TRACER: Transfer Knowledge-Based Collaborative Vehicle Trajectory Prediction for Highway Traffic Toward Cross-Region Adaptivity

Vehicle trajectory prediction, as a key enabler of the intelligent transportation system, has attracted considerable attention from academia and industry in recent years. However, the variability and dynamism of traffic conditions pose significant challenges to current vehicle trajectory prediction methods, particularly in the form of domain bias. Domain bias occurs when a model trained on one traffic domain, such as one segment of a highway, underperforms when applied to another segment with different traffic patterns. To address this challenge and advance the field, we propose a new transfer learning-based collaborative vehicle trajectory prediction framework called TRACER, designed to provide reliable and accurate traffic predictions with high adaptability for cross-domain highway traffic scenarios. The core of our framework lies in an adaptive interactive extraction module and a trajectory generation module based on Bidirectional Long Short-Term Memory (BiLSTM), further strengthened by a pre-task of intention recognition for vehicle operation types. To improve model robustness, consistency regularization is applied by injecting disturbances into the target data, and a one-dimensional Convolution (Conv1D)-based intention extraction module is integrated into the BiLSTM-based trajectory generation process, leading to notable improvements in prediction accuracy. Our framework is first trained on source domain data, followed by the transfer of a small amount of labeled data from the target domain, and the overall model is further refined using unlabeled data. By effectively mitigating domain bias, TRACER significantly enhances trajectory prediction accuracy while maintaining high adaptability. The results underscore the importance of addressing domain shift challenges in trajectory prediction tasks and demonstrate the potential of domain adaptation techniques to improve the prediction accuracy of vehicle trajectories across different domains in highway scenarios. © 2025 Elsevier B.V., All rights reserved.

Авторы
Qian Hui 1 , Shi Xin 2 , Hawbani Ammar 1 , Bi Yuanguo 3 , Liu Zhi 4 , Muthanna Ammar 5, 6 , Zhao Liang 1
Издательство
Institute of Electrical and Electronics Engineers Inc.
Номер выпуска
7
Язык
English
Страницы
10747-10763
Статус
Published
Том
26
Год
2025
Организации
  • 1 School of Computer Science, Shenyang Aerospace University, Shenyang, China
  • 2 KINGSEMI Co. Ltd., Shenyang, China
  • 3 College of Computer Science and Engineering, Northeastern University, Shenyang, China
  • 4 Department of Computer and Network Engineering, The University of Electro-Communications, Chofu, Japan
  • 5 Department of Applied Probability and Informatics, RUDN University, Moscow, Russian Federation
  • 6 Department of Telecommunication Networks and Data Transmission, Sankt-Peterburgskij Gosudarstvennyj Universitet Telekommunikacij imeni professora Bonch-Bruevicha, Saint Petersburg, Russian Federation
Ключевые слова
cross-region; domain adaption; domain shift; Intelligent transportation system; transfer learning; vehicle trajectory prediction
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