Deep encoder–decoder-NN: A deep learning-based autonomous vehicle trajectory prediction and correction model

Abstract

An accurate vehicle trajectory prediction promotes understanding of the traffic environment and enables task criticality assessment in advanced driver assistance systems (ADASs) in autonomous vehicles and intelligent connected vehicles. Nevertheless, conventional prediction models are characterized by low prediction accuracy, the inability of long-term prediction, and a single-road section adaptation. To tackle these limitations, this study proposes a trajectory prediction model based on a deep encoder–decoder and a deep neural network (DNN). One modification included introducing an attention mechanism into the traditional encoder–decoder framework. Overall, 1794,1400,2100 trajectory samples from highways, intersections, and roundabouts are used to train the proposed framework and obtain optimal deep encoder–decoder architectures for different road section types. Since the experiments revealed no significant advantages of using the attention mechanism in deep encoder–decoder, the mechanism is not included in the optimal architecture. Next, to achieve higher prediction accuracy and better long-term prediction capability, different DNN structures are tested as trajectory correction networks, and the optimal DNN structure is selected. Finally, the experiments are conducted using the proposed deep encoder–decoder framework and the optimal DNN. The results show that the proposed model reaches 92.87%, 86.65%, and 89.15% average trajectory fit ratio (TFR) on a highway, intersection, and a roundabout, respectively. Therefore, the model enables accurate long-term predictions of vehicle trajectories in these road segments. The proposed model and presented results provide a basis for ADASs’ trajectory prediction algorithms.