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 (2471,2481) 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 (2471,2481) 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 (2471,2481) associated with the detected lane mark; and distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles (1205,1210,1220,1225); wherein the two or more location identifiers are determined based on: acquisition, from a camera (122,124) 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 (2471,2481) 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 (2471,2481) 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 (2471,2481) 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 (2471,2481) associated with the detected lane mark; and distributing the updated autonomous vehicle road navigation model to a plurality of autonomous vehicles (1205,1210,1220,1225); wherein the two or more location identifiers are determined based on: acquisition, from a camera (122,124) 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 (122,124) 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 (2471,2481) 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.