Lane-level high-precision positioning algorithm based on integrated spatio-temporal proximity and CNN

doi:10.13474/j.cnki.11-2246.2022.0162.

Abstract

Abstract: Improving the accuracy of lane-level positioning is one of the most important technologies for the development of intelligent transportation system(ITS). We propose a new lane-level positioning method called spatio-temporal proximity and convolution neural network(STP-CNN), in which STP is designed to dynamically refine candidate matching roads, and personalize CNN is further adopted to adaptively identify the optimal matching lane. By optimally integrating parameters such as GPS receiver, driving speed and inertial measurement unit for the new method, centimeter-level and lane-level vehicle position is estimated smoothly. The test results justify the feasibility and efficiency of the proposed method.

Key words: CNN, deep learning, lane-level positioning, intelligent transportation system, spatio-temporal proximity, map matching

CLC Number:

P228

TENG Wenxin, ZHANG Jianchen, SHAO Jie. Lane-level high-precision positioning algorithm based on integrated spatio-temporal proximity and CNN[J]. Bulletin of Surveying and Mapping, 2022, 0(6): 1-5,61.

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