance of the driving flow of the target and adjacent vehicles and the target vehicle\u2019s dynamics in each driving situation. Furthermore, this study presents a method of linearizing the road geometry such that the trajectory prediction model can be used in a variety of road environments. We verified that the road geometry linearization mechanism can improve the trajectory prediction model\u2019s performance on various road environments in a virtual test-driving simulator constructed based on actual road data.<\/jats:p>","DOI":"10.3390\/s21238152","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T22:18:42Z","timestamp":1638829122000},"page":"8152","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Vehicle Trajectory Prediction with Lane Stream Attention-Based LSTMs and Road Geometry Linearization"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9959-4827","authenticated-orcid":false,"given":"Dongyeon","family":"Yu","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea"}]},{"given":"Honggyu","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea"}]},{"given":"Taehoon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3665-2564","authenticated-orcid":false,"given":"Sung-Ho","family":"Hwang","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Sungkyunkwan University, 2066 Seobu-ro, Suwon 16419, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/MITS.2016.2565718","article-title":"If, When, and How to Perform Lane Change Maneuvers on Highways","volume":"8","author":"Nilsson","year":"2016","journal-title":"IEEE Intell. 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