CMT-GAT: a contrastive learning framework for predicting lane change manoeuvres of surrounding vehicles in weaving areas under missing data conditions

Human lane-changing behaviour is highly variable, making accurate intention prediction and socially adaptive decision-making challenging for intelligent vehicles. This capability is essential for safe and effective human–machine collaboration. However, real-world driving data are often degraded by communication loss between vehicles and roadside units, as well as sensor noise and uncertainty. To address these challenges, this study proposes CMT-GAT, a robust self-supervised contrastive learning framework for lane-change intention prediction in urban expressway weaving areas. The model employs a dual-channel trend attention mechanism to process complete and incomplete data in parallel, while a graph attention network captures interactions among surrounding vehicles and environmental context. Experiments demonstrate that CMT-GAT consistently achieves the highest prediction accuracy across all prediction horizons and missing-data rates, particularly under severe missing-data conditions.