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(3) The fine-grained fusion module integrates the observed trajectory with the scene paths to generate multiple future trajectories. To fully explore the scene information and improve the efficiency, we present a novel scene-fusion Transformer, whose encoder is used to extract scene features and the decoder is used to fuse scene and trajectory features to generate future trajectories. Compared with the current state-of-the-art methods, our method decreases the ADE errors by 4.3% and 3.3% by gradually integrating different granularity of scene information on SDD and NuScenes, respectively. The visualized trajectories demonstrate that our method can accurately predict future trajectories after fusing scene information.<\/jats:p>","DOI":"10.1007\/s40747-022-00834-2","type":"journal-article","created":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T18:02:37Z","timestamp":1659636157000},"page":"851-864","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multi-granularity scenarios understanding network for trajectory prediction"],"prefix":"10.1007","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4434-0141","authenticated-orcid":false,"given":"Biao","family":"Yang","sequence":"first","affiliation":[]},{"given":"Jicheng","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Rongrong","family":"Ni","sequence":"additional","affiliation":[]},{"given":"Changchun","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"834_CR1","doi-asserted-by":"publisher","first-page":"137","DOI":"10.48550\/arXiv.1907.03395","volume":"13","author":"P Kothari","year":"2021","unstructured":"Kothari P, Kreiss S, Alahi A (2021) Human trajectory forecasting in crowds: a deep learning perspective. 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