Trajectory Prediction Using Graph-Based Deep Learning for Longitudinal Control of Autonomous Vehicles: A Proactive Approach for Autonomous Driving in Urban Dynamic Traffic Environments

Abstract

This article presents a method for the trajectory prediction of surrounding vehicles and proactive longitudinal control of autonomous vehicles (AVs) in an urban road environment. A long short-term memory (LSTM)-based deep learning model is designed for the surrounding vehicles’ trajectory prediction. In our model, the historical evolution of the relation between a target vehicle and lanes is considered to learn the driver’s behavior in a lane-aware manner. Interaction among adjacent vehicles is captured based on a graph convolutional network (GCN), which uses a self-attention mechanism. Compared to other approaches, our prediction model utilizes environment information that is acquirable in AVs with local sensors. A model predictive control (MPC) is designed to derive the control inputs of acceleration for AVs. The proposed control method utilizes the prediction results of the target vehicle to give action requests to AV in a proactive manner considering both safety and ride quality. The results of comparative studies indicate that the proposed prediction model achieves improved accuracy compared to baselines. The control results provided by automated driving tests show that the proposed control algorithm applied by the LSTM-based prediction model enables AVs to achieve safety with respect to surrounding vehicles and provide ride comfort to passengers.