A method for mapping a lane mark for use in autonomous vehicle navigation, the method comprising: receiving , by a server , two or more location identifiers associated with a detected lane mark from a host vehicle ; associating , by the server , the detected lane mark with a corresponding road segment; updating , by the server , an autonomous vehicle road navigation model relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane mark , wherein the autonomous vehicle road navigation model includes a target trajectory for navigating along the road segment and two or more location identifiers associated with the detected lane mark ; and distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles ; wherein the two or more location identifiers are determined based on: acquisition, from a camera of the host vehicle , of at least one image representative of an environment of the host vehicle ; analysis of the at least one image to detect the lane mark in the environment of the host vehicle ; and analysis of the at least one image to determine a position of the detected lane mark relative to a location of the host vehicle ; wherein the two or more location identifiers include locations in real world coordinates of points associated with the detected lane mark ; and wherein the detected lane mark is part of a dashed line marking a lane boundary, and the points associated with the detected lane mark correspond to a series of detected corner points representing corners of the dashed line to define a full boundary of the detected lane mark . receiving , by a server , two or more location identifiers associated with a detected lane mark from a host vehicle ; associating , by the server , the detected lane mark with a corresponding road segment; updating , by the server , an autonomous vehicle road navigation model relative to the corresponding road segment based on the two or more location identifiers associated with the detected lane mark , wherein the autonomous vehicle road navigation model includes a target trajectory for navigating along the road segment and two or more location identifiers associated with the detected lane mark ; and distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles ; wherein the two or more location identifiers are determined based on: acquisition, from a camera of the host vehicle , of at least one image representative of an environment of the host vehicle ; analysis of the at least one image to detect the lane mark in the environment of the host vehicle ; and analysis of the at least one image to determine a position of the detected lane mark relative to a location of the host vehicle ; acquisition, from a camera of the host vehicle , of at least one image representative of an environment of the host vehicle ; analysis of the at least one image to detect the lane mark in the environment of the host vehicle ; and analysis of the at least one image to determine a position of the detected lane mark relative to a location of the host vehicle ; wherein the two or more location identifiers include locations in real world coordinates of points associated with the detected lane mark ; and wherein the detected lane mark is part of a dashed line marking a lane boundary, and the points associated with the detected lane mark correspond to a series of detected corner points representing corners of the dashed line to define a full boundary of the detected lane mark . <EOS>