注意潜在特徴を用いたクロスドメイン転移学習によるマルチエージェント軌道予測
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
Transformerの注意表現にクロスドメイン適応を行い、グラフ畳み込みネットワークで車両間の時空間相互作用をモデル化することで、未知の交通ネットワークでも高精度に軌道予測できるフレームワークを提案した。
著者: Jia Quan Loh, Xuewen Luo, Fan Ding, Hwa Hui Tew, Junn Yong Loo, Ze Yang Ding, Susilawati Susilawati, Chee Pin Tan
分類: cs.AI, cs.RO
原文アブストラクト
With the advancements of sensor hardware, traffic infrastructure and deep learning architectures, trajectory prediction of vehicles has established a solid foundation in intelligent transportation systems. However, existing solutions are often tailored to specific traffic networks at particular time periods. Consequently, deep learning models trained on one network may struggle to generalize effectively to unseen networks. To address this, we proposed a novel spatial-temporal trajectory prediction framework that performs cross-domain adaption on the attention representation of a Transformer-based model. A graph convolutional network is also integrated to construct dynamic graph feature embeddings that accurately model the complex spatial-temporal interactions between the multi-agent vehicles across multiple traffic domains. The proposed framework is validated on two case studies involving the cross-city and cross-period settings. Experimental results show that our proposed framework achieves superior trajectory prediction and domain adaptation performances over the state-of-the-art models.
