A Self-Selection Personalized Lane-Changing Trajectory Prediction Approach Through a Hybrid Deep Learning Model

The difference between the desired trajectory of the drivers and the lane change trajectory of autonomous vehicles is one of the crucial factors that limit their development. This paper proposes a lane change trajectory prediction framework, referred to as LCT-DPP, which incorporates driver personality parameters to improve the acceptance of the lane change trajectories of autonomous vehicles. In order to identify the lane-changing styles of the drivers, a clustering algorithm Autoencoder (AE) -Gaussian Mixture Model (GMM) is first proposed to cluster the historical lane-changing trajectories and vehicle interaction parameters of the vehicles and classify the lane-changing styles of the driver into aggressive, normal, and conservative according to the clustering results. The encoder module and the fully connected layer are then used to predict the lane change style of the driver, and the output is a vector holding the lane change style features of the driver. Finally, the encoder-decoder model is developed for lane change trajectory prediction by fusing lane change style feature vectors and time series features and using them as inputs to the model. The proposed model is then validated through experiments. The obtained results show that the root mean square errors (RMSEs) of the TCN-LSTM-Attention based encoder-decoder model are respectively 0.312, 0.568, and 0.734 m for the time intervals of 1, 2, and 3 s, which indicates a very high prediction accuracy.