Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912115449659392 |
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| author | Loh, Jia Quan Luo, Xuewen Ding, Fan Tew, Hwa Hui Loo, Junn Yong Ding, Ze Yang Susilawati, Susilawati Tan, Chee Pin |
| author_facet | Loh, Jia Quan Luo, Xuewen Ding, Fan Tew, Hwa Hui Loo, Junn Yong Ding, Ze Yang Susilawati, Susilawati Tan, Chee Pin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_06087 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction Loh, Jia Quan Luo, Xuewen Ding, Fan Tew, Hwa Hui Loo, Junn Yong Ding, Ze Yang Susilawati, Susilawati Tan, Chee Pin Artificial Intelligence Robotics 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. |
| title | Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction |
| topic | Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2411.06087 |