Hidden Markov Map Matching Optimization Algorithm Considering Vehicle Information
The signal transmission is obscured when vehicles are matched in elevated areas,and navigation is prone to mismatching,increased output latency and lane drift,etc..To address such navigation defects,this paper proposes the hidden Markov map matching optimization algorithm with vehicle information.The algorithm eliminates redundant and drifting localization points in the sampled data;generates a grid index when determining the candidate roads,and uses the road topology to delete unconnected roads to reduce the computation and output delay;generates a confidence function using the road and vehicle information,and improves the transfer probability by fusing the speed similarity to determine the matching road sections.The experimental results show that the matching time is shorter when the vehicle drives to the elevated area,and the duration does not increase with the increase of road sections;and it has a high accuracy rate to meet the matching demand of vehicles in the 3D area.
Urban road networkHidden Markov modelMap matchingVehicle information
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