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지역 환경 및 차량 상호작용을 활용한 two-stage 멀티모달 차량 미래 경로 예측 기술
최세환(Sehwan Choi),윤준용(Junyong Yun),김정호(Jungho Kim),최준원(Jun Won Choi) 한국자동차공학회 2023 한국자동차공학회 부문종합 학술대회 Vol.2023 No.5
In this paper, we propose a two-stage multi-modal future trajectory prediction framework designed to effectively utilize significant inter-agent interaction and local scene context. This two-stage motion prediction architecture, referred to as GL-Pred, consists of two networks: the proposal trajectory network and the refinement trajectory network. The proposal trajectory network produces multi-modal trajectory proposals by leveraging past trajectories and global environmental information. The refinement trajectory network enhances each of the trajectory proposals using group-query attention and localquery attention mechanisms. Group-query attention mechanism further enhances the trajectory proposals by modeling the interagent interaction by grouping the proposal trajectory of the neighboring agents. Local-query attention mechanism is used to aggregate local scene context features collected from around trajectory proposals. Finally, we combine group-query attention and local-query attention features to produce the multi-modal future trajectory. The experiments conducted on the Argoverse dataset demonstrate that the proposed GL-Pred outperforms existing motion prediction methods.