Surrounding vehicle trajectory prediction under mixed traffic flow based on graph attention network

This paper proposes a trajectory prediction method based on graph attention network to accurately predict the trajectories of HDV (Human Drive Vehicles) around the ICV (Intelligent Connected Vehicles) under mixed traffic flow scenario on highways. Firstly, the vehicle trajectory data is filtered and smoothed to construct a trajectory prediction dataset containing map information. Secondly, the vehicle interaction relationship graph is constructed based on the position and behavior of vehicles. The high-dimensional spatial interaction relationship features between the target vehicle and surrounding vehicles are extracted using the graph attention network, which serves as input for the encoder-decoder model. Subsequently, an encoder-decoder model based on GRU (Gate Recurrent Unit) is employed to encode time-series features of vehicle trajectory data and generate future trajectories through decoding. Finally, experimental validation using NGSIM (Next Generation Simulation) datasets demonstrates that our proposed method achieves low displacement error in predicting vehicle trajectories compared to models such as GRU, and CNN-GRU (Convolutional Neural Network-Gate Recurrent Unit).