首页|Multibranch Attentive Transformer With Joint Temporal and Social Correlations for Traffic Agents Trajectory Prediction

Multibranch Attentive Transformer With Joint Temporal and Social Correlations for Traffic Agents Trajectory Prediction

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  • NETL
  • NSTL
  • IEEE
Accurately predicting the future trajectories of traffic agents is paramount for autonomous unmanned systems, such as self-driving cars and mobile robotics. Extracting abundant temporal and social features from trajectory data and integrating the resulting features effectively pose great challenges for predictive models. To address these issues, this article proposes a novel multibranch attentive transformer (MBAT) trajectory prediction network for traffic agents. Specifically, to explore and reveal diverse correlations of agents, we propose a decoupled temporal and spatial feature learning module with multibranch to extract temporal, spatial, as well as spatiotemporal features. Such design ensures each branch can be specifically tailored for different types of correlations, thus enhancing the flexibility and representation ability of features. Besides, we put forward an attentive transformer architecture that simultaneously models the complex correlations possibly occurring in historical and future timesteps. Moreover, the temporal, spatial, and spatiotemporal features can be effectively integrated based on different types of attention mechanisms. Empirical results demonstrate that our model achieves outstanding performance on public ETH, UCY, SDD, and INTERACTION datasets. Detailed ablation studies are conducted to verify the effectiveness of the model components.

TrajectoryFeature extractionTransformersCorrelationPredictive modelsSpatiotemporal phenomenaAttention mechanismsComputational modelingRepresentation learningGraph neural networks

Xiaobo Chen、Yuwen Liang、Junyu Wang、Qiaolin Ye、Yingfeng Cai

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School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China

School of Information and Electronic Engineering, Shandong Technology and Business University, Yantai, China

College of Information Science and Technology & Artificial Intelligence, State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, China

Automotive Engineering Research Institute, Jiangsu University, Zhenjiang, China

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2025

10.1109/TCSS.2024.3517656

IEEE transactions on computational social systems

IEEE transactions on computational social systems

ISSN：
年,卷(期)：2025.12(2)
  • 52