A processor-implemented method of generating lane information using a neural network, comprising: generating a lane probability map based on an input image using a first neural network trained to generate lane probability values respectively corresponding to pixels in the input image, wherein each of the lane probability values comprise a probability of a pixel of the pixels being in a lane; characterized in that the method further comprises: generating lane feature information and depth feature information by applying the lane probability map to a second neural network, wherein the lane feature information includes information associated with features of the lane distinguished from other objects in the input image, and wherein the depth feature information comprises a height of the lane comprising a length in a vertical direction with respect to a reference line; generating depth distribution information by applying the depth feature information to a third neural network, wherein the depth distribution information comprises depth probability distributions respectively corresponding to the pixels in the input image; generating spatial information through an outer product of the lane feature information and the depth distribution information; generating offset information (191, 192) comprising a displacement between a position of the lane and the reference line by applying the spatial information to a fourth neural network; and generating three-dimensional, 3D, lane information by applying the offset information (191, 192) based on a plurality of reference lines (X i) parallel to a travel direction of a vehicle. generating a lane probability map based on an input image using a first neural network trained to generate lane probability values respectively corresponding to pixels in the input image, wherein each of the lane probability values comprise a probability of a pixel of the pixels being in a lane; characterized in that the method further comprises: generating lane feature information and depth feature information by applying the lane probability map to a second neural network, wherein the lane feature information includes information associated with features of the lane distinguished from other objects in the input image, and wherein the depth feature information comprises a height of the lane comprising a length in a vertical direction with respect to a reference line; generating depth distribution information by applying the depth feature information to a third neural network, wherein the depth distribution information comprises depth probability distributions respectively corresponding to the pixels in the input image; generating spatial information through an outer product of the lane feature information and the depth distribution information; generating offset information (191, 192) comprising a displacement between a position of the lane and the reference line by applying the spatial information to a fourth neural network; and generating three-dimensional, 3D, lane information by applying the offset information (191, 192) based on a plurality of reference lines (X i) parallel to a travel direction of a vehicle. generating lane feature information and depth feature information by applying the lane probability map to a second neural network, wherein the lane feature information includes information associated with features of the lane distinguished from other objects in the input image, and wherein the depth feature information comprises a height of the lane comprising a length in a vertical direction with respect to a reference line; generating depth distribution information by applying the depth feature information to a third neural network, wherein the depth distribution information comprises depth probability distributions respectively corresponding to the pixels in the input image; generating spatial information through an outer product of the lane feature information and the depth distribution information; generating offset information (191, 192) comprising a displacement between a position of the lane and the reference line by applying the spatial information to a fourth neural network; and generating three-dimensional, 3D, lane information by applying the offset information (191, 192) based on a plurality of reference lines (X i) parallel to a travel direction of a vehicle.