A method for identifying a lane line, comprising: identifying (S11, S22, S61, S82) a plurality of image regions of lane lines in an image by using hat-like filter kernels; inputting (S62, S83) the image, in which the plurality of image regions of the lane lines have been identified, into a convolutional neural network-based lane line identification model to obtain an output probability of each identified image region of the lane line in the image being an image region of a real lane line; and identifying the lane line in the input image based on the output probability of each identified image region of the lane line in the image being the image region of the real lane line; wherein the identifying the lane line in the input image based on the output probability comprises: determining the lane line that causes an equation below to have a maximum value: arg max k ∑ i ∈ L k ‖ w i length + w i score + ∑ j ∈ L k & j ≠ i w ij angle + w ij dist ‖, wherein k represents that image regions of lane lines in the input image are divided into k groups, L k represents a number of lane lines in each group, w i length and w i score respectively represent a length of an i th lane line and a probability outputted by the convolutional neural network-based lane line identification model that the i th lane line is a real lane line, and w ij angle, w ij dist respectively represent constraint relationships of angle similarity and distance between the i th lane line and a j th lane line, w i length = min 1, l i × 2 ÷ H, H and l i respectively represent a height and a width of the i th lane line, w i angle = exp ‖ θ i − θ j ‖ 2 / σ angle 2, θ i represents an angle of the i th lane line, θ j represents an angle of the j th lane line, and σ angle represents an angle difference threshold, w i dist = 1, ‖ l i − l j ‖ ≤ l max and ‖ l i − l j ‖ ≥ l min 0, else, l max represents a maximum distance threshold, and l min represents a minimum distance threshold. identifying (S11, S22, S61, S82) a plurality of image regions of lane lines in an image by using hat-like filter kernels; inputting (S62, S83) the image, in which the plurality of image regions of the lane lines have been identified, into a convolutional neural network-based lane line identification model to obtain an output probability of each identified image region of the lane line in the image being an image region of a real lane line; and identifying the lane line in the input image based on the output probability of each identified image region of the lane line in the image being the image region of the real lane line; wherein the identifying the lane line in the input image based on the output probability comprises: determining the lane line that causes an equation below to have a maximum value: arg max k ∑ i ∈ L k ‖ w i length + w i score + ∑ j ∈ L k & j ≠ i w ij angle + w ij dist ‖, wherein k represents that image regions of lane lines in the input image are divided into k groups, L k represents a number of lane lines in each group, w i length and w i score respectively represent a length of an i th lane line and a probability outputted by the convolutional neural network-based lane line identification model that the i th lane line is a real lane line, and w ij angle, w ij dist respectively represent constraint relationships of angle similarity and distance between the i th lane line and a j th lane line, w i length = min 1, l i × 2 ÷ H, H and l i respectively represent a height and a width of the i th lane line, w i angle = exp ‖ θ i − θ j ‖ 2 / σ angle 2, θ i represents an angle of the i th lane line, θ j represents an angle of the j th lane line, and σ angle represents an angle difference threshold, w i dist = 1, ‖ l i − l j ‖ ≤ l max and ‖ l i − l j ‖ ≥ l min 0, else, l max represents a maximum distance threshold, and l min represents a minimum distance threshold.