Abstract
Next-generation Intelligent Transportation Systems (ITS) require collaboration between vehicular communication systems and transport networks to provide highly safety-critical services. For accurate land-vehicle positioning, they rely on high-end Global Navigation Satellite System (GNSS) receivers which are cost-wise inefficient while discontinuities are prevalent in multi-story urban centers. In this paper, a Cooperative Positioning (CP) solution is presented to improve the accuracy of low-cost GNSS receivers, mainly in obstructed propagation areas. Specifically, a multi-attribute decision-making (MADM) methodology is proposed for the dynamic neighboring vehicles’ ranking. Afterward, the target’s vehicle receiver can select the optimal neighboring vehicle to cooperate with and retrieve GNSS corrections from, improving its own Position-Velocity-Time (PVT) state. Experimental criteria data are employed to emulate a scenario of neighboring cars equipped with low-cost GNSS receivers to evaluate the feasibility and ranking performance of the proposed MADM algorithm. The positioning data time series and the numerical results are then presented, exhibiting interesting findings, and a good ranking and selection performance.
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