Spatio-temporal interactive graph convolution network for vehicle trajectory prediction

Abstract

Vehicle trajectory prediction is crucial in achieving safe and reliable autonomous driving decision-making. The accuracy of the prediction is affected by many different factors, such as the integrity and efficiency of vehicle-to-vehicle (V2V) data transfer, the complex environmental factors of the surrounding roads and the perception range of vehicle sensors. However, most existing methods cannot capture the dynamic interactive information of vehicles at different time steps. In this paper, we propose a Spatio-Temporal Interactive Graph Convolutional Network (STI-GCN) that predicts future trajectories by acquiring spatiotemporal features of vehicles. In the spatial dimension, we construct a kernel function based on spatial autocorrelation and use it as prior knowledge to describe the degree of mutual influence between vehicles in real traffic scenarios. In addition, the Gated Recurrent Unit (GRU) is used to dynamically capture the spatial features of vehicles to solve the dynamic making graph problem of vehicles in the real traffic scene. In the temporal dimension, we use a Convolutional Neural Network (CNN) to extract the temporal feature in the historical trajectory of the vehicle. Finally, we experimented with our method and existing methods on the public Next Generation Simulation (NGSIM) dataset. The experimental results show that the error of our model is reduced by about 10% compared with the state-of-the-art model. And it also improves about 10 times in two key metrics, namely the number of parameters and inference time.