@Article{cmes.2026.086179,
AUTHOR = {Huaran Zhou, Gaoteng Yuan, Ping Qiu},
TITLE = {Lane-Changing Intention-Aware Vehicle Trajectory Prediction via Mutual Information-Guided Feature Selection and a Hybrid Convolutional Neural Network–Transformer Architecture},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/CMES/online/detail/28248},
ISSN = {1526-1506},
ABSTRACT = {Accurate vehicle trajectory prediction and lane-changing intention recognition are essential for autonomous driving and advanced driver-assistance systems, as they support motion planning, collision avoidance, and risk assessment. Existing deep learning methods often use high-dimensional trajectory variables without explicitly evaluating their relevance, which may introduce redundant information and reduce computational efficiency. In addition, local motion variations and long-range temporal dependencies are frequently modeled in isolation, although both are important for representing lane-changing behavior. To address these limitations, this paper proposes a joint intention-and-trajectory prediction framework that combines mutual-information-guided feature selection with a hybrid convolutional neural network (CNN)–Transformer architecture. Mutual information (MI) is used to rank target-vehicle, surrounding-vehicle, and road-related features according to their relevance to lane-changing intention, and a compact feature subset is retained. The CNN branch extracts short-term local motion patterns, whereas the Transformer branch captures long-range temporal dependencies. Their fused representation is optimized through an intention-classification head and a trajectory-regression head. Experiments on the Next Generation Simulation (NGSIM) and highD naturalistic highway trajectory datasets show that the proposed framework achieves better results than the in-house CNN, long short-term memory (LSTM), and Transformer baselines under the same evaluation protocol. It achieves intention-recognition accuracies of 95.81% and 92.16% on NGSIM and highD, respectively, while also reducing trajectory-prediction errors. The results indicate that feature relevance analysis and complementary local–global temporal modeling can improve the accuracy, efficiency, and interpretability of intention-aware vehicle trajectory prediction.},
DOI = {10.32604/cmes.2026.086179}
}