Clustering method and system for vehicle line of crowdsourced map, and storage medium

A clustering method for a vehicle line of a crowdsourced map, the method used in a precision map to assist automatic driving, comprising the following steps executed from a processor of a clustering system:

• S1:
• collecting data on vehicle line road elements of the crowdsourced map, and classifying and extracting the vehicle line road elements to obtain to-be-clustered data lists of different vehicle lines including the vehicle line;
• S2:
• computing a mean Z value of each sample in the to-be-clustered data list of the vehicle line to obtain a mean Z value list of the vehicle line, wherein each sample in the to-be-clustered data list of the vehicle line is a type of the vehicle line;
• computing centroid coordinates of each sample in the to-be-clustered data list of the vehicle line to obtain a centroid coordinate list of the vehicle line;
• S3:
• looping through the to-be-clustered data list of the vehicle line to obtain a current sample, and determining E neighborhood samples of the current sample, among each sample in the to-be-clustered data list of the vehicle line, to obtain an E neighborhood sample list of the current sample;
• S4:
• obtaining the mean Z value of the current sample from the mean Z value list of the vehicle line, randomly obtaining an E neighborhood sample from the E neighborhood sample list of the current sample, computing a difference between the mean Z value of the current sample and the mean Z value of the E neighborhood sample, performing distance threshold determination when the difference between the two is less than a preset difference threshold, and putting the E neighborhood samples that conform to the distance threshold determination into a first list of the current sample until the E neighborhood sample list of the current sample is fully looped through;
• S5:
• using the current sample as a nucleus seed when a length of the first list of the current sample is greater than a minimum clustering sample number threshold, and storing, with the current sample, data that conform to the distance threshold determination;
• S6:
• repeating steps S3 to S5 until all nucleus seeds that meet requirements that the length of the first list of each nucleus seed is greater than the minimum clustering sample number threshold, and data associated with the nucleus seeds are found;
• S7:
• looping through a nucleus seed list to obtain a cluster CK where the current sample is located, finding all nucleus seeds located in the current cluster CK, and updating the nucleus seed list until all the nucleus seeds are clustered;
• and
• S8:
• merging all the clusters to obtain final clustering results, wherein the merging is skipped when a cover area of two clusters is less than a preset cover area threshold, or the merging is performed when a cover area of two clusters is greater than or equal to the preset cover area threshold.