Vehicle trajectory prediction based on the integration of long- and short-term spatiotemporal features and trajectory optimization

The study demonstrates that the CTS-Informer model delivers strong long-term prediction performance, with each component contributing effectively to overall accuracy. The results validate the effectiveness of multi-scale spatiotemporal feature fusion for extended-horizon prediction. Furthermore, the model provides reliable trajectory boundaries, providing robust reference input for autonomous driving decision-making systems.

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Publication Details

Journal
Traffic Injury Prevention
Published
2025-10-27
DOI
https://doi.org/10.1080/15389588.2025.2569715
Primary Topic
Autonomous Vehicle Technology and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

Vehicle trajectory prediction based on the integration of long- and short-term spatiotemporal features and trajectory optimization

Ruitong Chi, Peng Ji, Junwei Chen
Traffic Injury Prevention
Autonomous Vehicle Technology and Safety
article

Vehicle trajectory prediction based on the integration of long- and short-term spatiotemporal features and trajectory optimization

Ruitong Chi, Peng Ji, Junwei Chen
article en

Abstract

The study demonstrates that the CTS-Informer model delivers strong long-term prediction performance, with each component contributing effectively to overall accuracy. The results validate the effectiveness of multi-scale spatiotemporal feature fusion for extended-horizon prediction. Furthermore, the model provides reliable trajectory boundaries, providing robust reference input for autonomous driving decision-making systems.

Traffic Injury Prevention
Hebei University of Engineering (CN), Hebei University of Science and Technology (CN), Hebei University of Environmental Engineering (CN)
Openalex Percentile: Top 66%
Autonomous Vehicle Technology and Safety
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