DC Field | Value | Language |
dc.contributor.author | Tang Yi | - |
dc.contributor.author | German, O. V. | - |
dc.coverage.spatial | Минск | en_US |
dc.date.accessioned | 2025-01-11T07:17:32Z | - |
dc.date.available | 2025-01-11T07:17:32Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | Tang Yi. А hybrid agent-centric and scene-centric approach for multi-agent trajectory prediction / Tang Yi, O. V. German // Информационные технологии и системы 2024 (ИТС 2024) = Information Technologies and Systems 2024 (ITS 2024) : материалы международной научной конференции, Минск, 20 ноября 2024 г. / Белорусский государственный университет информатики и радиоэлектроники ; редкол.: Л. Ю. Шилин [и др.]. – Минск, 2024. – С. 200–201. | en_US |
dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/58675 | - |
dc.description.abstract | Accurately predicting the future trajectories of agents in autonomous driving is crucial for safe navigation and decision-making. Traditional trajectory prediction models have limitations when dealing with complex multi-agent interactions. In this paper, we propose a hybrid approach that leverages the strengths of both agent-centric and scene-centric models by using agent-centric normalization for dynamic agents and a scene-centric framework for static map elements. | en_US |
dc.language.iso | en | en_US |
dc.publisher | БГУИР | en_US |
dc.subject | материалы конференций | en_US |
dc.subject | automated systems | en_US |
dc.subject | multi-agent systems | en_US |
dc.subject | autonomous driving | en_US |
dc.subject | machine learning | en_US |
dc.title | А hybrid agent-centric and scene-centric approach for multi-agent trajectory prediction | en_US |
dc.type | Article | en_US |
Appears in Collections: | ИТС 2024
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