Vehicle Trajectory Prediction Method Coupled With Ego Vehicle Motion Trend Under Dual Attention Mechanism

Predicting the trajectory of neighboring vehicles is closely related to the driving safety of intelligent vehicles and supports driving assistance. This article proposes a dual-attention mechanism trajectory prediction method based on long short-term memory encoding that is coupled with the motion trend of the ego vehicle. The temporal attention mechanism maximizes the use of historical trajectory information and analyses its validity under different working conditions. A spatial attention mechanism coupled with motion trend is proposed that analyses how neighboring vehicles influence the target vehicle's trajectory, combining the motion trend of the ego vehicle with the free space of the target vehicle to reduce uncertainty in the potential trajectory. Tests conducted on the NGSIM dataset show that the method proposed here achieves greater prediction accuracy than state-of-the-art models and can divide the historical trajectory contribution and quantify the impact of neighboring vehicles. An error analysis for different traffic scenarios shows the generalizability of the model.