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.
Authors
- Ruitong Chi
- Peng Ji (ORCID: https://orcid.org/0000-0002-6859-463X)
- Junwei Chen (ORCID: https://orcid.org/0000-0002-5535-9745)
Institutions
- Hebei University of Engineering (CN)
- Hebei University of Science and Technology (CN)
- Hebei University of Environmental Engineering (CN)
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