Cooperative Localization Enhancement Through Asynchronous Multi-vehicle Data Fusion Base on Particle Filtering

In order to make up for the deficiency of single positioning technology, it is hoped that the collaborative positioning in the V2X environment can be realized through the cooperation between nodes to improve the positioning accuracy of vehicles in the vehicle-road collaborative application system. In this paper, based on the V2V environment of the Internet of Vehicles, the position, speed, and driving angle are taken as the primary state variables of the system. The GPS positioning data of neighbor vehicles are obtained by vehicle communication equipment, and vehicle sensors measure the motion state information of neighbor vehicles. The asynchronous information cooperative positioning model is constructed. A particle filter is selected to realize asynchronous information fusion estimation in real nonlinear motion scenarios. Finally, the algorithm's performance is tested and analyzed by building a real vehicle experiment scene and establishing relevant analysis indicators. It has been proved that the proposed algorithm can significantly reduce positioning error and improve the stability and accuracy of the positioning system.