融合多头注意力的时空交互感知车辆轨迹预测模型OA

High-precision vehicle trajectory prediction is crucial to improving both the safety and operational efficiency of autonomous driving systems.This paper proposes a spatiotemporal attention and bidirectional long short-term memory(STA-BiLSTM)trajectory prediction model that integrates multi-head attention mechanisms with bidirectional LSTM networks.By hierarchically extracting single-vehicle trajectory features and jointly modeling spatiotemporal interdependencies among multiple vehicles,the proposed model effectively enhances prediction accuracy in complex dynamic scenarios.First,the model employs a CNN-LSTM architecture to encode historical trajectory features of individual vehicles.Then,a multi-head attention mechanism is introduced to adaptively model spatial interactions among vehicles.Finally,a BiLSTM network is employed to capture the temporal evolution of these inter-vehicle dependencies.Experimental results demonstrate that the proposed model reduces the root mean square error(RMSE)at a 5s prediction horizon by9.8％compared with the Double Transformer baseline,while maintaining strong interpretability.By explicitly modeling the spatiotemporal dependencies between ego and surrounding vehicles,this study may provide an effective solution for trajectory prediction in complex traffic environments.