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Traffic Agents Trajectory Prediction Based on Spatial-Temporal Interaction Attention.

Jincan Xie^1,2, Shuang Li^1,2, Chunsheng Liu^1,2

¹School of Information and Automation Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China.

Sensors (Basel, Switzerland)

|September 28, 2023

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This study introduces a new network for trajectory prediction, enhancing accuracy by modeling complex interactions between traffic participants. The Spatial-Temporal Interaction Attention-based Trajectory Prediction Network (STIA-TPNet) improves future movement predictions.

Area of Science:

Computer Science
Artificial Intelligence
Robotics

Background:

Trajectory prediction is crucial for autonomous systems and traffic management.
Existing methods struggle with complex spatio-temporal interactions and dynamic participant numbers.
Pooling methods inadequately capture social interactions, leading to prediction errors over time.

Purpose of the Study:

To develop an advanced trajectory prediction network that effectively models spatio-temporal interactions.
To introduce a novel Spatial-Temporal Interaction Attention Module (STIA Module) for enhanced interaction modeling.
To improve the accuracy and robustness of trajectory prediction in dynamic traffic scenarios.

Main Methods:

Proposed the Spatial-Temporal Interaction Attention-based Trajectory Prediction Network (STIA-TPNet).

Keywords:

social interaction spatial–temporal interaction trajectory prediction

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Developed the STIA Module with temporal, spatial, and fused spatio-temporal attention mechanisms.

Utilized graph neural networks to model spatial interactions between dynamically changing participants.

Main Results:

The STIA Module adaptively allocates attention weights to capture movement patterns and interaction importance.
Experiments on INTERACTION and UTP datasets demonstrated significant improvements in prediction accuracy.
The proposed STIA-TPNet outperformed existing representative trajectory prediction methods.

Conclusions:

STIA-TPNet effectively models complex spatio-temporal interactions for accurate trajectory prediction.
The STIA Module is key to capturing dynamic social behaviors in traffic scenarios.
This approach offers a robust solution for future movement prediction in complex environments.