The Impact of Road Configuration in V2V-Based Cooperative Localization: Mathematical Analysis and Real-World Evaluation

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Abstract
Cooperative map matching (CMM) uses the global navigation satellite system (GNSS) position information of a group of vehicles to improve the standalone localization accuracy. While increasing accuracy is expected by increasing the number of participating vehicles, fundamental questions on how the vehicle membership within CMM affects the performance of the CMM results need to be addressed to provide guidelines for design and optimization of the vehicle network. This paper presents a theoretical study that establishes a framework for quantitative evaluation of the impact of the road constraints on the CMM accuracy. More specifically, a closed-form expression of the CMM error in terms of the road constraints and GNSS error is derived based on a simple CMM rule. The asymptotic decay of the CMM error as the number of vehicles increases is established and justified through numerical simulations. Moreover, it is proved that the CMM error can be minimized if the directions of the roads on which the connected vehicles travel obey a uniform distribution. Finally, the localization accuracy of CMM is evaluated based on the real traffic data. The contributions of this paper include establishing a theoretical foundation for CMM as well as providing insight and motivation for applications of CMM.
Funding Information
  • University of Michigan (N021548)

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