1. A method comprising: receiving, using a computing device, parameterized intersection data indicative of an intersection in a roadway of an environment, wherein the parameterized intersection data includes a plurality of parameters that define the intersection in the environment; based on the plurality of parameters, determining a first configuration of the intersection, wherein the first configuration of the intersection includes one or more of predicted curb locations, predicted lane locations, or predicted pedestrian-control line locations; receiving map data based on a detection of the roadway in the environment by sensors on a vehicle that traverses the environment, wherein the map data includes one or more of candidate curb locations of the intersection, candidate lane locations of the intersection, or candidate pedestrian-control lines of the intersection; performing a first optimization of the predicted curb locations to the candidate curb locations to minimize correspondence error between the predicted curb locations and the candidate curb locations for optimal curb locations; performing a second optimization of the predicted lane locations to the candidate lane locations to minimize correspondence error between the predicted lane locations and the candidate lane locations for optimal lane locations; performing a third optimization of the predicted pedestrian-control lines to the candidate pedestrian-control lines to minimize correspondence error between the predicted pedestrian-control line locations and the candidate pedestrian-control line locations for optimal pedestrian-control line locations; based on one or more of the first optimization, the second optimization, and the third optimization, determining a second configuration of the intersection that includes the optimal curb locations, the optimal lane locations, and the optimal pedestrian-control line locations; and based on the second configuration of the intersection, navigating an autonomous vehicle through the intersection.