Research on Vehicle Trajectory Prediction Based on Multifeature Fusion

In response to the problems of insufficient vehicle feature extraction and single prediction scenarios in existing models, this paper proposes a vehicle trajectory prediction model, called MTF-GRU-MTSHMA, that integrates multiple features in multiple scenarios. The proposed model consists of an encoder module, multifeature extraction module, multifeature fusion module, and trajectory prediction module. In the encoder module, the Gated Recurrent Unit (GRU) is used to encode the historical information of the vehicle to obtain its historical status. In the multifeature extraction module, considering the spatial correlation between surrounding vehicles in the target vehicle area, a multidimensional spatial attention mechanism is proposed to mine the deep features of surrounding vehicles. Additionally, a triple attention mechanism is introduced to extract features from the encoded state vector. In the multifeature fusion module, the extracted multiple features are linearly concatenated and input into the multifeature fusion network for fusion. In the trajectory prediction module, improvements are made to the GRU by proposing a Mixed Teaching Force Gated Recurrent Unit (MTF-GRU) as the decoder, which controls the decoding mode by introducing a teaching rate to improve decoding performance. The fused features are input into the decoder to generate future trajectories. The proposed model is experimentally simulated using the NGSIM dataset. The results show that the average Root Mean Square Error (RMSE) of the proposed model for straight road, intersection, and roundabout road scenarios increases by 8.16%, 10.31%, and 8.37%, respectively, compared with the optimal benchmark model, proving the effectiveness of the proposed model.