Dynamic Optimal Vehicle Selection for Cooperative Positioning Using Low-Cost GNSS Receivers

The recent developments in the Global Navigation Satellite Systems (GNSS) and the advent of Intelligent Transportation Systems (ITS) have accelerated the need for accurate, reliable, and robust land vehicle positioning. Current low-cost, single-frequency GNSS receivers are universally available and have already been employed in a variety of urban mobility applications. However, low-cost GNSS receivers provide low (5-15 m) accuracy that deteriorates rapidly in deep urban and harsh environments imposing a significant impact on the effectiveness and the reliability of critical ITS services. In this paper, a novel vehicle ranking and selection methodology for cooperative positioning (CP) is developed aiming at capitalizing on low-cost GNSS receivers' potential and maximizing their benefits in safety-critical vehicular applications. The proposed method is based on the Multi-attribute Decision-Making (MADM) theory and provides a prioritization of the neighboring vehicles in the vicinity of a target vehicle using criteria related to position accuracy and reliability. By selecting the optimal neighbor vehicle for CP, the low-cost receiver of the target vehicle enhances its location-awareness, and hence, its absolute/relative positioning accuracy. The proposed optimal vehicle selection process was inspired by the Cooperative-Differential GNSS (C-DGNSS) technique. An additional contribution of the proposed methodology is the expansion of the "moving base station" concept for use in ITS. Various MADM algorithms are considered and simulated employing real experimental data from multiple, low-cost GNSS receivers. The optimal MADM algorithm proposed is TOPSIS because the derived rankings offer maximum stability and similarity with Average Correlation Index (ACI) = 0.78, thus satisfying the requirements for critical applications.