Improved Attention Mechanism for Human-like Intelligent Vehicle Trajectory Prediction

In order to overcome the low long-term predictive accuracy associated with mainstream prediction models and the limited consideration of driver characteristics, this study presents an enhanced attention mechanism for human-like trajectory prediction, which is based on Long Short-Term Memory (LSTM). A novel database structure is proposed that incorporates data about driving style and driving intent, pertaining to human factors. By utilizing the convolution computation of Convolutional Social-Long Short-Term Memory (CS-LSTM) for surrounding vehicles, spatial feature extraction around the target vehicle is achieved. Simultaneously, we introduce a dynamic driving style recognition model and a human-like driving intent recognition model to fulfill the output of the human-like module. From a temporal perspective, we employ a decoder attention mechanism to reinforce the emphasis on key historical information, while refining the attention mechanism based on driving style for human-like weight assignment. Comparative analysis with other models indicates that the proposed Driving Style-based Attention-enhanced Convolutional Social-Long Short-Term Memory (DACS-LSTM) model exhibits notable advantages in predicting human-like trajectories for long-term tasks. Visualizing the predicted trajectories of both the Attention-enhanced Convolutional Social-Long Short-Term Memory (ACS-LSTM) and our proposed model, and analyzing the impact of the human-like module on the predicted trajectory, shows that our model's predicted trajectory aligns more closely with the actual one. By comparing the weight distribution of the conventional attention mechanism and the enhanced attention mechanism proposed here, and analyzing the trajectory changes in conjunction with the driving styles, it becomes evident that our proposed model offers a marked improvement.