V2X based Probabilistic Cooperative Position Estimation Applying GNSS Double Differences

Abstract

Future applications for connected and automated driving depend on high-precision, lane selective positioning especially in dense urban environments. Estimating a user's position is often based on Global Navigation Satellite Systems (GNSS), but stand-alone GNSS positioning methods do not meet the necessary performance requirements. To achieve higher accuracies, additional sensor information is usually incorporated. Recent trends to enhance GNSS based positioning have focused on Cooperative Positioning (CP)approaches which allow the elimination of correlated GNSS error terms. The work presented in this paper provides a Dedicated Short Range Communication (DSRC)enhanced CP scheme using IEEE 802.11p and low-cost, multi-constellation GNSS receivers. A proposal for integrating GNSS raw data exchange through DSRC is given. An Extended Kalman Filter (EKF)performing GNSS Double Differencing (DD)is used as positioning algorithm and is compared to a conventional Least Squares Estimator (LSE). The proposed method is described in detail and validated in an experimental, dynamic measurement scenario.