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Learning-based approaches for multi-agent trajectory prediction, such as primarily relying on graph neural networks, graph transformers, and hypergraph neural networks, have demonstrated outstanding performance on real-world datasets in recent years. However, the hypergraph transformer-based method for trajectory prediction is yet to be explored. Therefore, we present a <jats:bold>M<\/jats:bold>ultisc<jats:bold>A<\/jats:bold>le <jats:bold>R<\/jats:bold>elational <jats:bold>T<\/jats:bold>ransformer (<jats:bold>MART<\/jats:bold>) network for multi-agent trajectory prediction. MART is a hypergraph transformer architecture to consider individual and group behaviors in transformer machinery. The core module of MART is the encoder, which comprises a Pair-wise Relational Transformer (PRT) and a Hyper Relational Transformer (HRT). The encoder extends the capabilities of a relational transformer by introducing HRT, which integrates hyperedge features into the transformer mechanism, promoting attention weights to focus on group-wise relations. In addition, we propose an Adaptive Group Estimator (AGE) designed to infer complex group relations in real-world environments. Extensive experiments on three real-world datasets (NBA, SDD, and ETH-UCY) demonstrate that our method achieves state-of-the-art performance, enhancing ADE\/FDE by 3.9%\/11.8% on the NBA dataset. Code is available at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/gist-ailab\/MART\">https:\/\/github.com\/gist-ailab\/MART<\/jats:ext-link>.<\/jats:p>","DOI":"10.1007\/978-3-031-72848-8_6","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T14:03:03Z","timestamp":1732802583000},"page":"89-107","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":22,"title":["MART: MultiscAle Relational Transformer Networks for\u00a0Multi-agent Trajectory Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1712-0282","authenticated-orcid":false,"given":"Seongju","family":"Lee","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5212-2657","authenticated-orcid":false,"given":"Junseok","family":"Lee","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2147-4718","authenticated-orcid":false,"given":"Yeonguk","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5139-7963","authenticated-orcid":false,"given":"Taeri","family":"Kim","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4299-4923","authenticated-orcid":false,"given":"Kyoobin","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"6_CR1","doi-asserted-by":"crossref","unstructured":"Alahi, A., Goel, K., Ramanathan, V., Robicquet, A., Fei-Fei, L., Savarese, S.: Social LSTM: human trajectory prediction in crowded spaces. 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