Complexity Reduction for Cooperative Positioning in Vehicular Networks

This paper addresses the limitations of GPS accuracy in vehicular networks, which is often insufficient for safety-critical applications. Cooperative positioning (CP) methods can mitigate GPS errors but encounter challenges in large networks due to high computational and communication complexity. To overcome these issues, we propose a star model CP method that reduces the observation vector size and an adaptive scheduling method that dynamically selects participating vehicles based on a safety-driven cost function, lowering computational and communication demands. This scheduler optimizes positioning frequency while reducing data exchange. Numerical evaluations show that our framework achieves comparable or improved positioning accuracy compared to methods that do not use scheduling, especially under high GPS error conditions.