Fine-grained highway autonomous vehicle lane-changing trajectory prediction based on a heuristic attention-aided encoder-decoder model

Abstract

Accurate prediction of lane-changing (LC) trajectories can improve traffic efficiency and reduce the probability of accidents, as well as provide driving strategies for intelligent connected vehicles (ICVs) and connected autonomous vehicles (CAVs). Aiming at solving the problems of low prediction accuracy, difficulty in long-term prediction, and an inability of a fine-grained level description of conventional models, a prediction model based on an attention-aided encoder-decoder structure and deep neural network (DNN) is proposed. This study analyzes the LC process and proposes an LC segmentation and sampling method for dividing LC into four stages. The optimal attention-aided encoder-decoder model is tested by trajectory data in the four LC stages and then a heuristic network model is designed. In addition, the proposed heuristic network is connected with the DNN for predicting vehicle kinematics data while predicting the vehicle trajectory. Finally, the heuristic network and DNN are tested in cascade to form a joint cascade prediction model that can perform a fine-grained LC description based on the prediction results. The experimental results show that the proposed cascade prediction model has high accuracy and long-term prediction capability of a vehicle’s trajectory, velocity, acceleration, and steering angle and is also capable of fine-grained LC description. The proposed prediction model can provide a useful theoretical basis for further research on ICVs and CAVs.