Research on Lane Change Intention Prediction Based on Fusion of Vehicle Forward Features

In mixed traffic environments with autonomous and traditional vehicles, perceiving the lane change intention of vehicles in advance is crucial to ensure traffic safety. Considering the current issue of lane change intention detection methods overemphasize the surrounding features of the host vehicle and have low accuracy, this paper proposes a lane change intention prediction algorithm that only fuses the vehicle forward features. This study first extract lane-changing trajectory data of vehicles that meet the definition of lane change from the NGSIM dataset based on changes in the lane marking. Then, the motion features and the forward traffic state features of the host vehicle are extracted from these lane-changing trajectory data to construct the dataset for predicting the vehicle’s lane change intention. Finally, we use the LSTM_Self-Attention model to predict the lane-changing intention for different lead times. The results show that the LSTM_self-Attention model proposed in this study performs well for predicting vehicle’s lane change intention. The prediction accuracy can reach 82% two seconds before the lane change and remains at 70% three seconds before the lane change, being of great significance for improving the safety of Autonomous driving.