Hybrid Cooperative Relative Localization for Urban Vehicles Based on Vehicle-to-Vehicle Communication

Abstract

Accurate vehicle localization is crucial for urban vehicles. We propose a hybrid Gaussian variational message passing (HGVMP) scheme for cooperative relative localization. First, we propose a Gaussian variational message passing (GVMP) framework for state estimation of global navigation satellite system (GNSS) and vehicle-to-vehicle (V2V) observations from multiple vehicles, which puts the messages in GVMP in closed Gaussian form to ensure the stability and efficiency of estimation. In addition, we integrate GVMP with inertial navigation system (INS) via the extended kalman filter (EKF), which makes full use of the inertial information of INS to improve the system’s localization accuracy and stability in dynamic and complex environments. Our experimental results show that in simulated GNSS signal blocked urban environment, the proposed HGVMP achieves a 32.19% improvement in localization accuracy compared to the cooperative localization extended kalman filter (CL-EKF), and the computational efficiency improves by 93.78% over the nonparametric belief propagation (NBP) method.