Lane Change Prediction for Autonomous Driving With Transferred Trajectory Interaction

Lu, Yuhuan ORCID: https://orcid.org/0000-0001-5332-3389, Xu, Pengpeng ORCID: https://orcid.org/0000-0001-9826-4212, Jiang, Xinyu ORCID: https://orcid.org/0009-0003-9052-8043, Bashir, Ali Kashif ORCID: https://orcid.org/0000-0003-2601-9327, Gadekallu, Thippa Reddy ORCID: https://orcid.org/0000-0003-0097-801X, Wang, Wei ORCID: https://orcid.org/0000-0002-1717-5785 and Hu, Xiping ORCID: https://orcid.org/0000-0002-4952-699X (2025) Lane Change Prediction for Autonomous Driving With Transferred Trajectory Interaction. IEEE Transactions on Intelligent Transportation Systems, 26 (4). pp. 4543-4556. ISSN 1524-9050

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Abstract

In mixed-autonomy traffic environments, accurately predicting the lane change behavior of human-driven vehicles is critical for ensuring the safety and reliability of autonomous vehicle decision-making. However, existing approaches face two major challenges: 1) they tend to represent the relationships between the target vehicle and surrounding vehicles using parameters like relative position and speed. This approach either requires a fixed number of surrounding vehicles or introduces significant noise by relying on virtual vehicles; and 2) they often fail to fully exploit the vast amount of available vehicle trajectory data, leaving the complexities of vehicular interactions underexplored. To address these issues, this paper presents a novel lane change prediction framework using Transformer-based transfer learning. Our design aims to leverage inter-vehicle interactions learned from trajectory data to improve lane-change prediction accuracy. Specifically, pre-trained trajectory prediction models are used to adapt dynamically to the varying number of surrounding vehicles and to capture interaction context from large sets of trajectory data. We then refine the Transformer model to integrate this context and predict the target vehicle’s lane change intentions. The Transformer encoder transforms trajectory interaction context into a lane-change-oriented context using aggregated multi-head attention. The Transformer decoder, in turn, utilizes this context alongside the target vehicle’s states through relation-aware multi-head attention to forecast lane change behavior. Extensive experiments on two real-world datasets demonstrate that our proposed framework outperforms state-of-the-art baselines in both accuracy and robustness.