A Big Data Method to Rebuild 3D Road Map Based on Vehicle Data

doi:10.12068/j.issn.1005-3026.2020.06.003

Abstract

Abstract: A big data based 3D map reconstruction method was presented based on multiple vehicles data. On individual vehicle site， in the proposed algorithm， the least square method and Kalman filter were used， and the refined vehicle instant 3D position was uploaded to the server. The original GPS signal error was reduced by over 50%. Next， genetic algorithm was used instead of Kalman filter， resulting in the error being further reduced by over 16%. On the server site， a 3D road surface point cloud database was generated based on the data from multiple vehicles. K-means method was used as the data mining strategy to search for the lane centers from roads with multiple lanes. The reconstructed map can be used by every online vehicles that support the relevant researches for the optimum driving strategy.

Key words: map reconstruction, big data, Kalman filter, genetic algorithm, K-means

CLC Number:

P284.9

HUANG Chuan， HU Ping， LIAN Jing. A Big Data Method to Rebuild 3D Road Map Based on Vehicle Data[J]. Journal of Northeastern University Natural Science, 2020, 41(6): 771-777.

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