Vehicular Positioning Based on GPS/IMU Data Fusion Aided by V2X Networks

In autonomous driving research area, vehicular positioning has become a critical issue. For vehicular positioning, global positioning system (GPS) and inertial measurement unit (IMU) are commonly used. However, when GPS signal is lost or vehicular networks are interrupted, the positioning accuracy degrades. In order to improve the positioning accuracy, this article proposes a multiple data fusion framework of GPS/IMU aided by vehicular networks, which can optimize vehicle localization systems for accuracy and robustness. Specifically, at the first phase, attention-based long short-term memory (ALSTM) network is utilized to fuse GPS and IMU information. Comparing with traditional GPS and IMU fusion methods, this model avoids requiring any prior information about state transfer model. It aims to enhance the exploitation of past state information by integrating an attention layer above the long short-time memory (LSTM) layer to operate more optimally and comprehensively. At the second phase, the vehicular-to-everything (V2X) network is developed to further improve localization accuracy. A collaborative graph model-based on graph convolutional network (GCN) for vehicular networks is proposed. Each communication node is modeled as an undirected graph node, and can exchange its GPS information with other nodes. At the final phase, a nonlinear regression layer is designed to fuse the information from the prior two phases. Numerical results show that the proposed ALSTM-GCN networks can fuse GPS, IMU, and V2X observation information better, which can achieve high localization accuracy in comparison with the conventional approach.