For human‒machine co-driving vehicles, the dynamic complexity of urban scenarios and the uncertainty of driving behavior impose stringent demands on the accuracy and generalization capability of ego vehicle trajectory prediction. Current research predominantly relies on multivehicle interaction information or single-scenario settings, overlooking the inherent dynamic correlation between driver and vehicle. This results in prediction models struggle to adapt to complex urban environments. To address this, this study proposes a trajectory prediction framework based on driver-vehicle coupling feature encoding, enabling precise capture of vehicle trajectory evolution patterns under driver manipulation across diverse urban scenarios. The framework employs bidirectional long short-term memories (LSTMs) to perform temporal encoding on historical driver-vehicle coupling features and future road geometric features. Combined with an attention mechanism, it generates context vectors integrating temporal features and manipulation details, ultimately using LSTMs to recursively produce multistep prediction results. Validation using diverse urban scenarios data from a dynamic driving simulatordemonstrates that our prediction framework achieves precise trajectory prediction in typical scenarios such as lane changing, turning, and roundabout navigation, while exhibiting robust stability and generalization capabilities.