A vehicular lane line data processing method, comprising: acquiring at least two consecutive original images of a vehicular lane line and positioning data of the original images; calculating, using a deep neural network model, for each of the original images, a pixel confidence for each pixel for a similarity between a pixel characteristic in the each original image and an image sample characteristic of a vehicular lane line; determining an outline of the vehicular lane line from the each original image and using each outline of the vehicular lane line as a candidate vehicular lane line; calculating a vehicular lane line confidence of each candidate vehicular lane line based on the pixel confidences of pixels in the each candidate vehicular lane line; filtering the candidate vehicular lane lines based on the vehicular lane line confidences of the candidate vehicular lane lines; recognizing, for the filtered candidate vehicular lane lines, attribute information of the vehicular lane lines; and determining map data, for being used in a two-dimensional map and/or a three-dimensional map, of the vehicular lane lines based on the attribute information of the vehicular lane lines and the positioning data during shooting of the original images, wherein the recognizing, for the filtered candidate vehicular lane lines, attribute information of the vehicular lane lines comprises: recognizing, for each of the filtered vehicular lane lines, a line type and color of the vehicular lane line; calculating a pixel size of the vehicular lane line in each original image; and calculating a physical width, physical length, center line position, and edge line position of the vehicular lane line based on the pixel size and a shooting parameter for shooting each original image. acquiring at least two consecutive original images of a vehicular lane line and positioning data of the original images; calculating, using a deep neural network model, for each of the original images, a pixel confidence for each pixel for a similarity between a pixel characteristic in the each original image and an image sample characteristic of a vehicular lane line; determining an outline of the vehicular lane line from the each original image and using each outline of the vehicular lane line as a candidate vehicular lane line; calculating a vehicular lane line confidence of each candidate vehicular lane line based on the pixel confidences of pixels in the each candidate vehicular lane line; filtering the candidate vehicular lane lines based on the vehicular lane line confidences of the candidate vehicular lane lines; recognizing, for the filtered candidate vehicular lane lines, attribute information of the vehicular lane lines; and determining map data, for being used in a two-dimensional map and/or a three-dimensional map, of the vehicular lane lines based on the attribute information of the vehicular lane lines and the positioning data during shooting of the original images, wherein the recognizing, for the filtered candidate vehicular lane lines, attribute information of the vehicular lane lines comprises: recognizing, for each of the filtered vehicular lane lines, a line type and color of the vehicular lane line; calculating a pixel size of the vehicular lane line in each original image; and calculating a physical width, physical length, center line position, and edge line position of the vehicular lane line based on the pixel size and a shooting parameter for shooting each original image. recognizing, for each of the filtered vehicular lane lines, a line type and color of the vehicular lane line; calculating a pixel size of the vehicular lane line in each original image; and calculating a physical width, physical length, center line position, and edge line position of the vehicular lane line based on the pixel size and a shooting parameter for shooting each original image. <EOS>