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However, most of the existing methods only focus on the interaction of local pedestrians according to distance, ignoring the influence of far pedestrians; the range of network input (receptive field) is small. In this paper, an extended graph attention network (EGAT) is proposed to increase receptive field, which focuses not only on local pedestrians, but also on those who are far away, to further strengthen pedestrian interaction. In the temporal domain, TSG\u2010LSTM (TS\u2010LSTM and TG\u2010LSTM) and P\u2010LSTM are proposed based on LSTM to enhance information transmission by residual connection. Compared with state\u2010of\u2010the\u2010art methods, the model EGAT achieves excellent performance on both ETH and UCY public datasets and generates more reliable trajectories.<\/jats:p>","DOI":"10.1155\/2021\/9985401","type":"journal-article","created":{"date-parts":[[2021,10,20]],"date-time":"2021-10-20T11:08:35Z","timestamp":1634728115000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["EGAT: Extended Graph Attention Network for Pedestrian Trajectory Prediction"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0321-4149","authenticated-orcid":false,"given":"Wei","family":"Kong","sequence":"first","affiliation":[]},{"given":"Yun","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8533-2084","authenticated-orcid":false,"given":"Hui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Chuanxu","family":"Wang","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2021,10,19]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1103\/physreve.51.4282"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1287\/trsc.1040.0108"},{"key":"e_1_2_9_3_2","doi-asserted-by":"crossref","unstructured":"AlahiA. 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