用注意力特征实现跨城市跨时段车辆轨迹预测，提升模型泛化能力。
Cross-Domain Transfer Learning using Attention Latent Features for Multi-Agent Trajectory Prediction
- 基于Transformer的注意力特征进行跨域自适应
- 在跨城市和跨时段场景下显著优于现有模型
- 适合需要跨区域部署的智能交通系统
随着传感器硬件、交通基础设施和深度学习架构的发展，车辆轨迹预测已在智能交通系统中打下坚实基础。然而，现有方法通常针对特定交通网络和时间周期定制，导致在某一网络上训练的深度学习模型难以有效推广至未见网络。为此，我们提出一种新型时空轨迹预测框架，通过Transformer模型的注意力表示实现跨域自适应。同时引入图卷积网络，构建动态图特征嵌入，精确建模多智能体车辆在多个交通域中的复杂时空交互关系。该框架在跨城市与跨时段两个案例研究中进行了验证，实验结果表明，所提方法在轨迹预测与域适应性能上均优于当前最优模型。
原文摘要 · Abstract (English)
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.
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