Lane line group construction method and device

Abstract

The embodiment of the invention provides a lane line group construction method and device. The lane line group construction method comprises the steps: acquiring original lane lines; grouping the original lane lines to obtain a plurality of first lane line groups; for each first lane line group, obtaining a reference line corresponding to the first lane line group, respectively obtaining the distance between each first lane line in the first lane line group and the reference line, and clustering each distance by using a DBSCAN clustering algorithm to obtain a classification corresponding to each first lane line; fitting all the first lane lines belonging to the same class in the plurality of first lane line groups to obtain a plurality of target lane lines; and combining the plurality of target lane lines to complete the construction of the lane line group. According to the scheme, automatic construction of the lane line set is achieved in a big data mode, and the workload is small, and the cost is low, and the accuracy is high.

Description

technical field [0001] Embodiments of the present invention relate to the technical field of crowdsourcing high-precision map production, and more specifically, to a method and device for constructing lane line groups. Background technique [0002] As one of the most important elements in high-precision maps, lane lines play an indispensable role in automatic driving positioning and lane-level path planning. When the original lane lines are missing, fragmented, or out of order, it is impossible to provide services for automatic driving. It needs to be grouped and supplemented, and finally each group of complete and orderly lane line groups can be obtained in order to play its role. [0003] At present, to obtain complete and orderly lane lines, the lane line groups are generally grouped manually, and then the lane line groups required for automatic driving are output. However, manual methods often have a large workload, high cost, and poor accuracy. There is an urgent need ...

AI Technical Summary

Problems solved by technology

[0003] At present, to obtain complete and orderly lane lines, the lane line groups are generally grouped manually, and then the lane line groups required for automatic driving are output. However, manual methods often have a large workload, high cost, and poor accuracy. There is an urgent need to provide an automatic lane group construction method

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